Minimize Landsat-8

Landsat-8 / LDCM (Landsat Data Continuity Mission)

Spacecraft     Launch    Mission Status     Sensor Complement    Ground Segment    References

The Landsat spacecraft series of NASA represents the longest continuous Earth imaging program in history, starting with the launch of Landsat-1 in 1972 through Landsat-7 with the ETM+ imager (launch April 15, 1999). With the evolution of the program has come an increased emphasis on the scientific utility of the data accompanied by more stringent requirements for instrument and data characterization, calibration and validation. This trend continues with LDCM, the next mission in the Landsat sequence. The enhancements of the Landsat-7 system, e.g., more on-board calibration hardware and an image assessment system and personnel, have been retained and improved, where required, for LDCM. Aspects of the calibration requirements are spread throughout the mission, including the instrument and its characterization, the spacecraft, operations and the ground system. 1) 2)

The following are the major mission objectives: 3)

• Collect and archive moderate-resolution, reflective multispectral image data affording seasonal coverage of the global land mass for a period of no less than five years.

• Collect and archive moderate-resolution, thermal multispectral image data affording seasonal coverage of the global land mass for a period of no less than three years.

• Ensure that LDCM data are sufficiently consistent with data from the earlier Landsat missions, in terms of acquisition geometry, calibration, coverage characteristics, spectral and spatial characteristics, output product quality, and data availability to permit studies of land cover and land use change over multi-decadal periods.

• Distribute standard LDCM data products to users on a nondiscriminatory basis and at no cost to the users.

Background: In 2002, the Landsat program had its 30th anniversary of providing satellite remote sensing information to the world; indeed a record history of service with the longest continuous spaceborne optical medium-resolution imaging dataset available anywhere. The imagery has been and is being used for a multitude of land surface monitoring tasks covering a broad spectrum of resource management and global change issues and applications.

In 1992 the US Congress noted that Landsat commercialization had not worked and brought Landsat back into the government resulting in the launches of Landsat 6 (which failed on launch) and Landsat 7. However there was still much conflict within the government over how to continue the program.

In view of the outstanding value of the data to the user community as a whole, NASA and USGS (United States Geological Survey) were working together (planning, rule definition, forum of ideas and discussion among all parties involved, coordination) on the next generation of the Landsat series satellites, referred to as LDCM (Landsat Data Continuity Mission). The overall timeline foresaw a formulation phase until early 2003, followed by an implementation phase until 2006. The goal was to acquire the first LDCM imagery in 2007 - to ensure the continuity of the Landsat dataset [185 km swath width, 15 m resolution (Pan) and a new set of spectral bands]. 4) 5) 6) 7) 8) 9) 10) 11)

The LDCM project suffered some setbacks on its way to realization resulting in considerable delays:

• An initial major programmatic objective of LDCM was to explore the use of imagery purchases from a commercial satellite system in the next phase of the Landsat program. In March 2002, NASA awarded two study contracts to: a) Resource21 LLC. of Englewood, CO, and b) DigitalGlobe Inc. of Longmont, CO. The aim was to formulate a proper requirements set and an implementation scenario (options) for LDCM. NASA envisioned a PPP (Public Private Partnership) program in which the satellite system was going to be owned and operated commercially. A contract was to be awarded in the spring of 2003. - However, it turned out that DigitalGlobe lost interest and dropped out of the race. And the bid of Resource21 turned out to be too high for NASA to be considered.

• In 2004, NASA was directed by the OSTP (Office of Science and Technology Policy) to fly a Landsat instrument on the new NPOESS satellite series of NOAA.

• In Dec. 2005, a memorandum with the tittle "Landsat Data Continuity Strategy Adjustment" was released by the OSTP which directed NASA to acquire a free-flyer spacecraft for LDCM - thus, superseding the previous direction to fly a Landsat sensor on NPOESS. 12)

However, the matter was not resolved until 2007 when it was determined that NASA would procure the next mission, the LDCM, and that the USGS would operate it as well as determine all future Earth observation missions. This decision means that Earth observation has found a home in an operating agency whose mission is directly concerned with the mapping and analysis of the Earth's surface allowing NASA to focus on advancing space technologies and the future of man in space.

Overall science objectives of the LDCM imager observations are:

• To permit change detection analysis and to ensure consistency of the LDCM data with the Landsat series data

• To provide global coverage of the Earth's land surfaces on a seasonal basis

• To acquire imagery at spatial, spectral and temporal resolutions sufficient to characterize and understand the causes and consequences of change

• To make the data available to the user community.

The procurement approach for the LDCM project represents a departure from a conventional NASA mission. NASA traditionally specifies the design of the spacecraft, instruments, and ground systems acquiring data for its Earth science missions. For LDCM, NASA and USGS (the science and technology agency of the Department of the Interior, DOI) have instead specified the content, quantity, and characteristics of data to be delivered.

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Figure 1: History of the Landsat program (image credit: NASA) 13)

Legend to Figure 1: The small white arrow within the Landsat-7 arrow on this timeline indicates the collection of data without the Scan Line Corrector.

"The Landsat series of satellites is a cornerstone of our Earth observing capability. The world relies on Landsat data to detect and measure land cover/land use change, the health of ecosystems, and water availability," NASA Administrator Charles Bolden told the Subcommittee on Space Committee on Science, Space and Technology U.S House of Representatives in April 2015.

"With a launch in 2023, Landsat-9 would propel the program past 50 years of collecting global land cover data," said Jeffrey Masek, Landsat-9 Project Scientist at Goddard. "That's the hallmark of Landsat: the longer the satellites view the Earth, the more phenomena you can observe and understand. We see changing areas of irrigated agriculture worldwide, systemic conversion of forest to pasture – activities where either human pressures or natural environmental pressures are causing the shifts in land use over decades."

Landsat-8 successfully launched on Feb. 11, 2013 and the Landsat data archive continues to expand. — Landsat-9 was announced on April 16, 2015. The launch is planned for 2023. 14)

Dec. 31, 2015: NASA has awarded a sole source letter contract to BACT (Ball Aerospace & Technologies Corporation), Boulder, Colo., to build the OLI-2 (Operational Land Imager-2) instrument for the Landsat-9 project. 15)

 


 

Spacecraft:

In April 2008, NASA selected GDAIS (General Dynamics Advanced Information Systems), Inc., Gilbert, AZ, to build the LDCM spacecraft on a fixed price contract. An option provides for the inclusion of a second payload instrument. LDCM is a NASA/USGS partnership mission with the following responsibilities: 16) 17) 18) 19)

• NASA is providing the LDCM spacecraft, the instruments, the launch vehicle, and the mission operations element of the ground system. NASA will also manage the space segment early on-orbit evaluation phase -from launch to acceptance.

• USGS is providing the mission operations center and ground processing systems (including archive and data networks), as well as the flight operations team. USGS will also co-chair and fund the Landsat science team.

In April 2010, OSC (Orbital Sciences Corporation) of Dulles VA acquired GDAIS. Hence, OSC will continue to manufacture and integrate the LDCM program as outlined by GDAIS. Already in Dec. 2009, GDAIS successfully completed the CDR (Critical Design Review) of LDCM for NASA/GSFC. 20) 21)

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Figure 2: Artist's rendition of the LDCM spacecraft in orbit (image credit: NASA, OSC)

The LDCM spacecraft uses a nadir-pointing three-axis stabilized platform (zero momentum biased), a modular architecture referred to as SA-200HP. The SA-200HP (High Performance) bus is of DS1 (Deep Space 1) and Coriolis mission heritage. The spacecraft consists of an aluminum frame and panel prime structure.

The spacecraft is 3-axis stabilized (zero momentum biased). The ADCS (Attitude Determination and Control Subsystem) employs six reaction wheels, three torque rods and thrusters as actuators. Attitude is sensed with three precision star trackers (2 of 3 star trackers are active), a redundant SIRU (Scalable Inertial Reference Unit), twelve coarse sun sensors, redundant GPS receivers (Viceroy), and two TAMs (Three Axis Magnetometers).

- Attitude control error (3σ): ≤ 30 µrad

- Attitude knowledge error (3σ): ≤ 46 µrad

- Attitude knowledge stability (3σ): ≤ 0.12 µrad in 2.5 seconds; ≤ 1.45 µrad in 30 seconds

- Slew time: 180º any axis: ≤ 14 minutes, including settling; 15º roll: ≤ 4.5 minutes, including settling.

Key aspects of the satellite performance related to imager calibration and validation are pointing, stability and maneuverability. Pointing and stability affect geometric performance; maneuverability allows data acquisitions for calibration using the sun, moon and stars. For LDCM, an off nadir acquisition capability is included (up to 1 path off nadir) for imaging high priority targets (event monitoring capability).
Also, the spacecraft pointing capability will allow the calibration of the OLI using the sun (roughly weekly), the moon (monthly), stars (during commissioning) and the Earth (at 90° from normal orientation, a.k.a., side slither) quarterly. The solar calibration will be used for OLI absolute and relative calibration, the moon for trending the stability of the OLI response, the stars will be used for Line of Sight determination and the side slither will be an alternate OLI and relative gain determination methodology. 22) 23)

C&DH (Command & Data Handling) subsystem: The C&DH subsystem uses a standard cPCI backplane RAD750 CPU. The MIL-STD-1553B data bus is used for onboard ADCS, C&DH functions and instrument communications. The SSR (Solid State Recorder) provides a storage capacity of 4 Tbit @ BOL and 3.1 Tbit @ EOL.

The C&DH subsystem provides the mission data interfaces between instruments, the SSR, and the X-band transmitter. The C&DH subsystem consists of an IEM (Integrated Electronics Module), a PIE (Payload Interface Electronics), the SSR, and two OCXO (Oven Controlled Crystal Oscillators).

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Figure 3: Photo of the EM SSR (Solid State Recorder), image credit: NASA

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Figure 4: Block diagram of the C&DH subsystem (image credit: NASA, USGS, Ref. 157)

- The IEM subsystem provides the command and data handling function for the observatory, including mission data management between the PIE and SSR using FSW on the Rad750 processor. The IEM is block redundant with cross strapped interfaces for command and telemetry management, attitude control, SOH (State of Health) data and ancillary data processing, and for controlling image collection and file downlinks to the ground.

- The SSR subsystem provides for mission data and spacecraft SOH storage during all mission operations. The OCXO provides a stable, accurate time base for ADCS fine pointing.

- The C&DH accepts encrypted ground commands for immediate execution or for storage in the FSW file system using the relative time and absolute time command sequences (RTS, ATS respectfully). The commanding interface is connected to the uplink of each S-band transceiver, providing for cross-strapped redundancy to the C&DH. All commands are verified onboard prior to execution. Real-time commands are executed upon reception, while stored commands are placed in the FSW file system and executed under control of the FSW. Command counters and execution history are maintained by the C&DH FSW and reported in SOH telemetry.

- The IEM provides the command and housekeeping telemetry interfaces between the payload instruments and the ADCS components using a MIL-STD-1553B serial data bus and discrete control and monitoring interfaces. The C&DH provides the command and housekeeping interfaces between the CCU (Charge Control Unit), LCU (Load Control Unit) , and the PIE boxes.

- The PIE is the one of the key electrical system interfaces and mission data processing systems between the instruments, the spacecraft C&DH, SSR, and RF communications to the ground. The PIE contains the PIB (Payload Interface Boards ) for OLI (PIB-O) and TIRS (PIB-T).

Each PIB contains an assortment of specialized FPGAs (Field Programmable Gate Arrays) and ASICs, and each accepts instrument image data across the HSSDB for C&DH processing. A RS-485 communication bus collects SOH and ACS ancillary data for interleaving with the image data.

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Figure 5: Block diagram of PIB (image credit: USGS, NASA)

- Data compression: Only the OLI data, sent through the PIB-O interface, implements lossless compression, by utilizing a pre-processor and entropy encoder in the USES ASIC. The compression can be enabled or bypassed on an image-by-image basis. When compression is enabled the first image line of each 1 GB file is uncompressed to provide a reference line to start that file. A reference line is generated every 1,024 lines (about every 4 seconds) to support real-time ground contacts to begin receiving data in the middle of a file and decompressing the image with the reception of a reference line.

- XIB (X-band Interface Board): The XIB is the C&DH interface between the PIE, SSR, and X-band transmitter, with the functional data path shown in Figure 6.

The XIB receives real-time data from the PIE PIB-O and PIB-T and receives stored data from the SSR via the 2 playback ports. The XIB sends mission data to the X-band transmitter via a parallel LVDS interface. The XIB receives a clock from the X-band transmitter to determine the data transfer rates between the XIB and the transmitter to maintain a 384 Mbit/s downlink. The XIB receives OLI realtime data from the PIB-O board, and TIRS real-time data from the PIB-T board across the backplane. The SSR data from the PIB-O and PIB-T interfaces are multiplexed and sent to the X-Band transmitter through parallel LVDS byte-wide interfaces.

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Figure 6: X-band mission data flow (image credit: USGS, NASA)

- SSR (Solid Ste Recorder): The SSR is designed with radiation hard ASIC controllers, and up-screened commercial grade 4GB SDRAM (Synchronous Dynamic Random Access Memory) memory devices. Protection against on-orbit radiation induced errors is provided by a Reed-Solomon EDAC (Error Detection and Correction) algorithm. The SSR provides the primary means for storing all image, ancillary, and state of health data using a file management architecture. Manufactured in a single mechanical chassis, containing a total of 14 memory boards, the system provides fully redundant sides and interfaces to the spacecraft C&DH.

The spacecraft FSW (Flight Software) plays an integral role in the management of the file directory system for recording and file playback. FSW creates file attributes for identifier, size, priority, protection based upon instructions from the ground defining the length of imaging in the interval request, and its associated priority. FSW also maintains the file directory, and creates the ordered lists for autonomous playback based upon image priority. FSW automatically updates and maintains the spacecraft directory while recording or performing playback, and it periodically updates the SSR FSW directory when no recording is occurring to synchronize the two directories (Ref. 157).

TCS (Thermal Control Subsystem): The TCS uses standard Kapton etched-foil strip heaters. In general, a passive, cold-biased system is used for the spacecraft. Multi-layer insulation on spacecraft and payload as required. A deep space view is provided for the instrument radiators.

EPS (Electric Power Subsystem): The EPS consists of a single deployable solar array with single-axis articulation capability and with a stepping gimbal. Triple-junction solar cells are being used providing a power of 4300 W @ EOL. The NiH2 battery has a capacity of 125 Ah. Use of unregulated 22-36 V power bus.

The onboard propulsion subsystem provides a total velocity change of ΔV = 334 m/s using eight 22 N thrusters for insertion error correction, altitude adjustments, attitude recovery, EOL disposal, and other operational maintenance as necessary.

The spacecraft has a launch mass of 2780 kg (1512 kg dry mass). The mission design life is 5 years; the onboard consumable supply (386 kg of hydrazine) will last for 10 years of operations.

Spacecraft platform

SA-200HP (High Performance) bus

Spacecraft mass

Launch mass of 2780 kg; dry mass of 1512 kg

Spacecraft design life

5 years; the onboard consumable supply (386 kg of hydrazine) will last for 10 years of operations

EPS (Electric Power Subsystem)

- Power: 4.3 kW @ EOL (End of Life)
- Single deployable solar array with single-axis articulation capability
- Triple-junction solar cells
- NiH2 battery with 125 Ah capacity
- Unregulated 22 V - 36 V power bus
- Two power distribution boxes

ADCS (Attitude Determination &
Control Subsystem)

- Actuation: 6 reaction wheels and 3 torque rods
- Attitude is sensed with 3 precision star trackers, a redundant SIRU (Scalable Inertial Reference Unit),
12 coarse sun sensors, redundant GPS receivers (Viceroy), and 2 TAMs (Three Axis Magnetometers)
- Attitude control error (3σ): ≤ 30 µrad
- Attitude knowledge error (3σ): ≤ 29 µrad
- Attitude knowledge stability (3σ): ≤ 0.12 µrad in 2.5 seconds
- Attitude jitter: ≤ 0.28 µrad, 0.1-1.0 Hz
- Slew time, 180º pitch: ≤ 14 minutes, inclusive settling
- Slew time, 15º roll: ≤ 4.5 minutes, inclusive settling

C&DH (Command & Data Handling)

- Standard cPCI backplane RAD750 CPU
- MIL-STD-1553B data bus
- Solid state recorder provides a storage capacity of 4 TB @ BOL and 3.1 TB @ EOL

Propulsion subsystem

- Total velocity change of ΔV = 334 m/s using eight 22 N thrusters
- Hydrazine blow-down propulsion module

Table 1: Overview of spacecraft parameters

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Figure 7: Two views of the LDCM spacecraft (without solar arrays) and major components (image credit: NASA, USGS)

RF communications: Earth coverage antennas are being used for all data links. The X-band downlink uses lossless compression and spectral filtering. The payload data rate is 440 Mbit/s. The X-band RF system consists of the X-band transmitter, TWTA (Travelling Wave Tube Amplifier), DSN (Deep Space Network) filter, and an ECA (Earth Coverage Antenna). The serial data output is set at 440.825 Mbit/s and is up-converted to 8200.5 MHz. The TWTA amplifies the signal such that the output of the DSN filter is 62 W. The DSN filter maintains the signal's spectral compliance. An ECA provides nadir full simultaneous coverage, utilizing 120º half-power beamwidth, for all in view ground sites below the spacecraft's current position with no gimbal or actuation system. The system is designed to handle up to 35 separate ground contacts per day as forecasted by the DRC-16 (Design Reference Case-16).

The X-band transmitter is a single customized unit, including the LDPC FEC algorithms, the modulator, and up converter circuits. The transmitter uses a local TXCO (Thermally Controlled Crystal Oscillator) as a clock source for tight spectral quality and minimum data jitter. This clock is provided to the PIE XIB to clock mission data up to a 384Mbit/s data rate to the transmitter. The X-band transmitter includes an on-board synthesized clock operating at 441.625 Mbit/s coded data rate using the local 48 MHz clock as a reference. Using the on-board FIFO buffer, this architecture provides a continuous data flow through the transmitter (Ref. 157).

The S-band is used for all TT&C functions. The S-band uplink is encrypted providing data rates of 1, 32, and 64 kbit/s. The S-band downlink offers data rates of 2, 16, 32, RTSOH; 1 Mbit/s SSOH/RTSOH GN; 1 kbit/s RTSOH SN. Redundant pairs of S-band omni's provide transmit/receive coverage in any orientation. The S-band is provided through a typical S-band transceiver, with TDRSS (Tracking and Data Relay Satellite System) capability for use during launch and early orbit and in case of spacecraft emergencies.

Onboard data transmission from an earth-coverage antenna:

• Real-time data received from PIE (Payload Interface Electronics) equipment

• Play-back data from SSR (Solid State Recorder)

• To three LGN (LDCM Ground Network) stations

- NOAA Interagency Agreement (IA) to use Gilmore Creek Station (GLC) near Fairbanks, AK

- Landsat Ground Station (LGS) at USGS/EROS near Sioux Falls, SD

- NASA contract with KSAT for Svalbard; options for operational use by USGS (provides ≥ 200 minutes of contact time)

• To International Cooperator ground stations (partnerships of existing stations currently supporting Landsat).

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Figure 8: Photo of the EM X-band transponder (left) and AMT S-band transponder (right), image credit: NASA

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Figure 9: Alternate view of the deployed LDCM spacecraft showing the calibration ports of the instruments TIRS and OLI (image credit: NASA/GSFC)

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Figure 10: The LDCM spacecraft with both instruments onboard, OLI and TIRS (image credit: USGS) 24)

 

Launch: The LDCM mission was launched on February 11, 2013 from VAFB, CA. The launch provider was ULA (United Launch Alliance), a joint venture of Lockheed Martin and Boeing; use of the Atlas-V-401 the launch vehicle with a Centaur upper stage. 25) 26)

Note: Initially, the LDCM launch was set for July 2011. However, since this launch date was considered as too optimistic, NASA changed the launch date to the end of 2012. This new launch delay buys some time for an extra sensor with TIR (Thermal Infrared) imaging capabilities.

Orbit: Sun-synchronous near-circular orbit, altitude = 705 km, inclination = 98.2º, period = 99 minutes, repeat coverage = 16 days (233 orbits), the nominal LTDN (Local Time on Descending Node) equator crossing time is at 10:00 hours. The ground tracks will be maintained along heritage WRS-2 paths. At the end of the commissioning period, LDCM is required to be phased about half a period ahead of Landsat 7. 27)

 

Minimize Mission Status:

Mission status:

• March 18, 2017: Several hundred lakes dot the expansive Tibetan Plateau. With the average plateau elevation exceeding 4,500 meters above sea level, its lakes are among the highest in the world.

- Puma Yumco in Lhozhag County is one of the larger lakes in southern Tibet. A small village along the eastern edge of the lake—Tuiwa—is reportedly one of the highest administered settlements in the world, sitting at an elevation of 5,070 meters. Tuiwa's economy centers on raising livestock (sheep and yaks), tourism, and textiles. Though there are fish in the lake, they are considered sacred and are not eaten by most Tibetans. - Lake Puma Yumco is 32 km long and 14 km wide and covers an area of 880 km2.

- Every winter, villagers herd thousands of sheep across the lake's frozen surface to two small islands, where the soil is more fertile and the forage is better in the winter.

- While the rhythms of life have remained largely unchanged in Tuiwa for many decades, researchers have used satellites to track subtle changes at Puma Yumco and other lakes throughout the plateau. One team has found that the number of lakes on the Tibetan Plateau has increased by 48 percent, and the surface area of the water has increased by 27 percent between the 1990s and 2015. For Puma Yumco, the size of the lake dropped a bit between the 1970s and 1990s, but has risen since then, primarily because of an increase in precipitation.

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Figure 11: Landsat-8 image of the mostly ice-coverd lake Puma Yumco on the Tibetan Plateau, acquired with OLI on March 13, 2017 (image credit: NASA Earth Observatory, image by Jesse Allen, using Landsat data from the USGS, caption by Adam Voiland)

• March 17, 2017: Geographers who have studied the growth of China's cities over the past four decades tend to sum up the pace of change with one word: unprecedented. In 1960, about 110 million Chinese people—or 16% of the population—lived in cities. By 2015, that number had swollen to 760 million and 56%. -For comparison, the entire population of the United States was about 325 million people as of March 2017. 28)

- The surge in urbanization began in the 1980s when the Chinese government began opening the country to foreign trade and investment. As markets developed in "special economic zones," villages morphed into booming cities and cities grew into sprawling megalopolises. Perhaps no city epitomizes the trend better than Shanghai. What had been a relatively compact industrial city of 12 million people in 1982, had swollen to 24 million in 2016, making it one of the largest metropolitan areas in the world.

- For more than four decades, Landsat satellites have collected images of Shanghai. The composite images of Figures 12 and 13 show how cities in the Yangtze River Delta have expanded since 1984. Note how Suzhou and Wuxi have merged with Shanghai to create one continuous megalopolis (Figure 13).

- These "best-pixel mosaics" are made up of small parts of many images captured over five-year periods. The first image is a mosaic of scenes captured between 1984 and 1988; the second shows the best pixels captured between 2013 and 2017. This technique makes it possible to strip away clouds and haze, which are common in Shanghai.

- A 2015 World Bank report noted that 7,734 km2 in the Yangtze River Delta Economic Zone—which includes Shanghai, Suzhou, Wuxi, and several other cities—became urban between 2000 and 2010. That is an area equivalent to 88 Manhattans. During that period, population in that zone increased by 21 million people.

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Figure 12: China's metropolitan region of Shanghai observed with TM on Landsat-5 in 1984 (image credit: NASA Earth Observatory mosaics by Joshua Stevens and Jesse Allen using Landsat data from the USGS, caption by Adam Voiland)

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Figure 13: China's metropolitan region of Shanghai observed with OLI on Landsat-8 in 2017 (image credit: NASA Earth Observatory mosaics by Joshua Stevens and Jesse Allen using Landsat data from the USGS, caption by Adam Voiland)

• March 15, 2017: Along the western border of Virginia, two roughly parallel ridges—one of which is the backbone of Shenandoah National Park and the other part of George Washington National Forest—rise above the rolling lowlands of the Shenandoah Valley. Despite being just a few kilometers apart, the ridges show some marked differences (Figure 14). 29)

- With a perimeter of smooth, straight crests encircling a valley, Massanutten Mountain has the look of a flat-bottomed canoe. Shenandoah National Park's portion of the Blue Ridge, in contrast, has a more textured, irregular, and knobby shape; it looks more like a spine, with a dendritic network of gullies descending from its main crest.

- The two ridges look different due to distinct geological histories. The rock underneath Shenandoah National Park is largely igneous, meaning it was created when magma or lava cooled and solidified. Some of the oldest rocks in the park are granites that formed deep underground about 1.1 billion years ago when continents collided and pushed up a mountain range during the Grenville orogeny. Major outcrops of granite are located east of Shenandoah's highest crest and dominate peaks such as Old Rag and Mary's Rock.

- About 500 million years later, the tectonic tides had shifted. Instead of continents colliding, they were pulling apart. As the crust thinned and rifts formed, volcanoes sprang up and spilled lava across the land surface. This laid down layer upon layer of basalt, a type of igneous rock that cools quickly and thus has small mineral crystals. When exposed later to the high temperatures and pressures associated with the collision of tectonic plates, the basalt metamorphosized into metabasalt. This greenish rock, known in this area as the Catoctin Formation, makes up much of the Blue Ridge's highest crest, including peaks like Hawksbill, Stony Man, Mount Marshall, and Hightop.

- As the rift widened, it eventually connected with the ocean and was filled by a narrow, shallow sea. At its bottom, sedimentary rocks began to form as layers of sand, mud, and material from sea life began to rain down on the sea floor and become sandstone, shale, and carbonate rock.

- The landscape we see today was set up by one more cycle of continents slamming into each other—a collision between North America and Africa about 300 million years ago known as the Alleghanian orogeny. While building mountains that were once as tall as the Himalayas, the Alleghanian orogeny thrust the older igneous rocks (once the root of the Grenville range) upward and squeezed them into a curved bulge called an anticline, putting older rock layers quite close to the surface. The same collision squeezed and folded the nearby rocks of the Shenandoah Valley and Massanutten into a concave depression called a syncline that kept the youngest sedimentary rocks quite close to the surface.

- With the rock layers in place, erosion played the final role in sculpting the modern landscape. Rivers such as the Shenandoah's South Fork wore away relatively soft and weak types of sedimentary rock (the shale and carbonates) to create low-relief areas such as the Shenandoah Valley, Page Valley, and Fort Valley. Erosion-resistant, quartz-rich sandstone remained to give Massanutten Mountain its distinctive shape. To the east, the erosion-resistant igneous-based rocks of the Blue Ridge tower over Shenandoah Valley, Massanutten Mountain, and the rest of the Piedmont.

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Figure 14: OLI on Landsat-8 captured this natural-color image on Oct. 21, 2013 of the neighboring ridges. The Landsat image has been draped over a digital elevation model based on data from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) on NASA's Terra satellite. The tops of both ridges are brown because fall colors had emerged in these cool, high-elevation areas (image credit: NASA Earth Observatory image by Jesse Allen, using Landsat data from the USGS, caption by Adam Voiland)

• March 1, 2017: The Caspian Sea stretches about 1,000 km from Kazakhstan to Iran. In the north, temperatures are colder, and the water is fresher (less saline) and shallower. As a result, northern areas are more prone to freezing in wintertime. 30)

- The image of Figure 15 shows the northwestern Caspian where it meets western Kazakhstan. The brown areas are part of the Volga Delta. Just offshore, in the shallowest parts (only meters deep), a well-developed expanse of consolidated ice appears white. Farther offshore, a large field of old, hummocked, white and gray-white ice has detached. (When pieces of ice are pushed together, some ice is forced upward and downward into so-called ‘hummocks.') This ice is slowly drifting in a giant polynya which is covered by young, thin ice (nilas).

- The image of Figure 16 shows a detailed view of the nilas ice, which appears dark. Perhaps most notable, however, is the white, diamond-shaped piece of ice parked right in the middle. "This ‘island' of white ice is most probably a piece that detached from the ice field," said Alexei Kouraev, a scientist at the Laboratory of Geophysical and Oceanographic Studies (France). He notes that a likely point of origin is the "dent" of similar size in the boundary of the white ice (mid-right in the image of Figure 15).

- It might look like that ice diamond is on the move, cutting a path through the thinner cover. But it's more likely that the chunk of ice broke away from the thicker sea ice and became grounded—anchored to the bottom of the sea. The grounded ice (‘stamukha' in Russian) is not moving, according to Kouraev. Instead, the wind is pushing the thin ice around this grounded ice, creating a ‘shadow' of open water behind it.

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Figure 15: On February 4, 2017, OLI on Landsat-8 acquired natural-color images that beautifully demonstrate the variety of ice types that can form in the northern Caspian Sea (image credit: NASA Earth Observatory, images by Joshua Stevens, using Landsat data from the USGS)

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Figure 16: Detail image showing grounded ice (image credit: NASA Earth Observatory, images by Joshua Stevens, using Landsat data from the USGS)

• February 16, 2017: In the past few years, the title of "largest solar farm in the world" has been a rather short-lived distinction. For a period in 2014, the Topaz Solar Farm in California topped the list with its 550 MW facility. In 2015, another operation in California, Solar Star, edged its capacity up to 579 MW. By 2016, India's Kamuthi Solar Power Project in Tamil Nadu was on top with 648 MW of capacity. — As of February 2017, Longyangxia Dam Solar Park in China was the new leader, with 850 MW of capacity. 31)

- By January 5, 2017, solar panels covered 27 km2 of the Qinghai province in China (Figure 17). According to news reports, there were nearly 4 million solar panels at the site in 2017. The rapid expansion at Longyangxia coincides with China's fast-growing solar power sector. In 2016, China's total installed capacity doubled to 77 GW. That pushed the country well ahead of other leading producers—Germany, Japan, and the United States—to become the world's largest producer of solar power. However, those three countries (and several others) produce more solar power per person.

- It is unlikely that Longyangxia will remain the largest solar park in the world for long. A project planned for the Ningxia region in China's northwest will have a capacity of 2,000 MW when it is finished, Bloomberg reported.

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Figure 17: Landsat-8 image of the Longyangxia Dam Solar Park in China, acquired with OLI (operational Land Imager) on January 5, 2017 (NASA Earth Observatory, images by Jesse Allen, using Landsat data from the USGS, caption by Adam Voiland)

• February 5, 2017: Image comparison of Silicon Valley observed by Landsat-1 in 1972 and by Landsat-8 in 2016.

By the middle of the 20th Century, Silicon Valley was already "on the map." This part of California's Santa Clara Valley drew its nickname from the raw material being used in the region's growing semiconductor industry. The area at the south end of San Francisco Bay became a magnet for scientists and for technology companies, so by the time the new Landsat-1 satellite caught a glimpse in 1972, urban sprawl had already replaced many of the valley's orchards. 32)

- While the two images of Figures 18and 19 don't show much change in the development of the landscape, they clearly show the development of the technology behind Landsat's satellite sensors. The false-color image of Figure 18 was acquired on October 6, 1972, with the MSS (Multispectral Scanner System) on Landsat-1; the natural-color image of Figure19 was acquired on November 18, 2016, by the OLI (Operational Land Imager) on Landsat-8.

- The most obvious improvement is the spatial resolution. Over the past 45 years, you have certainly noticed similar improvements in your electronics and imaging products. Better spatial resolution is the reason you can now see blades of grass in a televised football game and the fine lines on your face in a smartphone photo. In short, there is a lot more detail visible in the 2016 image than in the 1972 image. Both are displayed at a resolution of 45 m per pixel. The MSS image is relatively blurry, however, because the sensor's spatial resolution was just 68 x 83 m. The OLI image appears crisper because the instrument can resolve, or "see," objects down to about 30 m (15 m in some cases).

- The 2016 image also has better radiometric resolution, which means the newer instrument is more sensitive to differences in brightness and color. OLI uses 4,096 data values to describe a pixel on a scale from dark to bright. MSS used just 64. More data ultimately translates to the features in the image appearing smoother.

- Finally, the images are very different colors because the wavelengths (color) of light used to compose the images are from different parts of the spectrum. Both images were composed using red and green wavelengths. The image of Figure 18, however, uses near-infrared. False-color images like this one (MSS bands 6, 5, 4) are still produced with modern instruments because they are useful for distinguishing features such as vegetation, which appears red in the top image.

- In contrast, the OLI image does not show near-infrared (although the instrument does have the capability). Instead, it includes blue, a color that MSS was not designed to sense. This combination (OLI bands 4, 3, 2) produces a natural-color image similar to what your eyes would see.

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Figure 18: False color image of the Silicon Valley, acquired with MSS on Landsat-1 (former ERTS) on October 6, 1972 (image credit: NASA Earth Observatory, image by Jesse Allen, using Landsat data from the U.S. Geological Survey, caption by Kathryn Hansen)

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Figure 19: Natural color image of the Silicon Valley, acquired with OLI on Landsat-8 on Nov. 18, 2016 (image credit: NASA Earth Observatory, image by Jesse Allen, using Landsat data from the U.S. Geological Survey, caption by Kathryn Hansen)

• January 27, 2017: Mariners have long considered the waters off Africa's southern tip to be treacherous. After decades of failed attempts to navigate around the continent, Portuguese explorers took to calling one of its southerly promontories the Cape of Storms (it was later renamed the Cape of Good Hope). Cape Agulhas , Africa's southernmost point, is Portuguese for Cape of Needles. Historians think the name may be a reference to the needle-like rock formations and reefs along its coast. 33)

- The convergence of two ocean currents—one warm and one cold—in the shallow waters of Agulhas Bank produces turbulent and unpredictable waters. Warm water arrives from the east on the fast-moving Agulhas Current, which flows along the east coast of Africa. Meanwhile, the cooler, slower Benguela Current flows north along Africa's southwestern coast. That means navigating around the tip of South Africa requires mariners to sail against ocean currents on both sides of the continent.

- Eventually, they learned to stay well out to sea as they rounded the Cape of Good Hope and Cape Agulhas, but not before failed attempts had littered the area's reefs with wrecked ships. Even in modern times, shipwrecks are relatively common in the turbulent water of Agulhas Bank, where colliding currents regularly spin off rogue waves, eddies, and meanders.

- The instability and churning does have one benefit. As water masses stir the ocean, they draw nutrients up from the deep, fertilizing surface waters to create blooms of microscopic, plant-like organisms (phytoplankton) in the open ocean. The phytoplankton feed a robust chain of marine life that makes Agulhas Bank one of the richest fishing grounds in southern Africa.

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Figure 20: Landsat-8 image of Cape Agulhas, acquired by OLI (Operational Land Imager) on May 25, 2016 (image credit: NASA Earth Observatory, image by Jesse Allen, using Landsat data from USGS)

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Figure 21: Suomi-NPP image of the South Africa Coast, acquired with VIIRS (Visible Infrared Imaging Radiometer Suite) on Jan. 4, 2017. The light blue swirl to the east of Cape Agulhas is a phytoplankton bloom in an area of cool, upwelling water (image credit: NASA Earth Observatory, image caption by Adam Voiland)

• January 4, 2017: Around Lake Thurmond, a large reservoir that straddles Georgia and South Carolina, something is not right with the birds. The lake is full of vegetation — particularly an invasive aquatic plant known as Hydrilla verticillata — and the area is full of birds that are distressed or dying. 34)

- Scientists have deduced that the birds are consuming a toxic cyanobacteria that lives on Hydrilla verticillata. The toxin causes a neurodegenerative disease and ultimately death in the water birds that ingest it. Eagles don't eat the plants, but they do prey on other birds. And many eagles have been found dead in the vicinity of Lake Thurmond.

- Hydrilla is tenacious. It grows in fresh water on every continent except Antarctica, and it has been found in at least 30 states in the U.S. The plant tolerates a wide range of temperature, nutrient, salinity, and turbidity conditions, and it can grow as fast as one inch per day. In Lake Thurmond, the plant can be found in 11,200 of the lake's 71,000 acres. The U.S. Army Corps of Engineers has used herbicides to temporarily control the plant's growth at boat ramps and swimming areas, but the impact on birds has encouraged investigation into treatments that would be more widespread and long-term.

- To help lake managers know where to focus management efforts, researchers at the University of Georgia developed a new way to assess the distribution of Hydrilla across the lake. The project was part of NASA's DEVELOP program, in which recent college graduates and early career professionals use NASA satellite observations to address an environmental or public policy issue. On October 18, 2015, the Operation Land Imager on the Landsat-8 satellite acquired an image (Figure 22) of Lake Thurmond. Blue overlays on the Landsat image show the extent of Hydrilla that month. The map is based on a model developed by the team, derived from Landsat-8 imagery and ground-based measurements.

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Figure 22: The OLI (Operational Land Imager) on Landsat-8 acquired this image of Lake Thurmond on Oct. 18, 2015 (image credit: NASA Earth Observatory, map by Jesse Allen, using Landsat data from the USGS and field observations and model data provided by Abhishek Kumar, University of Georgia; caption by Kathryn Hansen)

- Hydrilla grows best under specific water and light conditions. The model accounts for those parameters, represented in this series of four images (Figure 23). The most important factor is the water's transparency (top left). By lowering a Secchi disk into the water and measuring the depth at which it is no longer visible, scientists can estimate the water's transparency. In this map, the darkest orange areas are transparent and yellow areas are turbid (murky). Highly transparent regions are more suitable for Hydrilla.

Note: a Secchi desk, created by Angelo Secchi in 1965, is a plain white, circular disk 30 cm in diameter used to measure water transparency in bodies of water. The disc is mounted on a pole or line, and lowered slowly down into the water. The depth at which the disk is no longer visible is taken as a measure of the transparency of the water. This measure is known as the Secchi depth and is related to water turbidity.

- From the transparency measurement, the team derived other parameters, including the gradual loss of light at depth, or "light attenuation" (top-right), the percentage of light penetrating the column of water (bottom-left), and the maximum depth at which Hydrilla colonize (bottom-right). The new model integrates all of these parameters to determine the ideal locations for Hydrilla growth. According to the DEVELOP team, the model will "act as the foundation for later models intending to predict future locations in need of Hydrilla management."

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Figure 23: Model representation to estimate the water's transparency, light attenuation, etc. of the Hydrilla influence (image credit: NASA Earth Observatory, map by Jesse Allen, using Landsat data from the USGS and field observations and model data provided by Abhishek Kumar, University of Georgia; caption by Kathryn Hansen)

• December 21, 2016: At first glance, a river's course may seem fixed and unchanging. In truth, the path is a perpetual work-in-progress that, in some cases, can shift dramatically in a short span of time. This is especially true in the Moxos plains of northern Bolivia. In this tropical area east of the Andes, several rivers meander through a swampy landscape of savanna, forests, and ponds. While studying three decades of satellite imagery of this area, geographer Umberto Lombardo of the University Pompeu Fabra in Barcelona, Spain, noticed a particularly striking example of rapid change on a stretch of the Maniqui River. 35)

- OLI (Operational Land Imager) on Landsat-8 captured four false-color images between 2013 and 2016 (Figure 24). These images were assembled using red, green, and shortwave infrared light. The use of infrared light makes it easier to distinguish between silt-laden water and bare land. Water absorbs infrared light, while plants and bare earth reflect it. As a result, areas with standing water appear blue, while bare land appears light brown. Forests are bright green; savanna is pink-brown.

- "Images like this underscore how much and how quickly small- and medium-size rivers in this part of the world change," said Umberto Lombardo. Earlier research on larger rivers in the Amazon had suggested that big shifts in a river's path usually coincide with major flooding associated with La Niña, a cyclical cooling of ocean temperatures in the equatorial Pacific. But after systematically combing through three decades of satellite imagery and the paths of 12 small and medium-sized rivers in the southern Amazon, Umberto found no obvious connection to La Niña. Instead, he found that the course of small rivers tended to be in a regular state of change regardless of La Niña cycles.

- Umberto's findings have on-the-ground implications. Authorities should be prepared for the possibility that indigenous communities living along small rivers may have to be resettled if rivers shift in the coming years. Also, a planned road linking Villa Tunari to San Ignacio de Moxos may be destroyed or regularly flooded without careful planning, he explained.

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Figure 24: The image on the upper left shows the course of the river in September 2013, when it flowed in a northeasterly direction. By August 2014, it had broken through its right bank and began to spill into a swampy depression nearby. By September 2015, the river had broken out of its channel for a second time, this time flowing into a pond a few kilometers south of the first break. By July 2016, sediment deposited by the river had filled in most of the pond and the river was charting a more easterly path. Over those three years, vegetation growth started to cover up the channel that was full of water in 2013 (image credit: NASA Earth Observatory, images by Jesse Allen, using Landsat data from the USGS, caption by Adam Voiland)

• December 2016: The in-orbit performance of Landsat-8 continues to be outstanding, currently acquiring around 740 scenes per day, and several Antarctic and Arctic off-nadir requests have recently been fulfilled with no impact on routine imaging. Operational and data processing solutions have been implemented to mitigate the impact of the anomaly in Landsat-8's TIRS (Thermal Infrared Sensor) SSM (Scene Select Mirror). All affected data have been reprocessed and nominal TIRS data collection and processing have been restored. 36)

- Landsat MSS (Multispectral Scanner) Improvement Plan: There are efforts to improve the Landsat MSS archive, including MSS reflectance-based calibration, adjustment of minimum and maximum radiance values to minimize saturation, updating of gain-trend models, and derivation of a bulk correction factor to minimize attitude bias. Overall, the processing and model updates being implemented will help increase the number—as well as the geometric and radiometric quality—of MSS Level-1T scenes. Currently, the plan is to begin collection-processing for MSS in the summer of 2017.

- Landsat-8 TIRS Reprocessing Status: Ron Morfitt reported of how image measurements from geometric calibration are being used to correct the SSM issue, which caused TIRS images to be shifted out of alignment with OLI by as much as 500 m. Overall, the new TIRS processing model is working well, with registration accuracy of around 20 m when telemetry and calibration data are available.

- Matt Montanaro and Aaron Gerace provided an update on the stray-light correction algorithm being developed for TIRS on Landsat-8. Montanaro explained how stray light entering the optical path from outside the direct field-of-view is causing significant nonuniform banding in the TIRS bands 10 and 11. The approach to correct this issue uses TIRS data to estimate the out-of-view signal, based on in-scene statistics. Initial validation results based on comparison with underpass data from the MODIS (Moderate Resolution Imaging Spectroradiometer) on Terra are encouraging. Although more testing is planned over land and low-temperature regions of Antarctica, the LST (Landsat Science Team) recommended moving toward operational implementation of the developed stray-light correction algorithm. The current plan is to implement the algorithm during Collection 1 reprocessing, which is slated to begin in the fall of 2016.

The NASA–USGS Landsat mission and European Space Agency's Sentinel-2 missions have collaborated to produce a Harmonized Surface Reflectance Product. The data products from these missions represent the most widely accessible medium-to-high spatial resolution multispectral satellite data in the world. Following the launch of the Sentinel-2A satellite on June 23, 2015, the potential for synergistic use of the two sources creates unprecedented opportunities for timely and accurate observation of Earth status and dynamics. Thus, harmonization of the distributed data products is of paramount importance for the scientific community. Activities to harmonize data products are on their way, yet more coordination is needed to allow the majority of users to easily and effectively include both data types into their work.

Table 2: New harmonized Landsat–Sentinel reflectance product available

• December 14, 2016: Earth's ice is changing, from mountain glaciers to ice sheets to ice shelves. For the most part, land-based ice has been shrinking, and the very definition of a "glacial pace" has changed within our lifetimes. Now researchers have the tools to see those changes every few weeks and at scales as small as 5 meters — the technique is known as feature tracking. 37) 38)

- Using freely available data from the Landsat 8 satellite, scientists are working to provide a near-real-time view of every large glacier and ice sheet on Earth. A group of scientists from the National Snow and Ice Data Center (NSIDC), the University of Alaska–Fairbanks, the University of Bristol, and the Jet Propulsion Laboratory (JPL) have started the GoLIVE (Global Land Ice Velocity Extraction) project , a NASA-funded effort to better understand how ice flow is changing worldwide.

- "We are now able to map how the skin of the ice is moving," said Ted Scambos, senior research scientist at NSIDC and lead for the GoLIVE project. "From now on, we're going to be able to track all of the different types of changes in glaciers. There's so much science to extract from the data." The effort was described at the 2016 fall meeting of the American Geophysical Union.

- Evidence strongly suggests that the loss of ice from glaciers and ice sheets has been the largest contributor to sea level rise over the past three decades, with waters rising at a global average rate of 3.3 mm/year. By examining changes in ice flow in combination with data on ocean and atmospheric changes, the researchers hope to determine what causes ice masses to change and how much ice will flow into the ocean. The satellite-based approach is particularly valuable in remote landscapes, where ground- and airplane-based observations are expensive, dangerous, and intermittent.

- In Alaska and Canada's Yukon Territory, for instance, most glaciers are so remote that speedup events can go unnoticed for months until a pilot flies over the region and reports disrupted ice, notes Mark Fahnestock of the University of Alaska. The map of Figure 25, based on an analysis by GoLIVE investigators, shows the velocity of ice in southeastern Alaska near Malaspina and Hubbard glaciers.

- "By measuring ice flow all the time, we can identify a surge as it starts, providing an entirely new way to follow this phenomenon," Fahnestock said. "We can also follow large seasonal swings in tidewater glaciers as they respond to their environment. Scientists need to see all of this variability in order to identify trends."

- Automation has been key to this ice velocity mapping effort. Landsat 8 collects images of roughly 700 sunlit parcels of the planet every day; over the course of 16 days, it observes the entire land surface of Earth in multiple visible and infrared wavelengths. This means scientists can view changes in the same spot on Earth every 16 days (or 32, 48, 64, etc., as cloud cover allows).

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Figure 25: GoLIVE map of Landsat-8 OLI data showing the velocity of ice in the southeastern Alaska near the Malaspina and Hubbard glaciers (image credit: NASA Earth Observatory, images Landsat-derived ice velocity data courtesy of Alex Gardner, NASA/JPL, California Institute of Technology and ASTER GDEM data from the NASA/GSFC/METI/ERSDAC/JAROS, and U.S./Japan ASTER Science Team)

- It also means there is a huge amount of data to process and analyze. From 2013 to 2016, Landsat-8 collected thousands of images from Antarctica alone. The globes of Figure 26 show how many times Landsat-8 passed over a given icy parcel in 2015 alone. As many as 150 to 200 images were collected over the brightest yellow and green areas, while purple areas had just a handful of useful images because of frequent cloud cover and fewer orbital passes. Due to the nature of the satellite's polar orbit, areas in the far north and south can be imaged more frequently (when there is sunlight).

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Figure 26: Observation frequency of the polar regions of Earth, Antarctica (left) and the Arctic region (right) by the Landsat-8 satellite (image credit: NASA Earth Observatory, images Landsat-derived ice velocity data courtesy of Alex Gardner, NASA/JPL, California Institute of Technology and ASTER GDEM data from the NASA/GSFC/METI/ERSDAC/JAROS, and U.S./Japan ASTER Science Team)

- The imaging system on Landsat-8 is far more sensitive than previous Landsat sensors, distinguishing far more subtle differences in shading and surface texture. The GoLIVE team has written software that allows researchers to follow these subtle features, like bumps or dune-like patterns on ice surfaces. By comparing images of the same location on different dates, researchers can track individual features and determine the speed of the surface flow. "The question is: how sensitive are these ice sheets to changes in the atmosphere and the ocean?" said Alex Gardner of JPL. "We could wait and see, or we could look to the past to help inform what is most likely to happen in the future."

- Gardner has been looking closely at Antarctica, with ice velocities represented in the map of Figure 27. He is working to combine the new Landsat 8 ice-flow data with prior maps of the continent's glacier flow in the hopes of understanding decadal changes across the entirety of the ice sheet. Almost 2,000 km3 of ice flows into the Southern Ocean from Antarctica each year.

- Twila Moon, an ice scientist at the University of Bristol, is using the global maps to expand her research in Greenland. With the new database, she can study the movements of more than 240 glaciers, nearly all of the outlets from the ice sheet. And with Landsat-8 making an overpass every 16 days, she has an opportunity to detect seasonal changes and cyclical patterns.

- While most glaciers speed up in the warmer summer months, Moon has found several that slow down dramatically in the mid- to late-summer. "We can group these glaciers by looking at the similarities in their behavior," Moon said. "It's providing an opportunity to get at the underlying drivers of why they change." With measurements of what the seasonal shifts do to glacier speed, scientists can extrapolate what might happen to those glaciers as global temperatures continue to climb.

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Figure 27: GoLIVE map of Landsat-8 OLI data showing the velocity of ice in Antarctica in 2015 (image credit: NASA Earth Observatory, images Landsat-derived ice velocity data courtesy of Alex Gardner, NASA/JPL, California Institute of Technology and ASTER GDEM data from the NASA/GSFC/METI/ERSDAC/JAROS, and U.S./Japan ASTER Science Team)

• November 19, 2016: In Landsat images of the Tafilalt oasis of southeastern Morocco, dozens of thin lines run across the desert from the Anti-Atlas mountains toward the town of El Jorf. These are qanats (khettaras in Moroccan)—ancient underground water channels designed to transport water down slopes without active pumping. 39)

- Most wells involve digging a vertical shaft downward until it reaches the water table, and then hauling or pumping the water up to the surface. Qanats consist of gently inclined horizontal tunnels dug into sloping terrain. When the horizontal tunnel hits the water table, gravity causes water to simply flow downhill in the channel toward outlets at the base of the slope.

- While the channels that convey the water lay below the surface, the access shafts used for construction and maintenance are visible above ground. The access shafts are often dug near or through large earthen mounds made of material excavated during construction of the vertical shafts and channels.

- From above, these earthen mounds form long chains that appear as relatively continuous lines at Landsat's 30 m/pixel spatial resolution.

 

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Figure 28: The OLI (Operational Land Imager) of Landsat-8 acquired this image of several qanats leading toward El Jorf on July 2, 2016 (image credit: NASA Earth Observatory, image by Joshua Stevens, using Landsat data from the USGS)

• October 29, 2016: Kiruna, the largest underground iron ore mine in the world, has been in operation since 1900. But recent years have brought change for the residents of the nearby town. In the coming decade, the 23,000 people and their homes and businesses will move three kilometers away. 40)

- Resource extraction is crucial for the town's existence and economic wellbeing. But the steady development of mine shafts continues to weaken the ground there. In 2004, the mine's operator, the LKAB (Luossavaara-Kiirunavaara Company), announced that development of the mine has threatened the structural integrity of the town's buildings. Some of them, including Kiruna's historic red church, will be taken apart and reassembled at the new location.

- OLI (Operational Land Imager) on the Landsat-8 satellite captured this image (Figure 29) of the Kiruna mine, town, and nearby airport on October 10, 2016. The green vegetation is marbled with yellow, likely the result of birch forests and deciduous shrubs changing color. A dusting of snow whitens some hilltops in the image. The Sun's low angle on the southern horizon casts long shadows on the north sides of the hills.

- The Kiruna orebody is one of the world's largest magnetite-apatite deposits. Sweden, the biggest iron producer in Europe, owes its reserves to volcanic activity many thousands of years ago, according to a paper in Nature. 41)

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Figure 29: Image of the Kiruna Iron Ore Mine and the town of Kiruna, acquired on Oct. 10, 2016 with OLI on Landsat-8 (image credit: NASA Earth observatory, image by Joshua Stevens using Landsat data from the USGS)

• October 6, 2016: A special issue of the journal Remote Sensing of Environment details the improved capabilities and mission role of Landsat 8, the latest satellite in the world's longest, continuous program of Earth observation. 42) 43)

- Now available to the public, the compendium of 23 published papers describes how Landsat 8 is the most capable of the seven operational Landsat missions and highlights how Landsat 8's enhanced performance and new capabilities enable better science and research results. The selected articles cover topics from how the instrument's performance gives higher quality electromagnetic measurements to how its improved geometry allows for the tracking of moving ice sheets.

• October 14, 2016: Glaciers cover 11% of Iceland's landscape, the largest being the Vatnajökull – known as the Vatna Glacier in English – which at 8000 km2 is also the largest glacier in Europe (Figure 30). Up to 1 km thick, the Vatna ice cap has about 30 outlet glaciers – many of which are retreating owing to warming temperatures. 44)

- A number of volcanoes lie underneath this ice cap, including the infamous Grímsvötn, which caused disruption of northern European air traffic in recent years following eruptions and the spread of ash plumes. This volcano is visible as a black arc on the central-left side of the image. In 1996 an eruption of Grímsvötn caused some of the overlying glacial ice to melt. The water then broke out of the ice cap and flooded the nearby outwash plain, causing millions of dollars' worth of damage.

- In the upper-central part of the image, in an area known as the Holuhraun lava field, we can see a bright orange strip of lava through a crack in the surface. This type of elongated volcanic eruption is known as a fissure vent, and usually occurs without any explosive activity.

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Figure 30: Landsat-8 false color image of the Vatnajökull glacier (in blue) and the Holuhraun lava field on Iceland's southeastern coast, acquired on Sept. 6, 2014 (image credit: USGS, ESA)

• September 21, 2016: Today the Landsat project celebrates the 50th anniversary of Secretary of the Interior Stewart Udall's 1966 announcement of "Project EROS (Earth Resources Observation Satellites)". Udall's vision paved the way for what we today know as Landsat, and gave the world the confidence to create satellite systems to monitor our planet with a new perspective. 45)

- Secretary Udall's vision to create "a program aimed at gathering facts about the natural resources of the Earth from earth-orbiting satellites" was an idealistic goal at the time, but on July 23, 1972, the first Earth Resources Technology Satellite (ERTS) was launched from Vandenberg Air Force Base in California. In 1975, it was renamed Landsat 1. Since then, six more Landsat satellites have followed, collectively capturing millions of images of Earth, and creating an impressive archive that has been available at no charge since 2008.

• September 21, 2016: In late July 2016, an illegal campfire gave rise to the Soberanes fire that grew near the California coast between Monterey and Big Sur. The wildfire continued burning in the Los Padres National Forest through August. As of late September, it was still not fully contained. The Landsat-6 satellite acquired the image of the area on September 15, 2016 (Figures 31 and 32, same image with different spectral bands). 46)

- When these images were acquired, the fire had burned 435 km2 and was 55 percent contained. Some of the smoke from the fire hung low in the local valleys, most notably on September 18 after a temperature inversion set in. An air quality report called the air "unhealthy" at sites near the fire including Carmel Valley, Cachagua, and Tassajara.

- Not all of the smoke, however, stayed local. The Ozone Mapper Profiler Suite (OMPS) on Suomi-NPP (Suomi National Polar-orbiting Partnership) satellite observed smoke fanning out to the southwest over the Pacific Ocean and inland to the east and northeast.

- By September 20, the fire was 71 percent contained and had burned 490 km2. New evacuations were in place after winds carried embers across the containment line and started a new "spot" fire measuring less than 1 km2.

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Figure 31: False color image of OLI on Landsat-8 which combines shortwave infrared, near-infrared, and green light to provide a clear view of the charred landscape (dark red). The main area of active fire (bright red), which is nestled amid unburned vegetation (green), is small by comparison (NASA Earth Observatory, image by Jesse Allen using data from USGS)

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Figure 32: OLI image on Landsat-8 in in natural color. The burn scar in this image is brown; the surrounding unburned forest is dark green. The burn scar is subtle in natural color, so the clearest indication of the fire is the rising plume of smoke (NASA Earth Observatory, image by Jesse Allen using data from USGS)

• August 25, 2016: This week marks the 100th anniversary of the National Park Service. We are celebrating this milestone with a gallery of images. On most summer days, a trip to North Carolina's Outer Banks means a peaceful day at the beach soaking up the sun and playing in the waves. But there is evidence all around that this beach is not always so serene. The very existence of these barrier islands is due to the power of wind and water. 47)

- Figures 33 and 34 show a segment of the barrier islands in the vicinity of Cape Hatteras National Seashore. The park's origins date back to the 1930s, when Congress authorized the creation of this first "national seashore park" in the United States. It wasn't until 1953 that the National Park Service acquired enough land to establish the park, and another five years before facilities were in place and the park could formally open.

- Stanley Riggs, a scientist who in the mid 1960s developed the coastal and marine science program at East Carolina University, pointed out some notable features. Skinny parts of the island chain, which appear mostly white without any green vegetation, are "simple" barrier islands. These areas are generally eroding and thinning. The shoreline continues to recede and weak spots form, at which point water from the Atlantic Ocean can break through and form an inlet.

- The year that these images were acquired was a relatively quiet one for storms. In other years, however, hurricanes and nor'easters have opened multiple inlets in a single season. Within a year or two after an inlet opens up, a flood tide delta typically forms behind it and continues the natural cycle of island rebuilding. That cycle is influenced, however, by the human development and maintenance of structures such as highway 12—the road that runs like a spine down the length of North Carolina's barrier islands.

- In contrast, the green vegetated areas along the islands are usually wider and older "complex" barrier islands. The second image shows a detailed view of one such area at Cape Hatteras. In this view, you can see parallel east-west ridges that have built up over time behind the tip of the cape. The relative height of the dunes and distance from the ocean have allowed forests to grow. These are also the more protected and therefore urbanized parts of the island chain.

- Still, the wind and waves take a toll, eroding the east-facing shoreline of the cape and moving sand southward to Diamond Shoals. This huge pile of sand just below the ocean surface extends for about 16 km — and that's small compared to the shoals extending out from other capes in North Carolina, which Riggs calls the "graveyards of the Atlantic" for the hazard they have long posed to ships.

- Areas west of the islands do not escape the forces of nature. Parts of the mainland submerged by rising sea levels have transitioned to sandy shoals, which appear as various shades of white on the west side of Pamlico Sound. These sandy areas have a depth of 3 m or less. The center of Pamlico Sound is darker and deeper, about 6 m. Then, toward the eastern side of the sound the water becomes shallow again as you approach the barrier islands, an area known as Hatteras Flats.

- Storms have even built a mini barrier island on Hatteras Flats—miles out in the sound, but walkable from almost anywhere on the main barrier islands. The flats support rich grass beds, marshes, and nutrient beds that feed the mid-Atlantic fisheries. The park area as a whole is "an incredible habitat for wildlife," Riggs said. Birds overwinter on the islands, and sea turtles nest on its beaches.

- The islands in their natural state are resilient. But storms and coastal change can be hard on infrastructure and the roads that visitors use to access the park. "A little rise in sea level and next series of storms could do a number on that highway out there," Riggs said. "There will always be a national seashore—you just might have to get there by boat."

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Figure 33: These islands have been in flux long before the park was established, and they continue to change today. These images show a moment in time on June 7, 2015, captured with the Operational Land Imager (OLI) on the Landsat-8 satellite. Various stages of island evolution—from build-up to erosion—are all visible along the island chain (image credit: NASA Earth Observatory, images by Jesse Allen, using Landsat data from the USGS)

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Figure 34: Landsat-8 detail image of Cape Hatteras (image credit: NASA Earth Observatory, images by Jesse Allen, using Landsat data from the USGS)

• July 4, 2016: The Greater Boston area, encompassing the eastern third of Massachusetts, is a playground for the American history enthusiast. Sites important to the American Revolutionary War are interspersed throughout the modern-day metropolitan region; the view from space shows how preserved historic landscapes coexist with the new (Figures 35 and 36). 48)

- In December 1773, American colonists protested British taxation and regulation by dumping hundreds of chests of tea overboard from merchant ships into Boston Harbor. The series of events that followed—including the march of British troops westward to confiscate a cache of weapons—culminated in battles in the towns of Lexington and Concord. The battles marked the start of the Revolutionary War in April 1775.

- The conflicts near Concord and Lexington are memorialized at Minute Man National Historical Park, shown in detail in the second image. By the 1950s, the area grew crowded with roads and suburban growth. Gas stations, restaurants, and an airfield all cropped up in an area that was once farmland and open fields. The park was established in 1959 in part to protect the historic landscape from further development.

- Route 2A cuts through the park and Hanscomb Field still stands as a nearby reminder of 20th Century modernization. In 2003, the National Trust for Historic Preservation listed Minute Man National Historical Park and nearby historic sites as one of the 11 most endangered historical places in the United States.

- Steps have been taken to restore historic structures and to return the landscape to one that more closely resembles the look and feel of the 18th century. For example, many power lines have been removed; stone walls have been rebuilt; and agricultural fields have been opened up. In 2009, the park boundaries grew to include the now-restored Barrett House and its surrounding farm, an important landmark of the war.

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Figure 35: Overview of the Boston region from Boston Harbor to National Historical Park, acquired with OLI of Landsat-8 on October 15, 2015 (image credit: NASA Earth Observatory, image by Jesse Allen using Landsat data from the USGS)

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Figure 36: Detail map of the Minute Man National Historical Park acquired with OLI of Landsat-8 on October 15, 2015 (image credit: NASA Earth Observatory, image by Jesse Allen using Landsat data from the USGS)

• June 15, 2016: Glacial Change in Montana's Blackfoot-Jackson Basin is shown in two Landsat images of 1984 and of 2015. At the current rate of glacier melting, a study arrived at the year 2030, in which the Montana's Glacier National Park will likely be glacier-free. 49)

- Scientists arrived at the year 2030 through a simple geospatial model running on software from the 1990s. The model depicted the change expected to occur to glaciers in the Blackfoot-Jackson basin (shown above), an area that contains the largest concentration of glaciers in Glacier National Park. At the time of the study, glaciers in the basin were also among the park's largest. "It was conjectured that if the largest glaciers disappeared by 2030, most of the smaller ones would probably disappear too," said Daniel Fagre, a research ecologist for the Northern Rocky Mountain Science Center of the U.S. Geological Survey.

- The model took into account the basic parameters, such as warmer summer temperatures and meteorological snowfall. It did not account for more complicated factors such as "snow avalanching" and "snow scouring"—things that can keep a small glacier alive. But despite its simplicity, the model painted an accurate, if broad, picture of the situation: the shrinking of the ice in Glacier National Park is real and happening fast. " People focus too much on the date, but the basic story is still true," Fagre said. "These glaciers will be more or less gone in the next several decades."

- Before these images were acquired, glaciers in the basin had already decreased from 21.6 km2 in area in 1850 to just 7.4km2 in 1979. The Blackfoot and Jackson glaciers once ran together, as a photograph from 1914 shows; by 2009, they had retreated into separate valleys.

- Other phenomena have left their mark on the landscape. In the 2015 image, a burn scar from the Thompson fire is visible southeast of Blackfoot glacier. As global and regional climate continues to warm, the frequency of fire in the park could increase.

- For now, the clearest reflection of climate change is the ice. Year-to-year weather variations matter somewhat, but most of the loss is a response to decadal trends in warming. As we approach 2030, most of these glaciers will be "small insignificant lumps of ice on the landscape," Fagre said. "These tiny remnants could last 10 to 15 years past that time if they are in sheltered places, but the park will no longer really have viable glaciers."

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Figure 37: Landsat-5 TM (Thematic Mapper) image of the Montana's Glacier National Park, acquired on August 17, 1984 (image credit: NASA Earth Observatory, images by Jesse Allen, using Landsat data from the USGS)

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Figure 38: Landsat-8 OLI (Operational Land Imager) map of the Montana's Glacier National Park, acquired on August 23, 2015 (image credit: NASA Earth Observatory, images by Jesse Allen, using Landsat data from the USGS)

• May 31, 2016: The Rio Grande is a major North American river, flowing more than 3,000 km from Colorado to the Gulf of Mexico. In 1978, the U.S. Congress designated 315 km of the river along the U.S.-Mexico border as a "wild and scenic river." The designation protects the river's ecosystem and its natural free-flowing state. 50)

- Along the 134 km of the Lower Canyons area, you will find yourself in a truly remote river wilderness. This remoteness is likely part of the reason that a relatively small number of people visit the area compared to other sites managed by the National Park Service. Paddlers visit the Lower Canyons of the Rio Grande take advantage of this free-flowing river, maneuvering water ranging from calm to rapid as they float between canyon walls rising up 150 to 450 m. But it can take at least five days to emerge from a paddling trip through this part of the river.

- Despite its remoteness, the river is not immune from human influence. Dams upstream can affect the river downstream, changing the natural flooding cycles that normally shape the banks and build habitat for plants and animals. Drought can also affect the Rio Grande. In 2003 and 2015, portions of the river near El Paso ran dry.

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Figure 39: The OLI (Operational Land Imager) of Landsat-8 acquired this image of the river on March 21, 2016. It shows a segment of the protected river east of Big Bend National Park. In these "Lower Canyons," layers of rock were laid down about 100 million years ago and then shaped by lifting, folding, and faulting. The river cut through these rocks to form the steep walled canyons visible today (image credit: NASA Earth Observatory image by Jesse Allen, using Landsat data from the U.S. Geological Survey. Caption by Kathryn Hansen)

• May 2016: Preparation for Emergency Conjunction Avoidance Maneuvers: The Landsat FOT (Flight Operations Team) has developed an E-RMM (Emergency -Risk Mitigation Maneuver) procedure, a combination of quick-build spacecraft command buffers and computer scripts, using specifically-defined and human-readable formats, to allow an engineering team the ability to quickly perform complex, critical spacecraft operations. 51)

- So far, Landsat-8 has been collecting nearly 75% more science images per day than originally designed, improving the cost-benefit ratio and expanding science coverage to nearly every day-lit opportunity through most of the year.

- The improved science collection tempo is enabled largely by the use of small, general-purpose command buffers in spacecraft flight software, called ROS (Relative Operations Sequences). ROS files (also called binaries, loads, or buffers), are small, temporary computer files containing a series of commands separated by defined delays, so that a single command can start the buffer and its contents will execute in a set order with predictable timing.

- The ROS loads can be built quickly and easily on the ground for any purpose, and one or more can be uplinked on each spacecraft contact as necessary. The addition of a special ground software script that begins the load process onboard at a specific time also allows the relative timing of the commands to be matched to wall clock times for precise execution of these buffers. Currently, ROS buffers are used for spacecraft science recorder file maintenance, automatically resetting balky equipment, and certain types of troubleshooting and fault recovery.

- The original operations concept at launch was to anticipate the need for a burn at the earliest warning, monitor until a few days before TCA (Time of Closest Approach), and evaluate the need for a burn around two days ahead of the Julian day when it would be executed, though the specific commit time was driven by the conjunction geometry and the TCA relative to the change of Julian day. A general spacecraft command load would be purpose-built to include the burn activities, including suspending science collections for the duration, and when uplinked we would be committed to performing at least part of the burn activities and losing the science collections. In preparing this early, we also committed to using best-guess information about the ΔV requirement; future changes to the ΔV would involve manual commanding and the risk of sending incorrect values.

- As ROS buffers became more widely used in day-to-day operations, a new process was developed to quickly build a special-purpose, propulsive-maneuver command sequence that could be used to avoid orbital debris. The burn concept was termed an Emergency Risk Mitigation Maneuver (E-RMM); it allows the burn to take place as soon as the new orbit can be screened for other conjunctions, instead of waiting for a new spacecraft command load to be built, and therefore provides a more responsive solution in a severely restrictive timeline. The special-purpose buffer includes all the commands the FOT normally inserts into the load for a standard orbit adjust maneuver (e.g., enabling burn software, setting thruster on-time duration, and so on) that can be uplinked, changed, rebuilt, and re-uplinked several times if necessary, and that could be started or stopped with minimal impact to the science imaging schedule. This buffer is built using a special computer script that takes in "knowledge" of when the buffer will start and codes in appropriate delays so that, once the buffer is started at the planned time, key events will happen at the desired wall clock time.

- As a result, FOT can plan and re-plan a burn quickly, uplink and execute the ROS buffer in a single contact, and reduce the time and uncertainty of burn planning from several days to around six hours. The use of a buffer that runs independently of the spacecraft's main command load also offers the ability to work around change-of-day and change-of-year boundaries; a clean break in science data collection from which the FOT can easily recover; and the ability to verify burn parameters quickly, maximizing the abort window.

- As of spring 2016, the Landsat-8 FOT has executed two demonstration burns and a DMU (Drag Make-Up) using E-RMM products. Though we have not executed an actual E-RMM burn on-orbit using this process, our ability to do so has saved team workload and science data collection on at least six occasions through the ability to wait and see how the conjunction event develops. As the low-earth orbit regime will continue to be polluted by space debris for the foreseeable future, the flexibility of burning correctly, and only when necessary, will provide significant benefits to cost, risk, and science for Landsat-8 and its successors.

• May 10, 2016: If a volcano erupts and there is no one there to see it, did it really erupt? Before the advent of satellites and seismic monitoring, volcanic eruptions in distant places would mostly go unnoticed unless they were absolutely extraordinary. Today, scientists can pick up signatures of events occurring far from any human observers.

- That was the case in late April and early May 2016 when satellite sensors detected signs of a volcanic eruption in the far South Atlantic Ocean between South America and Antarctica. Mount Sourabaya, a stratovolcano on Bristol Island, appeared to be erupting for the first time in 60 years. There are no human residents of the island, which is almost always covered in glacial ice and snow. 52)

- OLI (Operational Land Imager) on Landsat-8 acquired the Bristol Island image of Figure 40 using a combination of shortwave-infrared, near-infrared, and red light (Landsat bands 6-5-4) that helps detect the heat signatures of an eruption. The image shows the heat signatures (red-orange) of what is likely hot lava, while white plumes trail away from the crater. The band combination makes the ice cover of the island appear bright blue-green.

- With a roughly rectangular shape of 12 x 14 km, Bristol Island is one of the largest in the South Sandwich Islands chain. The highest peak on the island stands 1100 m above sea level. Due to the remote location and the lack of landing sites amidst its ice cap, the stratovolcano is one of the least studied in the world. The last known eruption on Bristol Island was reported in 1956.

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Figure 40: OLI on Landsat-8 acquired this false-color image on May 1, 2016 (image credit: NASA Earth Observatory, image by Jesse Allen using data from USGS)

• April 30, 2016: A key shipping route through Egypt recently received a major overhaul. The Suez Canal—the first artificial waterway connecting the Mediterranean Sea and the Red Sea—initially opened in November 1869 after 10 years of construction. The "New Suez Canal" opened in 2015 after just one year of construction. 53)

- Since its inception, the canal has been an economically important shortcut between Europe and Asia. The passage through Egypt meant cargo ships no longer had to sail around the southern tip of Africa. Expansion projects over the decades have helped the canal accommodate more traffic and larger ship sizes. The latest effort not only widened and deepened areas along the existing canal, it also added a 35 km new canal that runs parallel to the old one.

- The older single-lane canal, including the north and south access channels, spans 193 km from Port Said on the Mediterranean Sea to the Port of Suez on the Red Sea. Follow the canal south about 76 km from Port Said, and you reach the city of Ismailia, centered in these images. At km 95 you reach Great Bitter Lake, visible at the bottom of these images. The lake is one of the canal's designated holding and passing areas. The time spent waiting in these bypass areas was eased with the addition of the new parallel canal, visible in the right image.

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Figure 41: OLI (Operational Land Imager) on Landsat-8 acquired these images of the canal's mid-section, where the project was focused. The left image shows the area on August 6, 2014, around the start of the expansion; the right image was acquired April 5, 2016, about nine months after the expansion was complete (image credit: NASA Earth Observatory, images by Jesse Allen, using Landsat data from the USGS)

• April 12, 2016: Toward the end of April, reprocessing efforts will begin for Landsat-8 OLI/TIRS scenes acquired from January 1 to March 31, 2016 along with data acquired during April, to create nominal Level-1 products containing valid TIRS data. Additionally, all future Landsat-8 scenes will contain valid TIRS data—however, newly processed data will use preliminary estimated position information from the TIRS SSM (Scene Select Mirror). 54)

- To compensate for not having TIRS SSM encoder information to indicate where the TIRS sensor is pointing, a new algorithm has been developed to provide estimates for the TIRS SSM encoder position. Note: SSM is also referred to as "Scene Select Mechanism".

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Figure 42: Diagram of the TIRS telescope and focal plane with the elements contributing to the spectral response labeled (image credit: NASA)

• April 6, 2016: In the early 1600s, the English established their first permanent settlements in North America at Jamestown, Virginia, and then Plymouth, Massachusetts. But those were not the first attempts to colonize America. In the late 16th century, settlers started a settlement on Roanoke Island, North Carolina. The disappearance of that colony remains a mystery today. 55)

- On June 7, 2015, OLI (Operational Land Imager) on Landsat-8 captured this natural-color image of the northern half of Roanoke Island, which is tucked between North Carolina's mainland and barrier islands. Fort Raleigh National Historic Site, which was established 75 years ago on the island's northern shore, spans aquatic habitats, swamp forests, and a rare maritime evergreen forest. Fort Raleigh also preserves relics of England's first attempts to colonize the New World, as well as the history Native Americans, European Americans, and African Americans on Roanoke Island.

- The events that took place within the boundaries of the historic site are subject to debate. Initially, archaeologists thought that the northern part of the island was the main settlement of more than 100 English colonists who arrived in 1587. They based their claim on some European artifacts found within the historic site. But the artifacts were not indicative of habitation, and some experts speculate that the main settlement and fort were located farther south near Shallowbag Bay. Here, ships would be sheltered and settlers could more easily offload supplies.

- In any case, the island's entire settlement was found deserted in 1590. The story of the so-called "Lost Colony," as interpreted by playwright Paul Green, has been performed since 1937 at Fort Raleigh's beachfront theater, visible as a small tan fleck near the shoreline. Researchers have theorized that some of the settlers relocated about 80 km south to Hatteras Island. Other clues uncovered in 2012 from an old map suggest that some colonists might have ventured inland.

- At Fort Raleigh, one can also learn about the culture of the Algonquians, the natives who long inhabited the area, cultivated the land, and fished in the sounds long before Europeans arrived. Or read about a colony established centuries later during the Civil War, when more than 1,000 people—former slaves and families—found refuge on Roanoke.

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Figure 43: OLI image of the northern half of Roanoke Island, along with the Fort Raleigh National Historic Site, acquired on June 7, 2015 (image credit: NASA Earth Observatory, image by Jesse Allen, using Landsat data from the USGS)

• March 30, 2016: Just 25 km northwest of downtown Atlanta, a long parcel of undeveloped forests and hills cuts through the metropolitan suburbs (Figure 44). But the site was not always so peaceful. During the summer of 1864, the 2,965 acres (1,200 hectare) of this Civil War-oriented national park teemed with tens of thousands of men, horses, and cannons. 56)

- The hilly terrain played a role in the battle that commenced on June 27, 1864. Confederate forces had established an arc-shaped line that included Kennesaw Mountain, Little Kennesaw Mountain, Pigeon Hill, and areas to the south. A head-on attack by Union forces failed, and thousands of soldiers were killed. Park visitors can still see historical features, such as trenches that were dug for infantry and cannons. The defeat, however, was not enough to stop Union troops from advancing toward Atlanta, cutting off railroad supply lines, and ultimately reaching the city in early September 1864.

- As this satellite view shows, the mountain ridge is a major geographic feature of the area. Steep, rocky slopes of the range rise on average to about 550 m above sea level. Outcrops and boulders are scattered among a mostly forested landscape. The image was acquired in winter, so vegetation appears less lush and green than it would in summer, when the deciduous trees have foliage. Still, historical surveys note that the landscape during the Civil War battle was even barer, as the summits had been cleared of trees.

- Today, heavy urbanization—roadways and private residential housing—surrounds the park. Construction around the battlefield put additional pressure on the park, which stands as the largest free park in the heavily developed Atlanta metropolitan area. Park managers have had to balance preservation of the memorial landscape with the needs of nearby residents who use the space for recreation and as a commuting route.

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Figure 44: On February 5, 2016, OLI (Operational Land Imager) on Landsat-8 acquired this image of Kennesaw Mountain National Battlefield Park in Georgia, USA. The park protects land associated with a historic battle of the Civil War's Atlanta Campaign (image credit: NASA Earth Observatory,, image by Jesse Allen using Landsat data from the USGS)

• March 20, 2016: It's an archetypal American story: grandeur and legend rising from modest, humble beginnings. Both islands (Figure 45) were once home to vast oyster beds, and one was built up from the spoilings and fill from nearby dredging operations. Then in the late 19th century, the two islands became central to the narrative of the United States of America as "melting pot" for different cultures and a beacon of hope for the "tired, poor, huddled masses" of the world looking to make a new start. 57)

- Liberty Island, once known Bedloe's Island, was an oyster harvesting ground for the Lenape Indians and later for early European settlers. It then saw stints as a quarantine station, a hospital, and a military outpost. Fort Wood, with its walls shaped into an eleven-point star, eventually became the foundation for the Statue of Liberty, which was completed in 1886. The statue was designated a national monument in 1924, and in 1933 responsibility for the statue and island was transferred to the National Park Service. Today Liberty Island is one of the most visited sites in the National Park system, with nearly 4.3 million visitors per year.

- Ellis Island has long had a complicated relationship with the "New Colossus" standing next door. Many people referred to it as the "Island of Hope, Island of Tears." From 1882 to 1954, more than 12 million new immigrants to the United States made their entry to the country through the processing station on Ellis Island. The busiest year was 1907, when 1,004,756 immigrants were processed — 11,747 of them on April 17, the busiest day.

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Figure 45: On Oct. 25, 2015, OLI (Operational Land Imager) of Landsat-8 acquired this image of Ellis Island and the Liberty Island (with the Statue of Liberty National Monument). Those once-modest islands stand like magnificent sentries in New York Harbor, with Jersey City, New Jersey to the west and the lower reaches of Manhattan Island and Brooklyn to the northeast and east (image credit: NASA Earth Observatory, image by Jesse Allen, Landsat data from USGS)

March 15, 2016: ESA has agreed with NASA, NOAA and the USGS to make data available to them from the European Sentinel satellites. With the third Copernicus satellite, Sentinel-3A, recently launched, ESA has signed technical arrangements with these US agencies for accessing Sentinel data. These arrangements coordinate the technical implementation covering the Sentinel data access to the US. 58) 59)

- ESA and its international partners are pursuing Earth observation activities in a number of areas of common interest, and are sharing each other's satellite data. All sides are committed to the principle of full, free and open access to the European Sentinel and the NASA, NOAA and USGS Earth observation satellite data and information.

- The signed arrangement will allow NASA, NOAA and USGS to systematically retrieve the Sentinel data from a dedicated International Data Hub operated by ESA. These agencies will then transfer the data to the US, absorbing them in their existing data access systems, such as EarthExplorer and GloVIS, and disseminating them to their own user communities.

- For over three decades, ESA has been acquiring, processing and disseminating data from a number of US missions such as Landsat to the European user communities as part of its Earthnet Third Party Mission Program.

- While the US agencies' objective is to serve the US user communities with priority, the Sentinel data will continue to be freely accessible for Copernicus Services, as well as to users worldwide, through the ESA operated data hubs.

• March 13, 2016: In February 2016, the United States government established the world's second-largest desert preserve. In designating three new national monuments in the California desert, the U.S. DOI (Department of the Interior) added 1.8 million acres (728, 400 hectare) to an existing 7.6 million acres (3, 075, 600 hectare) of protected land. This image from the Operational Land Imager (OLI) on Landsat 8 shows how they all connect. (The image is a composite of satellite data from Landsat 8 passes on February 8 and February 17, 2016.). 60)

- The western edge of Sand to Snow National Monument is located about 120 km east of downtown Los Angeles. This aptly named monument encompasses 150,000 acres (60,700 hectare) from the floor of the Sonoran Desert to the mountaintops in San Bernardino National Forest. Ecological diversity at the various altitudes makes this monument unique.

- Hikers encounter some of this diversity along 50 km of the Pacific Crest Trail that crosses through the monument, from Whitewater Canyon up 2100 m to Mission Springs.

- Sand to Snow shares a common boundary with Joshua Tree National Park, which in turn connects to Mojave Trails National Monument—the largest new addition at 1.6 million acres. Lava flows and mountains spread across this tract of the Mojave Desert. The focal point is the sand dunes; in particular, the remote and nearly pristine Cadiz Dunes that formed from the sand of dry lake beds.

- Finally, tucked into the northeast corner of the pre-existing Mojave National Preserve is the smallest addition—the 20,920 acres (8,460 hectare) of Castle Mountains National Monument. But this smallest addition is an important one. Inclusion of Castle Mountains connects ecosystems in the New York Mountains and the Piute Mountains. Protection of this area ensures that habitat stays intact for wildlife such as the desert bighorn sheep.

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Figure 46: Landsat-8 OLI composite image of the California desert preserve acquired on Feb. 8 and 16, 2016 (image credit: NASA Earth Observatory, Jesse Owen using Landsat data from USGS)

• Feb. 18, 2016: A partnership has been established between ESA (European Space Agency) and the USGS to allow for USGS storage and redistribution of data acquired by the MSI (Multispectral Instrument) on ESA's Sentinel-2A satellite that was launched in June 2015. The collaborative effort between ESA and USGS will provide for public access and redistribution of global acquisitions of Sentinel-2A data at no cost, allowing users to download the MSI imagery from the USGS. The MSI sensor acquires 13 spectral bands that are highly complementary to data acquired by the USGS Landsat-8 OLI (Operational Land Imager) and Landsat 7 ETM+ (Enhanced Thematic Mapper Plus). 61)

• Feb. 11, 2016: Today the Landsat-8 spacecraft is 3 year on orbit and operational. So far, the satellite has acquired more than 675,000 scenes, adding valuable time series data to the USGS Landsat Archives!

- As of February 10, 2016, many of the scenes acquired from October to December 2015 have been reprocessed into nominal Level-1 products containing valid TIRS (Thermal Infrared Sensor) data and are available for download. The generation of Landsat-8 surface reflectance products from these data will become available within the next week. 62)

• January 26, 2016: Two days after a massive winter storm system dropped snow from Tennessee and Georgia to Massachusetts, millions of Americans are digging out. By some news accounts, more than 30 million people lived in areas that received at least 50 cm of snow, and 3 million more saw at least 75 cm. 63)

- The highest snow total was recorded in Glengarry, West Virginia at 107 cm. Snow totals approached records at airports near Baltimore at 74 cm, Philadelphia at 57 cm, and Newark, New Jersey at 71 cm. The National Zoo in Washington, D.C. counted 57 cm and Central Park in New York picked up 68 cm.

- At least 37 people have died as a result of car accidents, hypothermia, carbon monoxide poisoning, or over-exertion from shoveling snow, according to multiple news reports. At least a quarter-million people have lost electric power, and more than 13,000 airline flights have been canceled.

- Beyond the snowfall, near-hurricane force winds combined with astronomically high tides to produce storm surges on the Delaware and New Jersey coasts. Sea water poured into coastal towns, while extensive beach erosion occurred as far north as Massachusetts.

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Figure 47: OLI on Landsat-8 captured this natural-color image of Virginia, Maryland, and Washington D.C. in the early afternoon on January 24 (image credit: NASA Earth Observatory, Joshua Stevens)

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Figure 48: Close-up of Washington D.C. drawn from the image of Figure 47; note the long shadow cast by the Washington Monument (image credit: NASA Earth Observatory, Joshua Stevens)

• January 13, 2016: The Landsat-8 TIRS (Thermal Infrared Sensor) data continue to be collected with the scene select mirror encoder electronics disabled (mode 0). While in this mode, the TIRS LOS (Line of Sight) model will be regularly updated and modifications are being made to automate revisions to the LOS in the LPGS (Level-1 Product Generation System). 64)

- OLI and TIRS data that have been collected through the 4th quarter of 2015 (October-December); they will be reprocessed into nominal Level-1 products containing valid TIRS data, and will be available in February 2016.

- TIRS data acquired during the 1st quarter of 2016 (January-March) will be reprocessed and made available in April 2016. A strategy is being developed for generating near-realtime products moving forward while operating in mode 0. More details will be posted on the Landsat Missions Web site as they become available.

• Nov. 17, 2015: The Landsat-8 FOT (Flight Operations Team) continues to monitor current levels within TIRS (Thermal Infrared Sensor) SSM (Scene Select Mirror) encoder electronics. The FOT and Calibration Validation team are continuing to investigate the current anomaly and analyze instrument telemetry data in order to accurately measure the position of the SSM and to develop the necessary parameters for the processing TIRS data under an alternative operations concept. 65)

- TIRS data will continue to be routinely collected but will not be processed to Level-1 products until the geometric model parameters are finalized and the algorithms and code in the LPGS (Landsat Level-1 Product Generation System) have been updated, tested, and verified. These activities are expected to be completed no sooner than February 2016, at which time all products will be reprocessed to provide valid TIRS data.

- Following the implementation of an alternate TIRS processing capability, the zero-fill Landsat-8 scenes will be reprocessed and made available from the USGS archive. Mission operations will continually assess potential opportunities for return to normal operations on the B-side encoder electronics. However, at this time, the probability of return to normal operations is unknown.

• Nov. 13, 2015: Free Data Proves Its Worth for Observing Earth. Since late 2008, when Landsat Earth observation images were made available to all users free of charge, nearly 30 million Landsat scenes have been downloaded through the USGS (U.S. Geological Survey) portal – and the rate of downloads is still increasing. That's a lot of free data about the state of the planet. But what is it worth? How valuable can something free possibly be? 66)

- The worth of many things is related to scarcity. If there are too many houses or diamonds, bushels of corn or barrels of oil for sale, the price for these items falls. A free market determines the market value of what we might hope is a $500,000 house or a $5,000 diamond.

- The concept of market value breaks down for goods and services that society has determined should be freely available to everyone. Free data for earth observation fits into this category. It is a public good ­– along with public education, public roads, and public parks. While these services are not actually free (they are, of course, funded with public money), we know that the broad use of such services benefits all of society so the cost to each individual user is largely borne by all.

- The Department of the Interior's policy of releasing the full Landsat archive at no cost allows researchers around the world in government, in the private sector, and at universities and institutions to generate even more data applications that are good for society. These purpose-driven data applications – known on mobile devices as "data apps" – can serve commercial endeavors in agriculture and forestry; they can enable land managers in and out of government to work more efficiently; they can help us define and address critical climate and environmental issues.

- The DOI (Department of the Interior) policy of releasing the full Landsat archive at no cost allows researchers around the world in government, in the private sector, and at universities and institutions to generate even more data applications that are good for society. These purpose-driven data applications – known on mobile devices as "data apps" – can serve commercial endeavors in agriculture and forestry; they can enable land managers in and out of government to work more efficiently; they can help us define and address critical climate and environmental issues.

- In the United States, the federal government invests about $3.5 billion annually in civil earth observations and data (including Landsat and other satellites, weather, GPS, etc.) across multiple agencies, while optimizing related investments that are also made by state, local and tribal governments, academia, and industry. The information derived from earth observations supplies the foundation for scientific advances in many fields and enables multiple federal agencies and partners to carry out their missions. Federal investments in various aspects of earth observation are conservatively estimated to add $30 billion to the U.S. economy each year by providing Americans with critical knowledge about natural resources, climate and weather, disaster events, land-use change, ecosystem health, ocean trends, and many other earth-related phenomena.

- The USGS, a bureau of the DOI, is a major contributor to civilian earth observation through its support of the Landsat mission in partnership with NASA. First launched by NASA in 1972, the Landsat series of satellites has produced the longest, continuous record of Earth's land surface as seen from space.

- Landsat images spanning four decades have been used by scientists and resource managers to monitor water quality, glacier recession, coral reef health, land use change, deforestation rates, and population growth. To give a few examples of Landsat's many commercial applications, Landsat data have been used to track the use of irrigation water, to assist drought-stricken California grape growers, and to contribute to the success of a forestry start-up company. As an indication of widespread public interest in Landsat data, third party avenues to the data and innovative ways to use it are available from Amazon, ESRI (Environmental Systems Research Institute) and Google.

- A recent White House-led assessment determined that Landsat is among the Nation's most critical Earth observing systems, second only to GPS and weather. In 2013, the U.S. National Research Council found, "The economic and scientific benefits to the United States of Landsat imagery far exceed the investment in the system." In 2014, the Landsat Advisory Group of the National Geospatial Advisory Committee was as even more specific in its finding, "The economic value of just one year of Landsat data far exceeds the multi-year total cost of building, launching, and managing Landsat satellites and sensors."

- Other nations recognize the benefits of free and open data. Fundamental knowledge of the land and its resources is a basic need for effective government and a productive economy in any nation. More than 30 countries and geopolitical groups now have earth observing satellites, reflecting a wide range of national priorities around the world for environmental monitoring and economic growth. At the same time, more countries are adopting policies of full, free, and open data for earth observation, whether the observation operations are conducted by their national satellites or whether the data is shared between countries and with the public. — In Rwanda, for example, the government has developed and used open data to support national land use planning via their National Land Use Portal, which provides transparency collaboration and cooperation among many different partners to help shape a more sustainable future. Brazil, a co-leader of the Open Government Partnership in South America, provides its citizens the opportunity to participate in the planning and development of public policies by providing government data on hydrography, transportation, energy and communications, and more, through their Brazilian Portal of Open Data.

- October 19, 2015: The United States and the European Commission signed an agreement on Copernicus Cooperation. The European Copernicus Program has adopted a policy of free and open data from its Sentinel series of satellite missions, helping make Europe a world leader in the implementation of free and open data policies. U.S. Deputy Assistant Secretary of State for Science, Space and Health Jonathan Margolis and European Commission Director for Space Policy, Copernicus and Defence Philippe Brunet signed the arrangement in Washington, D.C., on October 16. The arrangement will allow experts from U.S. agencies, including NASA, NOAA, and the USGS, to pursue cooperative data sharing activities with European counterparts, including the European Commission,ESA (European Space Agency), and EUMETSAT (European Organization for the Exploitation of Meteorological Satellites). This cooperation will enhance data access, validation, and quality control as well as satellite system compatibility, interoperability, and instrument inter-calibration. 67)

• In October 2015, a system of storms caused significant flooding in most of California's Death Valley National Park. Flash floods from the storm destroyed roads and utilities, and damaged several historical structures. In particular, a flash flood of water and mud caused extensive damage to Scotty's Castle, an ornate mansion built in the 1930s and one of the most popular landmarks in Death Valley National Park. The Oct. 18-19 storm dumped ~7 cm of rain, more than the region typically gets in a year. 68)

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Figure 49: A false-color Landsat-8 image highlights hydrogeology in Death Valley after a major flooding event, acquired on Oct. 26, 2015; the areas in green to blue are locations with moisture content. Especially striking is the Badwater Basin, normally a dry lakebed, which became full of water( image credit: USGS, NASA)

• August 12, 2015: Algae are complicated. The little plants can be both good and bad. Single-celled algae called phytoplankton are a main source of food for fish and other aquatic life, and account for half of the photosynthetic activity on Earth—that's good. 69) 70)

- But certain varieties such as some cyanobacteria produce toxins that can harm humans, fish, and other animals. Under certain conditions, algae populations can grow explosively — a spectacle known as an algal bloom, which can cover hundreds of square kilometers. For example, in August 2014, a cyanobacteria outbreak in Lake Erie prompted Toledo, Ohio, officials to ban the use of drinking water supplied to more than 400,000 residents. - In the United States alone, freshwater degradation from "bad" algae costs the economy about $64 million a year.

- NASA, the U.S. EPA (Environmental Protection Agency), NOAA (National Oceanic and Atmospheric Administration), and the USGS (U.S. Geological Survey) are doing something about it. NASA has long used Earth observing satellites to locate algal bloom outbreaks in the ocean. But now, this unique satellite data will be routinely produced in a form that helps US water quality managers monitor the freshwater. Water quality managers will soon, with a peek at their cell phones, have an answer to "how's the water?"

- The four agencies are working on a joint project, sponsored by NASA, to transform satellite data into an indicator of cyanobacteria outbreaks in the freshwater supply. The data will be integrated into an EPA Android smart phone application so environmental officials can see – at a glance – the condition of a specific water body.

- "With our app, you can view water quality on the scale of the US, and zoom in to get near-real-time data for a local lake," explains the EPA's Blake Schaeffer, Principal Investigator for the project. "When we start pushing this data to smartphone apps, we will have achieved something that's never been done – provide water quality satellite data like weather data. People will be able to check the amount of ‘algae bloom' like they would check the temperature."

- Here's how it works: A harmful species of cyanobacteria emits chlorophyll and fluorescent light at various points in their life cycles. Landsat and NASA's MODIS (Moderate Resolution Imaging Spectroradiometer ) can detect these "ocean color" signals, which reveal the location and abundance of cyanobacteria. The project team will collect this data for freshwater bodies and convert it into a form accessible through web portals and the EPA mobile app. In addition to MODIS, they'll draw data from the Sentinel-2 and Sentinel-3 satellites of ESA (European Space Agency).

- With early warning about a developing bloom, officials at water treatment plants will be better able to determine when, where, and how much to treat the water to keep consumers safe. That means unnecessary — and expensive — overtreatment may be avoided. The data will also help park managers alert swimmers, boaters, and other recreational users to hazardous conditions. Says NASA Administrator Charles Bolden: "We're excited to be putting NASA's expertise in space and scientific exploration to work protecting public health and safety."

- The project will also help scientists understand why "bad" algae outbreaks occur. By comparing the color data with landcover change data, they'll learn more about environmental factors that spur algal growth. The result: better forecasts of bloom events. So we'll know when an algae bloom is safe or harmful.

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Figure 50: A vast, seemingly benign bloom of phytoplankton gave the Atlantic Ocean a chalky green color on August 3, 2015. The OLI (Operational Land Imager) on the Landsat-8 satellite observed the scene off the coast of New Jersey and New York, in an area referred to by oceanographers and geologists as the New York Bight (image credit: NASA Earth Observatory)

• July 17, 2015: Located near the western edge of the Sahara Desert, the Eye of the Sahara is a feature that resembles a large eye when viewed from space. Also known as the Richat Structure or Guelb er Richat, the Eye is a symmetrical dome of eroded sedimentary and volcanic rock. The outermost rings measure approximately 40 km across. Persistent northeasterly winds keep much of the dome free from sand, exposing the various layers of rock. The circular feature was initially interpreted to be an asteroid impact structure, but most scientists have now concluded that it was caused by geologic uplift. 71)

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Figure 51: Eye of the Sahara, Mauritania, acquired on June 28 and July 5, 2015 (Lat: 20.983º, Long: -11.459º): This Landsat mosaic of four different scenes shows the geologic feature in false color. By blending visible and infrared wavelengths (bands), scientists can enhance the visibility of the various rock layers in contrast to the surrounding sand (yellow to white), image credit: EROS Data Center, USGS

• June 19, 2015: The Landsat-8 image of the San Francisco Bay Area (Figure 52) was released by ESA in the 'Earth from Space video program'. The city of San Francisco is on a peninsula in the center left section of the image. In the upper-central portion, we can see the delta of the Sacramento and San Joaquin rivers with brown, sediment-filled water flowing down into the larger bay. Starting in the top-left corner of the image and running diagonally to the south is the San Andreas Fault. This is the border between the North American and the Pacific tectonic plates, and is responsible for the high earthquake risk in the area. 72)

Surrounding the Bay one can see densely populated urban areas in white/grey, while forests and park areas appear in shades of green. In the upper-right corner, one can see geometric shapes of large-scale agriculture, with fields in different colors depending on the vegetation type. Distinguishing between different types of land cover is an important task for Earth-observing satellites, helping us to understand the landscape, map how it is used and monitor changes over time.

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Figure 52: The OLI instrument of Landsat-8 captured this image of the San Francisco Bay Area on March 5, 2015 (image credit: USGS, ESA)

• July 7, 2015: The Landsat-8 spacecraft and its subsystems are operating nominally. 73)

TIRS anomaly:

• 19 Dec. 2014 – Mechanical Control Electronics (MCE) reached a yellow over-current limit. Scene Select Mirror (SSM) encoder was switched to ‘Mode-0' which basically disables the encoder

- TIRS bands in products were set to 0 (until we reprocess to handle mirror drift)

• 2 Mar. 2015 – TIRS switched to Side-B ‘Mode-4'. Commission and calibration period followed

- Develop new parameters for CPF, BPF, RLUT

• 30 Apr. 2015 – Reprocessing of TIRS data started

- Mode-0 data (19 Dec. 2014 – 2 Mar. 2015)

- Commissioning data – Side-B ‘Mode-4' data (2 Mar. 2015 – 30 Apr. 2015)

• 14 May 2015 – Reprocessing completed.

TIRS Stray Light:

• Discussion forthcoming (Ron Morfitt)

• Tentatively planned for LPGS 2.6 (~October)

• Plan to reprocess OLI_TIRS data once stray light algorithm is validated

Table 3: Ground System Activities Related to TIRS (Ref. 73)

• May 20, 2015: Since measurements began in 1895, Alaska's Hubbard Glacier has been thickening and steadily advancing into Disenchantment Bay. The advance runs counter to so many thinning and retreating glaciers nearby in Alaska and around the world. The image of Figure53 , acquired by OLI (Operational Land Imager) on Landsat-8, shows Hubbard Glacier on July 22, 2014. The yellow lines indicate the location of the terminus on August 1, 1978, and on July 13, 2002. - The image of Figure 54 shows a close-up of the glacier's terminus on July 13, 2002. 74)

- According to Leigh Stearns, a glaciologist at the University of Kansas, Hubbard's advance is due to its large accumulation area; the glacier's catchment basin extends far into the Saint Elias Mountains. Snow that falls into the basin either melts or flows down to the terminus, causing Hubbard to steadily grow. In addition, Hubbard is building up a large moraine, shoveling sediment, rock, and other debris from Earth's surface onto the glacier's leading edge. The moraine at the front gives the glacier stability and allows it to advance more easily because the ice does not need to be as thick to stay grounded. - If it is thin, it can start floating and will not necessarily advance.

- Twice in the past hundred years — in 1986 and again in 2002 — the moraine has made contact with Gilbert Point and blocked the entrance to Russell Fjord. With nowhere to drain, runoff caused the water level in the fjord to rise rapidly. Water levels to rose 0.24 m per day. However, the closure was temporary, as water pressure overpowered the encroaching ice and debris and burst through the natural dam, returning the fjord to normal levels.

- In 2002, Leigh Stearns was attending a glaciology conference in nearby Yakutat, Alaska, a town that depends on Russell Fjord's marine life. "Understanding Hubbard's behavior is scientifically interesting," Stearns said, "but it also has immediate consequences for the town of Yakutat." — Those consequences provoked her to investigate what controls the terminus position and its advance, and to estimate when the fjord might become permanently blocked. The findings, recently accepted for publication in the Journal of Geophysical Research, explain how the mechanics at the terminus override the influence of other climate fluctuations. 75)

- One estimate suggests that the fjord could permanently close by 2025. But Hubbard's terminus is nearly 14 km wide, and does not advance at the same rate across its entire width. The region adjacent to Gilbert Point, where the closure would occur, advances more slowly, because seawater passing through the gap constantly erodes the ice. Based on the current rate of advance at the gap, Leigh Stearns estimated that closure could occur by 2043. Leigh Stearns cautions, however, that these closure dates are "projections based on our current observations, and should be viewed with skepticism."

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Figure 53: Advancement of Alaska's Hubbard Glacier into the Disenchantment Bay acquired on July 22, 2014 with the OLI instrument on Landsat-8 (image credit: NASA Earth Observatory, USGS, Joshua Stevens)

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Figure 54: Advancement of Alaska's Hubbard Glacier into the Disenchantment Bay acquired on July 13, 2002 with the ETM+ instrument on Landsat-7 (image credit: NASA Earth Observatory, USGS, Joshua Stevens)

• May 8, 2015: On March 6, 2015 the Landsat-8 TIRS (Thermal Infrared Sensor) switched from A side to B side electronics to resolve a problem with the A side encoder electronics. At that time, a plan was outlined for reprocessing data acquired since the problem began. 76)

- Beginning April 30, 2015, Landsat-8 scenes acquired from December 19, 2014 to March 13, 2015 began reprocessing to repopulate the TIRS data in the products. This calibration notice details the data changes during this timeframe. The reprocessing effort is expected to complete by May 18, 2015.

- Some TIRS data for a number of scenes will not be processed due to non-nominal instrument configuration. There were several intervals identified that the data was good for a portion of the interval, but where the SSM (Scene Select Mechanism) was commanded to rotate off nadir making the TIRS data unusable for the other portion of the interval. The reprocessing will process those where TIRS was well outside the field of view of OLI due to an off nadir SSM as OLI only products.

• April 16, 2015: Landsat-8 Thermal Data Reprocessing Update. On March 6, 2015 , USGS reported that the Landsat-8 TIRS (Thermal Infrared Sensor) resumed normal imaging operations, and outlined plans for reprocessing data acquired since December 19, 2014, when problems occurred in the A-side electronics of the sensor. Provisional TIRS data acquired since March 13, 2015 were expected to require reprocessing to refine the absolute calibration; however, based on a small number of vicarious measurements, it has been determined that these data will not need to be reprocessed for now since the radiometry appears to be consistent with the previous A-side data. The calibration will be monitored and updated in the future if needed. 77)

• April 12, 2015: March 2015 marked the 15th year that iceberg B-15 remained afloat around Antarctica... at least what's left of it. The iceberg first made its break from the Ross Ice Shelf in late March 2000. One of the largest icebergs ever observed, it measured about 270 km long and 40 km wide — almost as large as the state of Connecticut. 78)

- Fifteen years later, the U.S. National Ice Center (NIC) reported that eight fragments of the original berg remain (icebergs and fragments must measure at least 19 km long in order to be named and tracked by the center). According to NIC's weekly report from April 3, 2015, the largest surviving fragment was B-15T, which measured 52 km long and 13 km wide.

- The OLI (Operational Land Imager) on Landsat-8 acquired this natural-color image of B-15T on January 14, 2015 (Figure 55). The iceberg was located amid sea ice off the Princess Astrid Coast, just east of its position pictured in an image acquired April 5, 2015, by the MODIS (Moderate Resolution Imaging Spectroradiometer) on NASA's Terra satellite.

- Many icebergs get caught up in the currents circling Antarctica and then eventually spin off to the north and break up. However, icebergs that stay trapped in cool coastal waters can persist for decades.

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Figure 55: Iceberg B-15T still adrift; the image was acquired by OLI on January 14, 2015 (image credit: NASA Earth Observatory, Joshua Stevens, USGS)

• March 25, 2015: Landsat is a key data input for many products developed and used in water resources, agricultural monitoring, land use and land cover monitoring, forest management, and development planning. Yet, Landsat's contribution goes beyond its use solely as a primary data input. The products and processes developed by the Landsat program provide tools for data accuracy and visual communication improvement of other satellite imagery. These products are provided by the Landsat program at no cost to the users and have proven to be valuable assets to the private tech sector. This case study explores that various correction capabilities that Landsat offers to other satellite operators, acting as a tool in improvement of usability of other imagery. Several satellite companies and satellite imagery users are highlighted as beneficiaries of Landsat products.

- Landsat base layer: A base layer is a data set used as a reference for the display, analysis or correction of other data. Imagery, such as from Landsat and Landsat-like satellites is often used as a base layer. As a base layer, Landsat imagery is particularly helpful for verifying the accuracy of data due to the high quality of geometric correction and orthorectification of Landsat Level 1T data. Frank Warmerdam of Planet Labs, a company that designs, builds and operates a network of small satellites, notes that Landsat's high quality scenes provide a "trusted global basemap" for public and private sector use. Mapbox (Mapbox with HQ in Washington DC is one of the biggest providers of custom online maps for major websites) creates custom maps with open source tools and uses Landsat imagery as a basemap for a wide range of moderate resolution products. DigitalGlobe, a satellite imagery provider, is another user of Landsat imagery. As DigitalGlobe's data archive continues to grow, their use of Landsat imagery will likely be declining. However, the company used Landsat imagery to get started with their "DigitalGlobe Basemap" product series. Landsat data are also used by DigitalGlobe in parts of the world where either no high resolution data or no recent high resolution data is available.

- Processing satellite data using Landsat: A vast amount of processing is required to take the raw imagery collected by satellites and translate it into usable data for analysis. Spatial and spectral corrections are necessary in order to accurately locate the imagery on the earth's surface and convert pixel values to meaningful measures of the surface being captured. The Landsat program offers state of the art technology and methodology for conducting these corrections and as a result provides trusted remotely sensed data set for global coverage at the moderate resolution level. Due to Landsat's high standards and well documented approaches to spatial and spectral corrections, the correction and calibration of other satellite imagery can be achieved through the use of Landsat imagery.

- Spatial correction: Spatial correction, sometimes referred to as geo-rectification, geo-registration or spatial tie-down, involves accurately locating imagery on the earth's surface as well as correcting for distortions in the image through geometric correction and orthorectification. Geometric correction requires assigning known coordinates to points in the image, which geolocates the image to the correct place on the Earth's surface. Additionally, it corrects for large scale distortion or error inherent in image due to earth movement, earth curvature, and/or satellite movement during collection. Correcting small scale distortion is done through orthorectification, a type of geometric correction that utilizes digital elevation data to more accurately correct for distortions due to terrain.

The capability of Landsat to assist in spatial correction of other satellite data is utilized by companies such as DMCii, Planet Labs and BlackBridge. DMCii, a satellite imagery provider, uses Landsat data as a base layer for providing orthorectification for customers who do not have their own reference data, which is most of their customer base. This helps keep the costs down while improving the accuracy of DMCii's products. Planet Labs uses Landsat for the spatial tie down process everywhere around the globe for consistency and convenience. However, when processing difficult areas such as jungles and mountains, Landsat is a particularly critical resource. "Without Landsat it would be essentially impossible to get good GCP (Ground Control Point) locations from other sources," says Frank Warmerdam of Planet Labs. Similarly, BlackBridge, Canada's foremost supplier of satellite imagery products, is using Landsat imagery to perform geometric correction and orthorectification on areas in northern Africa and eastern Asia, where no better GCP's are available. BlackBridge has been using Landsat GCP since 2009 on about 750,000-800,000 out of 1 million products. The company will likely continue to use Landsat GCP's until higher accuracy GCP's become available for the rest of the landmass on Earth.

- Spectral corrections: Another important component of imagery processing is spectral correction. Spectral correction includes spectral characterization and radiometric calibration. Spectral characterization is a process of determining how different portions of the electromagnetic wavelength interact with an object. Radiometric calibration is a broader term for the process of relating values recorded (sensed) by a satellite sensor with radiance values (what was actually reflected/emitted on the ground). Calibration can either be absolute or relative and can employ different methods depending on when in the process the calibration is being performed.

For radiometric calibration, cross-calibration and surface reflectance are the principle processes used to correct satellite imagery. Cross-calibration is a form of relative calibration which compares values collected by one satellite sensor against values collected by a second satellite sensor. If one of the sensors has had corrections applied, the corrected values can be used for relative radiometric calibration of the other sensor. This method can be a cost effective method for radiometric calibration of operational satellites (Liu et al. 2004). DMCii initially used Landsat 7 as a standard reference satellite for cross-calibration over Libya and Antarctica. The company is now working on incorporating Landsat 8 data, which produces a good long term baseline for cross-calibration. In the future, DMCii anticipates incorporating data from the European Union Sentinel-2 mission into their tool kit.

For certain analyses of satellite imagery, it is necessary to calibrate for surface reflectance, which is the ratio of solar energy that is reflected rather than absorbed by an object on the earth's surface. However, the reflected energy must first pass back through the atmosphere before it is detected by the satellite sensor. As a result, corrections must be applied to remove atmospheric effects. Landsat currently uses a set of algorithms known as LEDAPS (Landsat Ecosystem Disturbance Adaptive Processing System ). This type of radiometric calibration uses auxiliary data including ozone, water vapor, geopotential height, and aerosol optical thickness to calculate surface reflectance. Once these corrections are applied, surface reflectance products represent what radiance values would be under ideal conditions (i.e., no atmospheric effects). Many higher-level surface geospatial analyses—including vegetation indexes, albedo, LAI (Leaf Area Index), burned area, land cover, and land cover change – rely on surface reflectance products. The availability of these products in the private sector is highly valued by the users.

Mapbox reports that of all the atmospheric correction methods tested by them, LEDAPS is the one they trust most. "LEDAPS allows types of comparison and analyses that would otherwise require significant single-purpose investments. We estimate that LEDAPS has saved our company approximately $250,000 in technical and opportunity costs over the last 24 months, and that number is steadily increasing," says Charlie Loyd, a member of Mapbox's satellite team. "Other than its accuracy, the key to LEDAPS's value to us is its generality. We can apply LEDAPS to a Landsat scene once and have output that's equally appropriate for true color imagery, classification algorithms, NDVI (Normalized Difference Vegetation Index), and other indexes. We recommend it broadly, knowing that it will meet virtually any user's needs for atmospheric correction. For these reasons, LEDAPS is the only non–in-house component of our Landsat software pipelines that we consider effectively irreplaceable."

DigitalGlobe is another company that has accrued benefits through LEDAPS. The company sees Landsat as a key input in the development process of their own surface reflectance product. In 2013, DigitalGlobe introduced a new product, seamless country-scale orthomosaics at half meter resolution, using Landsat for surface reflectance to bootstrap to a base map. DigitalGlobe reports that having Landsat data available accelerated the introduction of the product by about 9 months. While the company does not break out revenue by product in their public reporting, this particular product represents multiple millions of dollars of revenue annually.

- Additional benefits: In addition to the more specific spatial and spectral correction benefits discussed above, the Landsat program as a whole offers additional benefits to the private satellite imagery community. According to Frank Warmerdam of Planet Labs, the Landsat archive acts as a common reference point among datasets, where the well-published and respected nature of Landsat products provides a reference for product to product relation. Along with Landsat's long continuous data record and exceptional technical integrity, it is made indispensable by its free distribution (Charlie Loyd, Mapbox, written communications, 2014). The free and open access to the Landsat current and archived data enables private companies to improve their products at minimal cost, while developing additional products to push to the market.

Table 4: The impact of the Landsat program data in direct and complementary uses of Landsat imagery in various non-government programs 79)

• March 20, 2015: Ulan Bator, Mongolia, is featured in Figure 56. Sitting in the valley of the Tuul River – running northeast to southwest across the image – the city is flanked by the Bogd Khan Mountain to its south (center of image). This forested mountain is the site of one of the oldest national parks in the world, home to wildlife such as foxes and wolves and endangered species of hare and deer. 80)

- South of the mountain, a light covering of clouds blanket the steppe eco-region. This is part of the greater Eurasian Steppe, stretching from Moldova through Siberia, characterized by grasslands, savannahs and shrublands. The area pictured is also part of the discontinuous permafrost zone, meaning that in some areas the ground is frozen year round, while other areas thaw for weeks or months at a time. This poses a challenge for building, so many suburban residents of Ulan Bator live in traditional dwellings that are built on top of the soil. These circular houses – called yurts – are traditionally made from steam-bent wood and covered in layers of fabric for insulation.

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Figure 56: The Mongolian capital of Ulan Bator is captured in this image from the Landsat-8 satellite, acquired on Feb. 19, 2015 (image credit: USGS, ESA)

• March 6, 2015: The Landsat-8 TIRS (Thermal Infrared Sensor) resumed normal imaging operations on March 4, 2015, and nominal blackbody and deep space calibration data collection will resume on March 7, 2015. 81)

- Since the current anomaly associated with the scene select mirror encoder electronics forced a suspension of TIRS Level-1 data processing on December 19, 2014, an exhaustive study has been conducted to determine the root cause of the anomaly and to develop plans for reconfiguring the instrument for a return to nominal operations. During this time TIRS data continued to be collected and archived, although Level-1 processing was suspended.

- On March 2, 2015, the TIRS mechanism control electronics (MCE) were swapped to the redundant side ("side-B") and TIRS data collection resumed on March 4, 2015. It will still take several weeks of commissioning the instrument with the side-B MCE and obtaining sufficient calibration data to resume Level-1 processing. Likewise, TIRS data collected December 19, 2014 through March 1, 2015 will require updated calibration parameters before these data can be processed to generate Level-1 products.

- On March 13, 2015, processing of Landsat-8 TIRS data resumed. The newly processed data includes the revised Calibration Parameter Files established after the mechanism control electronics (MCE) swap on March 2, 2015. Investigations are continuing to improve the data product, and reprocessing of TIRS data acquired since December 19, 2014 is still planned. 82)

• March 6, 2015: The oceans may be vast, yet they still can grow crowded. Some congested areas have enough ship traffic that the IMO (International Maritime Organization) and other groups maintain traffic separation schemes—the equivalent of highways for ships—to reduce the risk of collisions. - The vessels in Figure 57 are most likely cargo ships, though some may be ferries or fishing boats. The ships appear as small gray and white specks. In the shallow coastal waters, their propellers kick up long, brown sediment plumes. Most of the northbound ships make a turn to the northwest as they round the tip of Shandong. 83)

- According to the IMO, the practice of following predetermined routes for shipping originated in 1898. It was first adopted by shipping companies operating passenger ships across the North Atlantic. Since then, traffic separation schemes have been established in most congested areas, causing the number of ship collisions and groundings to drop dramatically.

- But with upwards of 86,000 merchant ships on the world's oceans, accidents still happen. On May 2, 2010, the Bright Century, a cargo ship loaded with 170,000 tons of iron ore, sank after it collided with a freighter about 37 km east of Shandong Peninsula. In December 2012, a fishing boat collided with a cargo ship near the peninsula and sank with 11 fisherman on board.

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Figure 57: OLI on Landsat-8 captured this view of northbound and southbound shipping lanes off the coast of China's Shandong Peninsula on February 24, 2015. The lanes form one of the main routes from the Yellow Sea into the Bohai Sea and the Chinese ports of Dalian and Tianjin, two of the busiest in the world. As shown by this map, several lanes of traffic intersect northeast of the Shandong Peninsula (image credit: USGS, NASA Earth Observatory, Jesse Allen)

• The image of Figure 58 was released on Feb. 13, 2015 in ESA's 'Earth from Space video program.' Las Vegas with its grid-like urban plan is visible near the center. Sitting in a basin of the Mojave Desert, the city is surrounded by a number of mountain ranges. 84)

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Figure 58: Image of Las Vegas and Lake Mead acquired with OLI of Landsat-8 on Sept. 23, 2014 (image credit: USGS, ESA)

Legend to Figure 58: Zooming in southeast of the city one can see large, dark shapes in one of the desert valleys. These are solar panels of a large-scale plant called Nevada Solar One. The plant harnesses enough energy to power an estimated 14 000 homes a year. - Satellites can assist in the selection of sites of solar power plants by providing sunshine maps that combine information on overall solar irradiance and average cloudiness. Along with other space-derived products such as digital elevation models, this can help sustainable energy companies pinpoint areas best suited for exploiting solar energy.

The large dark area pictured is Lake Mead, the largest reservoir in the country. It primary source is the Colorado River, flowing in from the east and out to the south. This massive reservoir was established in the early 1930s by the construction of the Hoover Dam on the Colorado River. Drought and increased water demand in recent years have resulted in a decline in water levels, hitting record lows last summer. The lake and surrounding area form the Lake Mead National Recreation Area, where visitors can go boating, swimming, fishing, hiking, biking and camping.

• January 27, 2015: Authorities in the Indian states of Jammu and Kashmir are concerned that a landslide blocking the Tsarap River (also called the Phuktal River) may lead to a damaging flood downstream. 85)

- The landslide, which reportedly occurred on December 31, 2014, sent enough fine-grained debris into the river to create an earthen dam. As of January 20, 2015, that dam was about 600 m long, according to an analysis of satellite imagery collected by the ISRO's (Indian Space Research Organization) CartoSat-2. The artificial lake that formed behind the dam was nearly 8 km long and covered about 55 hectares (300 acres). Aerial surveys suggest the mound of debris blocking the river was about 60 m high.

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Figure 59: Landslide in Northern India: OLI acquired this image of landslide debris and the barrier lake on January 18, 2015 (image credit: NASA Earth Observatory, Joshua Stevens, USGS)

Legend to Figure 59: Notice that the river appears wider upstream of the landslide. The river also appears brighter after the landslide because the surface has frozen and a fresh coat of snow coats the ice. After surveying the situation on January 18, a team of civilian and military engineers recommended that people who live downstream move to higher ground. They also discouraged authorities from using explosives to clear the blockage as doing so could trigger additional landslides. While the chance of a catastrophic flood is lower with the lake frozen, the risk will increase when temperatures rise in the spring. As a precautionary measure, authorities have closed the Chadar ice trek, a popular route that involves hiking on frozen river ice downstream of the blockage.

• January 27, 2015: The Landsat-8 spacecraft is operating nominally and acquires OLI and TIRS data. However, the TIRS level-1 processing remains suspended. 86) 87)

- On December 19, 2014, the TIRS instrument on Landsat-8 was reconfigured due to detection of anomalous current levels associated with the scene select mirror encoder electronics. Since that time substantial testing has been conducted to isolate the root cause of the problem and to evaluate options for returning to routine operations. - During this time, TIRS data has been routinely collected with OLI data, but due to the lack of definitive calibration coefficients the processing of TIRS data to level-1 products has been suspended.

- Once a plan to return to normal operations has been defined, the backlog of TIRS data that have been collected will be processed to Level-1 products. Updates to the status of plans to resume normal operations will be provided periodically.

• The Landsat-8 image of Figure 60 was released on Jan. 30, 2015 showing Corsica, the most mountainous island of the Mediterranean Sea. About 40% of the island's surface area is dedicated to nature reserves, and its mountains are a popular destination for hiking. For beachgoers, the island boasts over 1000 km of coastline. 88)

- Near the northeastern coast, one can see the island's largest coastal lagoon, the Etang de Biguglia. This nature reserve has been noted for its support of numerous breeding and wintering waterbirds, as well as the vulnerable Hermann's tortoise and long-fingered bat.

- This lagoon is one of the over 2000 sites worldwide considered to be wetlands of international importance by the Ramsar Convention, an intergovernmental treaty for the sustainable use of wetlands. The World Wetlands Day is observed on 2 February, the anniversary of the signing of the Convention.

- ESA has been assisting the Ramsar Convention for a decade through the GlobWetland project, which provides satellite data to be used to monitor these precious resources. The next phase of the project, called GlobWetland Africa, will collaborate closely with ESA's TIGER initiative, which trains African water authorities and researchers in exploiting satellite data and Earth observation technology for sustainable water resource management.

- The Etang de Biguglia is not the island's only Ramsar site: further inland in the central-north part of the island is an active raised bog, home to a number of protected bat, reptile, bird and amphibian species.

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Figure 60: Image of the island of Corsica, acquired by Landsat-8 on August 29, 2014 (image credit: USGS, ESA)

• The Landsat-8 image of Figure 61 was released by ESA on January 23, 2015 showing parts of the Swiss and Italian Alps as well as the Aletsch Glacier, the largest glacier in the Alps located in the center of the image. The glacier originates in a large, flat area of snow and ice high in the mountains called Concordia, where three smaller glaciers converge. It extends south, and its meltwater creates the Massa River in the valley below. Owing to climate change, the glaciers in this region are showing long-term retreat. The melting ice has given birth to new lakes, which pose risks such as flooding and landslides to communities at lower level. 89)

- Aletsch and the surrounding mountains are part of the Jungfrau-Aletsch protected area, a UNESCO World Heritage site. The area is of major importance to scientific research in geology, geomorphology, climatic change, biology and atmospheric physics. It features a wide diversity of ecosystems, and its landscape has played an important role in European art, literature, mountaineering and alpine tourism.

- One particularly popular tourist destination is the Swiss city of Interlaken, located between lakes Thun and Brienz, seen in the upper part of the image. From the city, locals and visitors alike have easy access to the mountains and water bodies, and can partake in a variety of outdoor activities during all seasons.

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Figure 61: Image of the Aletsch Glacier in the Swiss and Italian Alps, acquired by OLI of Landsat-8 on June 8, 2014 (image credit: USGS, ESA)

• 2014 update: Landsat imagery provides the United States and the world with continuous, consistent inventory and monitoring of critically important global resources. Supplying an unprecedented record of global land cover status and change for over 40 years, Landsat imagery is an essential "national asset" which has made and continues to make critical "contributions to U.S. economic, environmental, and national security interests." Because Landsat imagery is used most often by governmental and other non-commercial entities, the general lack of market forces makes estimating the economic value of Landsat data challenging. However, cost savings from operational efficiency improvements, avoided alternative replacement costs (assuming Landsat data were not available), and opportunity costs related to economic and environmental decision-support can be used to estimate the value of Landsat data. 90)

• Dec. 19, 2014: A small portion of underwater structures of the Great Bahama Bank is pictured in Figure 62. Sitting north of Cuba, the bank is made of limestone – mainly from the skeletal fragments of marine organisms – that has been accumulating for over 100 million years. Currents sculpted these underwater sediments into the wavy pattern we see along the bottom of the image, just a few meters deep. 91)

The shallow waters drop off into the deep, dark water of an area known as the Tongue of the Ocean. With depths of up to about 4000 m, this trench surrounded by islands, reefs and shoals has an opening to the Atlantic Ocean at its northern end. The trench was carved during the last Ice Age when the land was still above sea level and exposed to erosion from draining rainwater. As the Ice Age ended and the massive ice sheets across the globe melted, global sea levels rose and flooded the canyon.

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Figure 62: An image of the Great Bahama Bank acquired with Landsat-8 on Feb. 5, 2014 and released on Dec. 19, 2014 (image credit: USGS, ESA)

The MODIS image of the Great Bahama Bank of Figure 63 provides a much broader view of the area, including the Tongue of the Ocean. 92)

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Figure 63: A MODIS image on Aqua of the Great Bahama Bank captured on Jan. 9, 2014 (image credit: NASA Earth Observatory)

• Nov. 7, 2014: The Landsat-8 image of Figure 64 was released by ESA on Nov. 07, 2014. It shows part of the Middle East, with the Jordan Rift Valley running north to south. The most prominent feature in this image is the Dead Sea: the lowest point on Earth's surface, with 427 m below sea level. The Dead Sea is 50 km long, 15 km wide at its widest point, and 306 m deep. The extremely high salinity (34.2%) means fish cannot live in this water body, although there are bacteria and fungi. 93)

- With the Jordan River as its main source of water, the Dead Sea is an ‘endorheic' basin, meaning that the water has no outflow. Nonetheless, the water level has been dropping, an effect of the diversion of incoming water from the river.

- Note the greenish rectangles just south of the Sea. This is a large complex of mineral evaporation ponds used to produce sodium chloride and other salts for the chemical industry and human and animal consumption. These ponds are separated from the northern part of the Dead Sea by what once was the Lisan Peninsula but lowering water levels have exposed the sea bed, dividing the two sections completely.

- Throughout the rest of the image one can see built-up areas such as Amman, the capital of Jordan, in the upper right and Jerusalem near the green hills west of the Dead Sea. Further west we can see green patches of agriculture on the coastal plain, with Tel Aviv on the Mediterranean coast. - In the lower-left corner of the image, one can clearly see the division between Israel and the Gaza Strip not only by the outline of the border, but in the difference in agricultural practices.

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Figure 64: Landsat-8 image of the Dead Sea, acquired on July 4, 2013 with OLI (image credit: USGS, ESA)

• Oct. 26, 2014: On October 17, 2014, the eye of category 3 Hurricane Gonzalo passed right over Bermuda. The storm knocked out power to most of the island and caused between $200–$400 million in property damage, though it did not cause any deaths. The potent storm also stirred up the sediments in the shallow bays and lagoons around Bermuda, spreading a huge mass of sediment across the North Atlantic Ocean. 94)

OLI ( Operational Land Imager) on the Landsat 8 satellite acquired the two natural-color views of Bermuda in Figures 65 and 66. After the storm, visible plumes of sediment stretch 25 to 30 km from Bermuda. They extend mostly to the south and east of the island, suggesting that the last winds from the storm may have been out of the northwest. The suspended sediments were likely a combination of beach sand and carbonate sediments from around the shallows and reefs.

Coral reef and carbonate island environments—Bermuda is a classic example—produce large amounts of calcium carbonate (CaCO3) mainly in the form of aragonite and magnesian calcite. One island can produce as much CaCO3 as several hundred square kilometers of open ocean. But unlike the calcium carbonate produced in the open ocean by coccolithophores, foraminifera, and pteropods, the sediment produced by reefs stays on the reef flats (where there is coralline algae that also produces carbonate). It builds up over time and forms islands.

These stores of calcium carbonate sediments can get moved from the shallows to the deep ocean by storms or density flows. The strong winds of storms like Gonzalo can move a large amount of sediment off the shallow islands in a single event. Density flows can happen when the shallow water on the reef flat is cooled by a weather system, making it more dense than the surrounding ocean water, and it sinks to the deep ocean, taking sediments with it.

Storm-induced export of carbonate sediments into the deep ocean—where they mostly dissolve—is a significant process in the ocean's carbonate and carbon cycles. It's also important for the eventual neutralization of excess carbon dioxide entering the oceans because of increasing atmospheric CO2 concentrations from fossil fuel combustion. The dissolution of calcium carbonate is an important process in the carbon cycle; it is one of the ways that the oceans naturally balance the addition of carbon dioxide to ocean waters. However, as more CO2 is added to the surface waters due to rising atmospheric concentrations, it is becoming increasingly difficult for coral and coralline algae to make calcium carbonate.

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Figure 65: OLI image of Bermuda acquired on Oct. 2, 2014 (image credit: NASA, Earth Observatory)

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Figure 66: OLI image of Bermuda, acquired on Oct. 18, 2014, a day after Gonzalo passed through (image credit: NASA, Earth Observatory)

• Sept. 29, 2014: The greens and blues of the ocean color from NASA satellite data (Figure 67) have provided new insights into how climate and ecosystem processes affect the growth cycles of phytoplankton—microscopic aquatic plants important for fish populations and Earth's carbon cycle. 95)

Climate change will unquestionably influence global ocean plankton because it directly impacts both the availability of growth-limiting resources and the ecological processes governing biomass distributions and annual cycles. Forecasting this change demands recognition of the vital, yet counterintuitive, attributes of the plankton world. The biomass of photosynthetic phytoplankton, for example, is not proportional to their division rate. Perhaps more surprising, physical processes (such as deep vertical mixing) can actually trigger an accumulation in phytoplankton while simultaneously decreasing their division rates. These behaviors emerge because changes in phytoplankton division rates are paralleled by proportional changes in grazing, viral attack and other loss rates. Here, the trophic dance between predators and prey is discussed, how it dictates when phytoplankton biomass remains constant or achieves massive blooms, and how it can determine even the sign of change in ocean ecosystems under a warming climate. 96)

At the bottom of the ocean's food chain, phytoplankton account for roughly half of the net photosynthesis on Earth. Their photosynthesis consumes carbon dioxide and plays a key role in transferring carbon from the atmosphere to the ocean. Unlike the plant ecosystems on land, the amount of phytoplankton in the ocean is always followed closely by the abundance of organisms that eat phytoplankton, creating a perpetual dance between predators and prey. This new analysis shows how tiny imbalances in this predator-prey relationship, caused by environmental variability, give rise to massive phytoplankton blooms, having huge impacts on ocean productivity, fisheries and carbon cycling.

The continuous year-in and year-out measurements provided by NASA's ocean color satellites have dramatically changed our understanding of phytoplankton dynamics on the Earth.

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Figure 67: Landsat-8 image of a phytoplankton bloom (green and blue swirls) near the Pribilof Islands off the coast of Alaska, in the Bering Sea, acquired on Sept. 22, 2014 (image credit: NASA/GSFC, USGS)

In early September 2014, the SLI (Sustainable Land Imaging) Office at NASA/GSFC announced, that the following companies have been awarded contracts to carry out the SLI 'Reduced Instrument Envelope Study'.

• BATC (Ball Aerospace & Technologies Corporation) of Boulder, CO

• Exelis Inc., Geospatial Systems of Fort Wayne, IN

• Lockheed Martin Space Systems Company of Greenbelt, MD

• Northrop Grumman Systems Corporation, Aerospace Systems of Redondo Beach, CA

• Raytheon Company of El Segundo, CA

• SST-US (Surrey Satellite Technology US LLC) of Englewood, CO.

The study focuses on investigating mid-term capabilities and technologies for instruments that may enable more efficient implementation of the SLI program objectives to continue the Landsat heritage of measurements. The study contract awards are intended to enable contractors to perform a more detailed analysis of techniques and trends that lead to reduction in size and mass of spaceborne Earth-imaging instruments, potentially resulting in cost savings to the U.S. Government while still meeting the SLI program objectives. These studies will be of 6-month duration.

Landsat data provide a consistent and reliable foundation for research on land use change, forest health, and carbon inventories, and changes to our environment, climate, and natural resources. Additionally, the free and open availability of the Landsat data enables the measurements to be used routinely by decision makers both inside and outside the Government, for a wide range of natural resource issues, including water resource management, wildfire response, agricultural productivity, rangeland management, and the effects of climate change.

The U.S. Government has committed to continue the Landsat program and its invaluable data stream. To continue data collection beyond Landsat-8, the Administration proposes to design and implement a spaceborne system to provide global, continuous Landsat-quality multispectral and thermal infrared measurements for at least the next 25 years. The satellite system may be combined with alternative sources for Landsat-quality data, either procured through commercial approaches or through partnership agreements, as they become available. In accordance with Administration objectives, NASA will lead the system design study in close collaboration with the USGS (United States Geological Survey) and be informed by existing knowledge of current and desired capabilities. The aim of the study will be to define a programmatically sustainable system that balances measurement capability, likelihood of data continuity (minimizing risks of gaps to the extent possible), and cost/affordability over the lifetime of the program. Technology infusion over the lifetime of the program will be considered as a feature of the long-term sustainable program.

In FY 2014, NASA will initiate the definition of a sustained, spaceborne, global land imaging capability for the nation, ensuring continuity following LDCM. Near-term activities led by NASA, in cooperation with USGS, will focus on studies to define the scope, measurement approaches, cost, and risk of a viable long-term land imaging system that will achieve national objectives. Evaluations and design activities will include consideration of stand-alone new instruments and satellites, as well as potential international partnerships. It is expected that NASA will support the overall system design, flight system implementation, and launch of future missions, while USGS will continue to fund ground system development, post-launch operations, and data processing, archiving, and distribution.

Table 5: Sustainable Land Imaging Architecture Study 97)

• August 27, 2014: Ethiopia's Danakil Depression (or Afar Depression) exhibits some uncommon wonders: lava that burns blue, bright yellow hot springs, and lakes of bubbling mud. These otherworldly oddities are all manifestations of a tectonic process called continental rifting. In other words, the Earth is pulling apart at the seams here. 98)

In northeastern Africa, the Arabian, Somali, and Nubian (or African) plates are separating, thinning Earth's crust as they pull apart. The Danakil Depression lies between the Danakil Alps (east) and the Ethiopian Plateau (west), which were once joined until the rifting process tore them apart. The land surface is slowly sinking, and Danakil Depression will someday fill with water as a new ocean or great lake is born. But for now, the region is full of other interesting liquids.

The image (Figure 68) shows a few of the diverse and compelling features of the Danakil Depression. Chief among them is Gada Ale, the northernmost volcano in the Erta Ale volcanic range. Gada Ale is a 287 m stratovolcano built of lava and ash, and it has a crater lake full of boiling mud and sulfurous gases. Basalt lava from the volcano paints the surrounding terrain a dark hue, with the youngest flows being the darkest colors in the satellite image.

Just southwest of Gada Ale, a 2 km wide salt dome has pushed ancient lava flows up to heights of 100 m. North of Gada Ale, a salt lake (Lake Karum) lies 116 m below sea level. To the south lies the Catherine Volcano, a 120 m circular shield surrounded by a tuff ring (an amalgamation of volcanic ash). With gently sloping sides of basaltic lava, the volcano has been dated at less than one million years old. In the center of that tuff ring is a small, salty lake fed by thermal springs.

The Afar people have survived in this unforgiving region for at least 2,000 years, mining and selling the plain's abundant salt, which was once used as currency in Ethiopia. The harsh desert also has created an ideal exposure for the tectonic rifting—a process that often occurs on the recesses of the ocean seafloor or elsewhere on land where younger sedimentary rocks obscure the geologic record.

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Figure 68: The image of the Danakil Depression was acquired on June 27, 2014 with OLI on Landsat-8 (image credit: NASA Earth Observatory, Jesse Allen and Robert Simmon)

• August 20, 2014: Retreat of the Yakutat Glacier in Alaska. Natural processes and human-caused warming have combined to bring rapid change to a glacier in southeastern Alaska. The Yakutat Glacier is one of the fastest retreating glaciers in the world. It is the primary outlet for the 810 km2 Yakutat ice field, which drains into Harlequin Lake and, ultimately, the Gulf of Alaska. 99)

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Figure 69: Image of the Yakutat Glacier acquired with the OLI instrument of Landsat-8 on August 13, 2014 (image credit: NASA, USGS Earth Observatory)

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Figure 70: Image of the Yakutat Glacier acquired with the TM instrument on Landsat-5 on August 22, 1987 (image credit: NASA, USGS Earth Observatory)

Legend to Figures 69 and 70: Landsat satellites captured this pair of images showing changes in the glacier and lake. The TM (Thematic Mapper) on Landsat -5 acquired the image of Figure 70 on August 22, 1987; the OLI (Operational Land Imager) on Landsat-8 captured the image of Figure 69 on August 13, 2013. Snow and ice appear white and forests are green. The brown streaks on the glaciers are lateral and medial moraines.

Over the past 26 years, the glacier's terminus has retreated more than 5 km. What is causing the rapid retreat? University of Alaska glaciologist Martin Truffer and colleagues pointed to a number of factors in their 2013 study published in the Journal of Glaciology. The chief cause is the long-term contraction of the Yakutat Ice Field, which has been shrinking since the height of the Little Ice Age. - Once part of a much larger ice field, Yakutat has been contracting for hundreds of years. As other nearby glaciers retreated, Yakutat ice field was cut off from higher-elevation areas that once supplied a steady flow of ice from the north. With that flow cut off, there simply is not enough snow falling over the low-elevation Yakutat ice field to prevent it from retreating.

Beyond this natural change, human-caused global warming has hastened the speed of the retreat. Between 1948–2000, mean annual temperatures in Yakutat increased by 1.38°Celsius . Between 2000 and 2010, they rose by another 0.48°C . The warmer temperatures encourage melting and sublimation at all ice surfaces exposed to the air.

In the past few years, the breakdown of a long, floating ice tongue has triggered especially dramatic changes in the terminus of the Yakutat glacier. For many years, Yakutat's two main tributaries merged and formed a 5 km calving face that extended far into Harlequin Lake. In the past decade, satellites observed a rapid terminus retreat and the breakup of the ice tongue in 2010. As a result, the calving front divided into two sections, with one running east-west and another running north-south.

• July 2, 2014: The Landsat 8 satellite is helping researchers to spot organisms like aquatic algae from space, gathering information that could direct beachgoers away from contaminated bays and beaches. With improved sensors and technology on the latest Landsat satellite, researchers can now distinguish slight variations in the color of coastal water due to algae or sediments to identify potential problem areas. 100)

- The OLI (Operational Land Imager) on Landsat-8 added the "New Deep Blue" band (433-453 nm) to pick up dark blue colors to help studies that are looking at coastal areas, both of lakes and oceans. Pollutants from land impact fresh and salt-water ecosystems including coral reefs.

Beyond the blue of the water, a study of John Schott of RIT (Rochester Institute of Technology) at the University of Rochester, N. Y. is paying attention to three colors to decipher what's in Lake Ontario: green, yellow and grey. Green indicates the presence of chlorophyll, the molecule found not only in land plants but in lake algae. The yellow color is decaying plant matter. The gray color, apparent from a combination of Landsat's bands, comes from particulates like dust and soil, or from dead algae that have lost their chlorophyll.

- After paddling out in boats and testing the waters on the same day Landsat 8 passed overhead, the team compared the water samples to the satellite data. The comparisons are used to create tables and computer programs that can use the satellite data to help determine water quality and composition. This summer, the team plans to sample the harmful algal blooms that start small in Lake Ontario's bays and rivers – which could grow and cause water quality and public health concerns.

- It's not just the algae floating on the surface that Landsat-8 can spy. Scientists with the Michigan Tech Research Institute are tracking the spread of Chladophora, a hair-fine algae that attaches to shallow water rocks, or the shells of dead invasive zebra and quagga mussels. Occasionally, due to storms in the Great Lakes, the algae slough off the rocks, and cover the beach in a green decaying mess.

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Figure 71: Landsat 8's new blue band and improved ability to distinguish subtle color variations help researchers study coastal water quality. John Schott and colleagues at RIT are measuring chlorophyll and more along Lake Ontario's shores. (image credit: RIT, NASA, USGS)

- Sediment swirls and chlorophyll whirls: Researchers in Belgium are looking at the swirls of sediments created by wind turbines in near shore waters. Wind companies are required to monitor the environmental impact of the turbines, explained Quinten Vanhellemont, a scientist with the Royal Belgian Institute for Natural Sciences, and Landsat-8 has the spatial resolution – and the sensitivity – to pick up some of the smaller features. The resolution and sensitivity of the imager on board makes it an ideal sensor for coastal water applications.

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Figure 72: Close-up of the Thames Estuary wind power turbines of the London Array and sediment swirls on the coast of England (image credit: NASA, USGS)

- At NASA/GSFC, scientists in the Ocean Ecology Laboratory are looking to Landsat-8 as well. They have created open-source software that researchers use to analyze satellite data for studies on marine phytoplankton chlorophyll concentration other water constituents. Currently they use satellites that take the big view, but Bryan Franz, a research oceanographer at Goddard, worked with Vanhellemont to see how well Landsat 8 data could be processed to determine chlorophyll patterns in coastal waters. It shows promise, Franz said. He plans to add Landsat 8 data to the software this summer to see how the ocean research community will use it (Ref. 100).

• The image of Figure 73 was released on July 1, 2014 in NASA's Earth Observatory program. Turkmenistan is a desert country that lies east of the Caspian Sea and borders Iran, Afghanistan, Uzbekistan, and Kazakhstan. Annual precipitation across the country ranges from 300 mm in the mountains to 80 mm in the desert northwest. In a country just larger than California, nearly 80% of the land is defined as desert. 101)

Despite the harsh, dry environment, these desert expanses were once part of the ancient east-west trade route between the Roman Republic and Han Dynasty. As part of the Silk Road from Europe to China, Turkmenistan was coveted by the Mongols, Turks, and Russians. Each have taken turns ruling the region over the past two millennia.

In the 18th century, people in the region started to think about taming the great Garagum Desert (also known as the Kara Kum or Karakumsky). The idea was to bring water southward from the Amu-Darya River, which runs through the northern desert. Under Soviet rule in the 1950s, the idea was realized. - In a feat of engineering, water from the Amu-Darya was channeled more than 1,300 km to irrigate the southern lands. Under construction from 1954 to 1988, the Garagum Canal project opened a million hectares of land to farming of cotton, wheat, melons, and animal fodder. Today, agriculture accounts for 7% of Turkmenistan's gross domestic product and employs nearly half of the country's workforce.

In this natural-color Landsat-8 image acquired on April 18, 2014, the Hanhowuz (Khauzkhan) Reservoir jumps out as a splash of turquoise amidst desert browns. The reservoir was constructed in a natural depression to capture winter runoff and overflow from the canal for use later during the driest periods of summer. Phytoplankton thrive in the warm waters, as do many commercial fish—including Aral barbel, asp, and catfish.

In the image of Figure 73, Garagum Canal is the brown ribbon dropping down from the upper right corner and heading south and east from the reservoir. A portion of the canal is diverted, and one can see the brown sediment-laden water entering the reservoir from the east and dropping its load of suspended sediments. The water that leaves is turquoise and travels west to irrigate the Tedjen Oasis. Roughly rectilinear farmlands appear on either side of that section of the canal.

Water is vital to the existence of Turkmenistan, but the canal that started as an engineering wonder for arid lands has also turned out to be an environmental tragedy. The canal has starved the Aral Sea, which has lost about 90% of its water since the canal's creation. Making matters worse, inefficient earthen canals lose nearly half of the canal water between the Aral Sea and Turkmenistan's farms. Smudges of green-grey along their sides of the canal (north of the reservoir) show where water has seeped out.

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Figure 73: Image of the Hanhowuz Reservoir in Turkmenistan, acquired by the OLI instrument of Landsat-8 on April 18, 2014 (image credit: USGS, NASA)

• The image of Figure 74 was released on June 18, 2014 in NASA's Earth Observatory program. The double oxbow — called the loop — is known as an "entrenched meander" by geologists, the Loop's canyon walls are about 150 m high. The lower canyon walls are part of the Hermosa Formation, a group of sedimentary rock layers that formed about 300 million years ago. At the narrowest point, just 150 m of rock separate the channels of the East Loop; the slightly wider neck of the West Loop measures about 520 m. As the Colorado River continues to erode the canyon wall, it will eventually punch through and create a new channel, leaving an oxbow lake and later a rincon. 102)

How the entrenched meanders of the Colorado River (and other rivers that flow through the Colorado Plateau) formed has intrigued western geologists since a team of explorers led by John Wesley Powell passed through the Canyonlands region in 1869. Were meanders established by ancestral rivers that flowed on softer sediments long before tectonic forces uplifted the Colorado Plateau? Or was it something about the rock of the Colorado Plateau that determined the shape and distribution of oxbows and meanders?

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Figure 74: The double oxbow loop of the Colorado river in Canyonlands National Park acquired by OLI of Landsat-8 on May 13, 2014 (image credit: USGS, NASA)

• Figure 75 is a Landsat-8 image of the southwestern coast of Greenland, released by ESA on June 13, 2014. Multiple ice streams, that drain the Greenland ice sheet, are pictured in this satellite image. 103)

- Covering more than 2,000,000 km2, Greenland is the world's largest island and home to the second largest ice sheet after Antarctica. Scientists, using data from Earth-observing satellites, have discovered that the rate of ice sheet melting is increasing. Between 1992 and 2012, Greenland was responsible for adding about 7 mm to the average global sea level. Many areas in Greenland – especially along the coast – are losing up to 1 m of ice thickness per year.

- Melting ice sheets, caused by rising temperatures and the subsequent rising of sea levels, is a devastating consequence of climate change, especially for low-lying coastal areas. In addition, the increased influx of freshwater into oceans affects the salinity, which in turn impacts global ocean currents – a major player in the regulation of our climate.

- Monitoring the effects of climate change on the cryosphere and oceans using Earth-observing satellites is the main topic of an event which took place on June 13, 2014 at the Royal Society in London. The event was jointly organized by ESA and the UK Space Agency (UKSA) with the aim to provide an overview of the outcome and scientific significance of the achievements of the Climate Change Initiative with a focus on new climate data sets from space, the cryosphere and the ocean.

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Figure 75: Image of the southwestern coat of Greenland acquired by OLI (Operational Land Imager) of Landsat-8 on June 12, 2013 (image credit: USGS, ESA)

Legend to Figure 75: In the lower part of the image, one can see icebergs speckling the waters of a fjord, with the mountainous Nuussuaq Peninsula visible along the bottom of the image.

• June 2014: During the early on-orbit period of Landsat-8, ground-based measurements indicated a significant error in the radiance produced from TIRS (Thermal Infrared Sensor), especially in Band 11. 104)
After several months of analysis, the NASA TIRS team suspected a stray light issue with the sensor. Scans of the moon by the TIRS instrument have confirmed this. Users were notified of the issue through the Landsat-8 Calibration Notices (https://landsat.usgs.gov/calibration_notices.php) and the USGS Landsat Updates (http://landsat.usgs.gov/about_Landsat_Updates.php), starting in August 2013.

- During the early on-orbit period of Landsat-8, ground-based measurements indicated a significant error in the radiance produced from the TIRS. After collecting additional ground measurements through the summer of 2013, this error was estimated to be 0.29 W/m2/sr/mm for Band 10 and 0.51 W/m2/sr/mm for Band 11.

- All errors showed that TIRS reported a higher radiance than the ground measurements, as shown in the left graph of Figure 76. The right graph shows the results after accounting for this average radiance error, which is the calibration adjustment that was implemented for reprocessing in February 2014. The adjustment accounts for the apparent bias, but there remains a significant amount of variance, especially within Band 11.

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Figure 76: Thermal band errors (left graph) prior to calibration adjustment and (right graph) after calibration adjustment (image credit: USGS, NASA)

• May 22, 2014: After 14 years of drought, Lake Powell (on the Colorado River) was at 42 % of its capacity as of May 20, 2014. The low water levels are evident in these images of Figures 77 and 78, which were acquired by the Landsat-8 satellite on May 13, 2014. White bleached rock show where Lake Powell's shore is when the reservoir is at capacity. In Figure 77, which shows the northern section of Lake Powell, a muddy Colorado River flows through a largely empty lakebed. The Figure 78 shows a section of the reservoir closer to the Glen Canyon Dam and popular with boaters. Here, Halls Creek Bay is clearly smaller than it is in the National Park Service map of Lake Powell. 105)

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Figure 77: Image of the northern section of Lake Powell acquired by Landsat-8 on May 13, 2014 (image credit: NASA Earth Observatory, USGS)

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Figure 78: Image of the Lake Powell section closer to the Glen Canyon Dam acquired by Landsat-8 on May 13, 2014 (image credit: NASA Earth Observatory, USGS)

Legend to Figures 77 and 78: Water rules the western United States. Not only does it sustain cities, but it also fuels the economy. It's both a primary source of electricity and the foundation for agriculture. For all that, water is often not available in the Southwest and Intermountain West. To ensure a steady supply, the United States built a series of reservoirs throughout the 20th century. The two largest, Lake Mead and Lake Powell, sit behind massive dams on the Colorado River and provide water and electricity to several western states.

It is normal for water levels to fluctuate in the reservoir depending on how much water flows in from snow and rain and how much flows out to meet needs. However, it has been dry in all but three of the past 14 years. At the beginning of 2000, Lake Powell was at 94% of capacity. By October 2013 (the beginning of the 2014 water year), water levels had dropped to a low of 50% capacity, according to the Bureau of Reclamation, the agency that manages the reservoir. The Earth Observatory's World of Change shows this annual fluctuation and overall decline. With slightly above average snowpack in the basin that feeds the lake, water levels are expected to rebound to about 51% of capacity by October 2014, the end of the current water year.

While the drop in water levels are worrying for those who generate electricity or use the water for agriculture, the lower water levels may be a draw for recreation. Boaters coming to Lake Powell in the spring of 2014 will find beaches and rock formations that are usually underwater. Bullfrog Bay is the starting point for many boat rentals. The popularity of the spot is evident in the lower image: boats dot the surface of the water, just tiny white flecks at this scale.

• April 25, 2014: Of the roughly 1,550 volcanoes that have erupted in the recent geologic past, 113 are found on Kamchatka. Forty Kamchatkan volcanoes are "active," either erupting now or capable of erupting on short notice. The Operational Land Imager (OLI) on Landsat-8 captured activity at five of them during a single satellite pass on April 14, 2014. The imagery can be found at the following reference. 106)

• On April 8, 2014, the NASA Earth Observatory series released an image of Landsat-8 which spotted a perfect alluvial fan in Kazakhstan's Almaty province on September 9, 2013 (Figure 79). 107)

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Figure 79: OLI of the Landsat-8 satellite captured this view of an alluvial fan in Kazakhstan's Almaty province on Sept. 9, 2013 (image credit: NASA Earth Observatory, USGS)

Legend to Figure 79: In the lower left of the image, the Tente River flows through a narrow channel in the foothills of the Dzungarian Alatau range. Where the Tente emerges from the hills near Lake Alakol, it spreads out and becomes a braided stream. The movement of the channel over time has left a large fan that's about 20 km across at its widest point.

Mountain streams are usually confined to narrow channels and tend to transport sizable amounts of gravel, sand, clay, and silt—material that geologists call alluvium. The type and quantity of alluvium transported depends on the volume of the water flow and the gradient of the stream. Larger rivers pick up more alluvium than smaller ones; fast-flowing streams on steep slopes transport coarser sediment than slow-moving ones on shallow slopes.

The narrowest point of an alluvial fan—closest to the mountain front—is known as the apex; the broader part is called the apron. Alluvium deposited closer to the apex tends to be coarser than the material that makes up the apron. Alluvial fans are more likely to form in deserts because there is plenty of loose alluvium and not much vegetation to prevent stream channels from shifting.

Alluvial fans in arid areas are often used for agriculture because they are relatively flat and provide groundwater for irrigation. This fan is no exception. The blocky green pattern across the apron are fields or pasture land. A number of towns and villages, including Usharal and Beskol, are visible along the fan's outer edge. The straight feature cutting through Beskol and along the northeastern portion of the fan are railroad tracks.

• In March 2014, the Landsat-8 spacecraft and its payload are operating nominally. 108)

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Figure 80: Landsat-8 spacecraft status (image credit: USGS)

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Figure 81: Landsat-8 and Landsat-7 observation coverage frequency in the fiscal years FY13 and in March FY14 (image credit: NASA) 109)

• Feb. 25, 2014: Large landslide detected in Southeastern Alaska. Using imagery from the Landsat-8 satellite, scientists have confirmed that a large landslide occurred in southeastern Alaska on February 16, 2014. A preliminary estimate suggests the landslide on the flanks of Mount La Perouse involved 68 million metric tons of material, which potentially makes it the largest known natural landslide on Earth since 2010.

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Figure 82: OLI of Landsat-8 acquired this image on February 23, 2014 (image credit: NASA Earth Observatory) 110)

Legend to Figure 82: The avalanche debris appears light brown compared to the snow-covered surroundings. The sediment slid in a southeasterly direction, stretching across 7.4 km and mixing with ice and snow in the process. The slide was triggered by the collapse of a near-vertical mountain face at an elevation of 2,800 m, according to Colin Stark, a geophysicist at the Lamont-Doherty Earth Observatory at Columbia University.

Stark first became aware that a landslide may have occurred when a rapid detection tool that sifts through data collected by a global earthquake monitoring network picked up a signal indicative of a fairly significant event. The earthquake sensors detect seismic waves—vibrations that radiate through Earth's crust because of sudden movements of rock, ice, magma, or debris.

While both earthquakes and landslides produce both high-frequency and low-frequency waves, landslides produce more low-frequency waves on balance than earthquakes. Most earthquake detection tools are focused on high-frequency waves, but the detection tool Stark was using the Global CMT (Centroid-Moment-Tensor) Project, also looks closely at low-frequency waves, meaning it is more likely to detect landslides than other tools.

• Feb. 11, 2014: One year ago, on Feb. 11, 2013, NASA launched the Landsat-8 Earth-observing satellite from Vandenberg Air Force Base in California. The launch went perfectly, and 100 days later NASA transferred operational control to the USGS (U.S. Geological Survey). Landsat-8 then joined its predecessor satellites to provide a continuous record of change across Earth's land surfaces since 1972. 111)

- Landsat-8 is acquiring around 550 images/day – significantly more than the 400-image/day design requirement. Between Landsat-7 (launched in 1999 and still active) and Landsat-8, nearly 1,000 images/day are being collected. This is almost double the imagery collected three years ago, when Landsat-5 and -7 were operating together. The ability of Landsat-8 to image more frequently in persistently cloudy areas (e.g., humid tropics, high latitudes) is improving data collection in areas of critical importance for climate studies.

- Landsat-8's robust data processing system also enables images to be available for public use within five hours of their arrival at the USGS EROS (Earth Resources Observation and Science) Center in Sioux Falls, S.D. Since 2008, all Landsat data is free to all. Enhanced Landsat-8 data have quickly found their way into a wide range of operational applications, including forest health monitoring by the U.S. Forest Service, burn severity mapping by the USGS, NASA and the National Park Service, and cropland mapping by the National Agricultural Statistical Service.

- During the past 12 months, the USGS-EROS Center and NASA/GSFC have worked in close collaboration — putting the new satellite through its paces by steering it into its orbit, calibrating its detectors, collecting test images and certifying the mission for sustained operation.

- As partners in the Landsat program since its inception in the 1960s, USGS and NASA have distinct roles. NASA develops remote-sensing instruments and spacecraft, launches the Landsat satellites and validates their performance. The USGS then assumes ownership and operation of the Landsat satellites, in addition to managing ground-data reception, archiving, product generation and distribution.

• January 2014: The Landsat-8 spacecraft and its payload are operating nominally in 2014 with performance exceeding specifications in many respects. 112)

• January 10, 2014: Figure 83 is an OLI image of Guinea-Bissau and the Bissagos islands. Mangrove swamps are abundant along this coastline, acting as important feeding grounds for fish, birds and animals. Flowing from the east, the Geba River empties into the Atlantic Ocean, with the country's capital city of Bissau located on the river estuary. The city appears as a light brown area in the upper-central portion of the image. 113)

- Off the coast in the lower-left section of the image are the Bissagos (or Bijagós) islands – an archipelago of over 80 islands and islets. In 1996 the archipelago was declared a UNESCO Biosphere Reserve. A diversity of mammals, reptiles, birds and fish can be found on the islands, including protected or rare species such as the Nile crocodile, hippopotamus, African manatee and the common bottlenose dolphin. The archipelago has also been recognized as an important site for green sea turtles to lay their eggs.

- In the lower left corner, the island of Orango looks like a tree, with the waterways like branches and land appears as foliage. This island is the center of a national park, and is known for its matrimonial tradition where marriage is formally proposed by the women – who are also responsible for building the homes.

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Figure 83: The coast of Guinea-Bissau in West Africa is pictured in this image from the Landsat-8 satellite, acquired with OLI on May 3, 2013 (image credit: USGS, ESA)

• Dec. 09. 2013: The coldest spot on Earth was found to be in Antarctica on the East Antarctic Plateau where temperatures in several hollows can dip below minus 92ºC on a clear winter night. Scientists made the discovery while analyzing the most detailed global surface temperature maps to date, developed with data from remote sensing satellites including the new Landsat 8, a joint project of NASA and the U.S. Geological Survey (USGS). 114)

The researchers analyzed 32 years' worth of data from several satellite instruments. They found temperatures plummeted to record lows dozens of times in clusters of pockets near a high ridge between Dome Argus and Dome Fuji, two summits on the ice sheet known as the East Antarctic Plateau. The new record of minus 93.2º C was set Aug. 10, 2010.

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Figure 84: The lowest temperature on Earth at -93.2ºC was measured in Antarctica on Aug. 10, 2010 (image credit: NASA)

• Nov. 2013: The OLI instrument of Landsat-8 acquired imagery of lava flows in northern Chile, highlighting some of the distinctive features of a coulée. Lava domes tend to have steep, cliff-like fronts at their leading edge and wrinkle-like pressure ridges on their surfaces. The Chao is a type of lava dome known as a coulée. These elongated flow structures form when highly viscous lavas flow onto steep surfaces. 115)

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Figure 85: Shapes of lava flows in Chile acquired with OLI on May 13, 2013 and released on Nov. 21, 2013 (image credit: NASA Earth Observatory)

Legend to Figure 85: The Chao dacite sits between two volcanoes in northern Chile: the older and partially-eroded Cerro del Leon and the younger Paniri. The dome itself is a giant tongue of rock that extends southwest from the vent. Curved pressure ridges known as ogives dominate the surface of the 14 km dome.

Volcanologists estimate the Chao dacite dome formed over a period of about 100 to 150 years. A pyroclastic flow during the Chao I phase left light-brown deposits of tephra and pumice at the leading edge of the flow. Pyroclastic flows are avalanche-like events that bring mixtures of hot gas and semi-sold rocks surging down the flanks of volcanoes at speeds as fast as 100 km/hour.

This period was followed by the Chao II phase, when 22.5 km3 of lava erupted. This flow has 400 m tall fronts that stand out with their dark shadows on the southwest end. The final, Chao III phase added another 3.5 km3 of denser lava with a lower viscosity. This type of lava is less likely to form pressure ridges, so surfaces in this part of the flow are comparatively smooth.

• Oct. 22, 2013: Since its launch in February 2013, Landsat-8 has collected about 400 scenes of the Earth's surface/day. Each of these scenes covers an area of about 185 km x 185 km or 34,200 km2 for a total of 13,690,000 km2/day. An area about 40% larger than the United States, every day.

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Figure 86: Lava flows and ash plume from Klyuchevskaya volcano, Kamchatka, acquired by OLI on Oct. 20, 2013 (image credit: NASA)

Legend to Figure 86: Located on Russia's Kamchatka Peninsula, Klyuchevskaya (also spelled Kliuchevskoi) is one of the world's most active volcanoes. More than 100 flank eruptions have occurred at the stratovolcano in the past 3,000 years, according to Smithsonian's Global Volcanism Program. Twelve confirmed eruptions have occurred since 2000.

Klyuchevskaya has been erupting since August 15, 2013, though the intensity of activity surged in October. The Kamchatka Volcanic Eruption Response Team (KVERT) reported a thick plume of ash and steam streaming from the summit on October 11. Subsequent days brought explosive eruptions, lava fountains, and volcanic tremors. At times, the ash plume rose from the summit (elevation 5 km) up to 7.5 to 10 km.

When OLI of Landsat-8 flew over in the afternoon on October 20, multiple lava flows streamed down Klyuchevskaya northern and western flanks. The false-color image of Figure 86 shows heat from the flows in SWIR, and green light. Ash, water clouds, and steam appear gray, while snow and ice are bright blue-green. Bare rock and fresh volcanic deposits are nearly black. 116)

• August 25, 2013: A study of Garden and Hog Islands of Lake Michigan observed by Landsat-8. 117)

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Figure 87: Landsat-8 OLI image (excerpt of Figure 88) of Garden and Hog Islands of Lake Michigan acquired on May 24, 2013 (image credit: USGS)

Legend to Figures 87 and 88: Over thousands of years, retreating glaciers scoured and carved out much of the basin that now holds Lake Michigan. But in some parts of the lake, patches of erosion-resistant rock still protrude above the water. A cluster of small islands in the far northern reaches of the lake—the Beaver Island archipelago—are composed of limestone bedrock covered with a layer of sand and gravel (glacial "till").

Except for Beaver Island, the largest of the group, the islands are unpopulated. About 700 people live on Beaver Island, mainly in a small town on the northern part of the island. A Native American community survived on Garden Island until as recently as the 1900s, but the size of the community dwindled until the last remaining resident died in the 1940s.

The OLI (Operational Land Imager) on Landsat 8 captured the top image (Figure 87) of Garden and Hog islands on May 24, 2013. The lower image, a broader view (Figure 88), shows Beaver Island and the other islands in the context of the great lake. Dense forests, swamps, and sandy beaches dominate the landscape. Offshore, deeper waters appear dark blue, while shallow areas are turquoise.

The shallows around Garden and Hog islands contain numerous parallel rock ridges interspersed by deeper channels. These reef areas offer ideal spawning ground for various species of fish, notably lake trout and perch. Federal and state resource managers have attempted to replenish depleted lake trout populations by stocking northern Lake Michigan waters.

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Figure 88: Landsat-8 OLI image of the upper Lake Michigan acquired on May 24, 2013 (image credit: USGS)

• On August 18, 2013, Mount Sakurajima (Japan) on the island of Kyushu erupted. The eruption lasted for about 50 minutes, sending ash and smoke across the bay into the city of Kagoshima, which is near the southwestern tip of the island of Kyushu in Japan. 118)

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Figure 89: Landsat-8 acquired these images on August 19, 2013 which have been pan-sharpened to more clearly show the remaining smoke continuing to billow from the volcano (image credit: USGS)

Legend to Figure 89: The color image (left) uses Landsat bands 4, 3 and 1 of OLI (Operational Land Imager). The black and white image (right) was acquired with TIRS (Thermal Infrared Sensor) in band 10 and displays the temperature differences. Warmer surfaces appear light gray to white in the thermal image, while cooler areas appear dark gray to black. While there are few clouds near the caldera, the bright land surface indicates the heat on the land near the volcano.

• In June 2013, the Silver Fire of 55850 hectar in size in New Mexico, USA, was observed by Landsat-8 repeatedly to give forest restoration specialists a means to analyze and determine where the burn destroyed vegetation and exposed soil – and where to focus emergency restoration efforts. 119)

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Figure 90: Left: Landsat-8 false color image acquired on May 28, 2013; Right: Landsat-8 image acquired on June 13, 2013, while the New Mexico Silver Fire was still growing, the white puffs with black shadows in the right image are clouds (image credit: USGS, NASA)

Legend to Figure 90: The red color in the right-hand picture means high-severity fire, and the red areas were concentrated in a watershed drainage that fed communities west of Las Cruces, N.M. The BEAR (Burned Area Emergency Response) teams are designed to go in as soon as the flames die down to help protect reservoirs, watersheds and infrastructure from post-fire floods and erosion.

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Figure 91: The Landsat-8 soil burn severity map of the NM Silver Fire shows areas that with high (red), medium (yellow) and low (green) severity burns (imagage credit: USDA Forest Service, Burned Area Emergency Response Team)

Legend to Figure 91: As a wildfire starts to die down, fire managers can contact the Forest Service's Remote Sensing Applications Center in Salt Lake City to request maps that identify the high, moderate and low severity burns. When that call comes in, remote sensing specialist Carl Albury finds satellite imagery of the burned forest both pre- and post-fire.

In Landsat-8 imagery, two of the 11 spectral bands – the near-infrared band (No 5) and a short-wave infrared band (No 6) are used for hot spots. The near infrared reflects well from healthy vegetation, and the short-wave infrared bands reflect well from exposed ground. By comparing the normalized ratio of the near- and shortwave-infrared bands in the pre-fire image to the post-fire image, the burn severity can be estimated (Ref. 119).

Landsat-8 is operational — LDCM was officially renamed to Landsat-8. On May 30, 2013, NASA transferred operational control of the Landsat-8 satellite to the USGS (U.S. Geological Survey ) in Sioux Falls, S.D. This marks the beginning of the operational phase of the Landsat-8. The USGS now manages the satellite flight operations team within the Mission Operations Center, which remains located at NASA's Goddard Space Flight Center in Greenbelt, MD.

The mission carries on a long tradition of Landsat satellites that for more than 40 years have helped to study how Earth works, to understand how humans are affecting it and to make wiser decisions for the future. The USGS will collect at least 400 Landsat-8 scenes every day from around the world to be processed and archived at the USGS/EROS (Earth Resources Observation and Science Center) in Sioux Falls. 120)

• May 22, 2013: One of two new spectral bands identifies high-altitude, wispy cirrus clouds that are not apparent in the images from any of the other spectral bands. The March 24, 2013, natural color image of the Aral Sea, for example, appears to be from a relatively clear day. But when viewed in the cirrus-detecting band, bright white clouds appear. 121)

The SWIR band No 9 (1360-1390 nm) is the cirrus detection band of the OLI (Operational Land Imager) instrument. Cirrus clouds are composed of ice crystals. The radiation in this band bounces off of ice crystals of the high altitude clouds, but in the lower regions, the radiation is absorbed by the water vapor in the air closer to the ground. The information in the cirrus band is to alert scientists and other Landsat users to the presence of cirrus clouds, so they know the data in the pixels under the high-altitude clouds could be slightly askew. Scientists could instead use images taken on a cloud-free day, or correct data from the other spectral bands to account for any cirrus clouds detected in the new band.

Figures 92 and 93 are simultaneous OLI observations of the same area of the Aral Sea region in Central Asia which illustrate the power of interpretation of a scene. The cirrus clouds of Figure 93 are simply not visible in the natural color image of Figure 92. This new analysis feature will give scientists a better handle to study the changing environment.

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Figure 92: Natural color image of the Aral Sea region observed on March 24, 2013 (image credit: NASA)

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Figure 93: Cirrus cloud detection band image of the Aral Sea region observed on March 24, 2013 (image credit: NASA)

• May 9, 2013: Availability of free long-term Landsat imagery to the public. Today, Google released more than a quarter-century of images, provided free to the public, of Earth taken from space and compiled into an interactive time-lapse experience. Working with data from the Landsat Program managed by the USGS (U.S. Geological Survey), the images display a historical perspective on changes to Earth's surface over time. 122) 123) 124) 125)

The long-term archive of Landsat images of every spot on Earth is a treasure trove of scientific information that can form the basis for a myriad of useful applications by commercial enterprises, government scientists and managers, the academic community, and the public at large.

In 2009, Google started working with USGS to make this historic archive of Earth imagery available online. Using Google Earth Engine technology, the Google team sifted through 2,068,467 images—a total of 909 terabytes of data—to find the highest-quality pixels (e.g., those without clouds), for every year since 1984 and for every spot on Earth. The team then compiled these into enormous planetary images, 1.78 terapixels each, one for each year.

• May 6, 2013: As the LDCM satellite flew over Indonesia's Flores Sea on April 29, it captured an image of Paluweh volcano spewing ash into the air. The satellite's OLI instrument detected the white cloud of smoke and ash drifting northwest, over the green forests of the island and the blue waters of the tropical sea. The TIRS (Thermal Infrared Sensor) on LDCM picked up even more. 126) 127)

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Figure 94: An ash plume drifts from Paluweh volcano in Indonesia in this image, taken April 29, 2013 with OLI (image credit: NASA)

By imaging the heat emanating from the 5-mile-wide volcanic island, TIRS revealed a hot spot at the top of the volcano where lava has been oozing in recent months (Figure 95).

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Figure 95: This thermal image was taken by the TIRS instrument on April 29, 2013 (image credit: USGS, NASA)

Legend to Figure 95: A bright white hot spot, surrounded by cooler dark ash clouds, shows the volcanic activity at Paluweh volcano in the Flores Sea, Indonesia. The image of Paluweh also illuminates TIRS' abilities to capture the boundaries between the hot volcanic activity and the cooler volcanic ash without the signal from the hot spot bleeding over into pixels imaging the cooler surrounding areas.

• May 2, 2013: All spacecraft and instrument systems continue to perform normally. LDCM continues to collect more than 400 scenes per day and the U.S. Geological Survey Data Processing and Archive System continues to test its ability to process the data flow while waiting for the validation and delivery of on-orbit calibration, which convert raw data into reliable data products. 128)

• On April 12, 2013, LDCM (Landsat Data Continuity Mission) reached its final altitude of 705 km. One week later, the satellite's natural-color imager (OLI) scanned a swath of land 185 km wide and 9,000 km long. 129) 130)

• Since April 4, 2013, LDCM is on WRS-2 (Worldwide Reference System-2),

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Figure 96: These images show a portion of the Great Salt Lake, Utah as seen by LS-7 (left) and LS-8 (LDCM) satellites (right); both images were acquired on March 29, 2013 (image credit: USGS, Ref. 130)

Legend to Figure 96: On March 29-30, 2013, the LDCM was in position under the Landsat 7 satellite. This provided opportunities for near-coincident data collection from both satellites. The images below show a portion of the Great Salt Lake in Utah, and the Dolan Springs, Arizona area, the latter of which is used in Landsat calibration activities. 131)

• March 21, 2013: Since launch, LDCM has been going through on-orbit testing. The mission operations team has completed its review of all major spacecraft and instrument subsystems, and performed multiple spacecraft attitude maneuvers to verify the ability to accurately point the instruments. 132)

- As planned, LDCM currently is flying in an orbit slightly lower than its operational orbit of 705 km above Earth's surface. As the spacecraft's thrusters raise its orbit, the NASA-USGS team will take the opportunity to collect imagery while LDCM is flying under Landsat 7, also operating in orbit. Measurements collected simultaneously from both satellites will allow the team to cross-calibrate the LDCM sensors with Landsat 7's Enhanced Thematic Mapper-Plus instrument.

- After its checkout and commissioning phase is complete, LDCM will begin its normal operations in May. At that time, NASA will hand over control of the satellite to the USGS, which will operate it throughout its planned five-year mission life. The satellite will be renamed Landsat 8. USGS will process data from OLI and TIRS and add it to the Landsat Data Archive at the USGS Earth Resources Observation and Science Center, where it will be distributed for free via the Internet.

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Figure 97: First image of LDCM released in March 2013 (image credit: NASA) 133)

Legend to Figure 97: The first image shows the meeting of the Great Plains with the Front Ranges of the Rocky Mountains in Wyoming and Colorado. The natural-color image shows the green coniferous forest of the mountains coming down to the dormant brown plains. The cities of Cheyenne, Fort Collins, Loveland, Longmont, Boulder and Denver string out from north to south. Popcorn clouds dot the plains while more complete cloud cover obscures the mountains.
The image was observed on March 18, 2013 using data from OLI (Operational Land Imager) bands 3 (green), 5 (near infrared), and 7 (short wave infrared 2) displayed as blue, green and red, respectively.

• March 18, 2013: First day of simultaneous OLI and TIRS Earth imaging (Ref. 130).

• Feb. 21, 2013: The LDCM mission operations team successfully completed the first phase of spacecraft activation. All spacecraft subsystems have been turned on, including propulsion, and power has been supplied to the OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) instruments. 134)

• LDCM will go through a check-out phase for the next three months. Afterward, operational control will be transferred to NASA's mission partner, the USGS (U.S. Geological Survey), and the satellite will be renamed to Landsat-8. The data will be archived and distributed free over the Internet from the EROS (Earth Resources Observation and Science) center in Sioux Falls, S.D. Distribution of Landsat-8 data from the USGS archive is expected to begin within 100 days of launch.

• The LDCM spacecraft separated from the rocket 79 minutes after launch and the first signal was received 3 minutes later at the ground station in Svalbard, Norway. The solar arrays deployed 86 minutes after launch, and the spacecraft is generating power from them (Ref. 25).

Minimize Sensor Complement

Sensor complement: (OLI, TIRS)

Background: In 2008 the TIRS (Thermal Infrared Sensor) instrument was still regarded an option to the LDCM mission. However, in Dec. 2009, the US government confirmed that TIRS would be developed and would be on board the LDCM spacecraft. In the spring of 2010, TIRS passed the CDR (Critical Design Review). 135) 136)

The OLI and TIRS data are merged into a single data stream. Together the OLI and TIRS instruments on LDCM replace the ETM+ instrument on Landsat-7 with significant enhancements.

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Figure 98: Photo of the EM PIE (Payload Interface Electronics) equipment, image credit: NASA

 

OLI (Operational Land Imager):

Already in July 2007, NASA had awarded a contract to BATC (Ball Aerospace Technology Corporation) of Boulder, CO, to develop the OLI (Operational Land Imager) key instrument for LDCM. The BATC contract terms call for the design, development, fabrication and integration of one OLI flight model. Furthermore, the company is also required to test, deliver and provide post-delivery support and five years of on-orbit support for the instrument.

The multispectral and moderate resolution OLI instrument has similar spectral bands to the ETM+ (Enhanced Thermal Mapper plus) sensor of Landsat-7. It includes new coastal aerosol (443 nm, band 1) and cirrus detection (1375 nm, band 9) bands, though it does not have a thermal infrared band.

The following list provides an overview of the most important observation requirements for the OLI instrument: 137)

• The specifications require delivery of data covering at least 400 Landsat scenes/day (185 km x 180 km) for the US archive. The data are to be acquired in a manner that affords seasonal coverage of the global land mass. Data are required for the heritage reflective Thematic Mapper (TM) spectral bands plus two new bands, a blue band for coastal zone observations and a short wave infrared band for cirrus cloud detection.

• 30 m GSD (Ground Sample Distance) for VIS/NIR/SWIR, 15m GSD for PAN data.

• The specifications do not require thermal data (TIR band), representing a departure from the TM (Thematic Mapper) heritage. The specification also requires data providing a 30 m GSD (Ground Sample Distance) for each of the multispectral bands. Note: The TIR band was deselected due to the extra cost of active cooling.

• An edge response slope is also specified for the image data from each spectral band. The edge response is defined as the normalized response of the image data to a sharp edge as expressed in a Level 1R VDP (Validation Data Product). An edge response slope of 0.027 is required for bands 1 through 7, a slope of 0.054 is required for the panchromatic band, band 8, and a slope of 0.006 for the cirrus band, band 9.

• All instrument source data will be quantized to 12 bit resolution.

Band Nr

Band Name

Spectral range (nm)

Use of data

GSD

Radiance (W/m2 sr μm), typical

SNR
(typical)

1

New Deep Blue

433-453

Aerosol/coastal zone

30 m

40

130

2

Blue

450-515

Pigments/scatter/coastal

 

 

30 m
(TM heritage bands)

40

130

3

Green

525-600

Pigments/coastal

30

100

4

Red

630-680

Pigments/coastal

22

90

5

NIR

845-885

Foliage/coastal

14

90

6

SWIR 2

1560-1660

Foliage

4.0

100

7

SWIR 3

2100-2300

Minerals/litter/no scatter

1.7

100

8

PAN

500-680

Image sharpening

15 m

23

80

9

SWIR

1360-1390

Cirrus cloud detection

30 m

6.0

130

Table 6: NASA/USGS requirements for LDCM imager spectral bands

• The WRS-2 (Worldwide Reference System-2) defines Landsat scenes as 185 km x 180 km rectangular areas on the Earth's surface designated by path and row coordinates. This heritage system is used to catalogue the data acquired by the Landsat 4, 5, and 7 satellites and will also be used for the LDCM.

• Provide "standard", orthorectified data products within 24 hours of observation (products available via the web at no cost)

• Data calibration consistent with previous Landsat missions

• Continue IC (International Cooperator) downlinks

• Support priority imaging and a limited off-nadir collection capability (± 1 path/row).

OLI (LDCM)

ETM+ (Landsat-7)

Band Nr

Wavelength (µm)

GSD (m)

Band No.

Wavelength (µm)

GSD (m)

8 (PAN)

0.500 - 0.680

15

8 (PAN)

0.52 - 0.90

15

1

0.433 - 0.453

30

 

 

 

2

0.450 - 0.515

30

1

0.45 - 0.52

30

3

0.525 - 0.600

30

2

0.53 - 0.61

30

4

0.630 - 0.680

30

3

0.63 - 0.69

30

 

 

 

4

0.78 - 0.90

30

5

0.845 - 0.885

30

 

 

 

9

1.360 - 1.390

30

 

 

 

6

1.560 - 1.660

30

5

1.55 - 1.75

30

7

2.100 - 2.300

30

7

2.09 - 2.35

30

OLI does not include thermal imaging capabilities

6 (TIR)

10.40 - 12.50

60

Figure 99: Spectral parameter comparison of OLI and ETM+ instruments

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Figure 100: OLI and ETM spectral bands (image credit: NASA)

OLI instrument:

The OLI design features a multispectral imager with a pushbroom architecture (Figure 101) of ALI (Advanced Land Imager) heritage, a technology demonstration instrument flown on the EO-1 spacecraft of NASA (launch Nov. 21, 2000). A pushbroom implementation is considered to be more geometrically stable than the whiskbroom scanner of the ETM+ instrument. As a tradeoff of this architecture selection, the imagery must be terrain corrected to ensure accurate band registration.. 138) 139) 140) 141) 142)

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Figure 101: Schematic view of the OLI instrument design (image credit: BATC)

The FPA (Focal Plane Assembly) consists of 14 FPMs (Focal Plane Modules). This is a consequence of the pushbroom architecture selection for OLI leading to a different set of geometric challenges than a cross-track whiskbroom implementation. Instead of using a small focal plane and a scanning mirror, 14 FPMs are required to cover the full Landsat cross-track field of view. Each FPM contains nine spectral bands in along-track (Figure 102). The along-track spectral band separation leads to an approximately 0.96-second time delay between the leading and trailing bands. This time delay creates a small but significant terrain parallax effect between spectral bands, making band registration more challenging.

The along-track dimension of the OLI focal plane (see Figure 103) also makes it desirable to "yaw steer" the spacecraft. This means that the spacecraft flight axis is aligned with the ground (Earth fixed) velocity vector, rather than with the inertial velocity vector, in order to compensate for cross-track image motion due to Earth rotation.

Although the pushbroom architecture requires many more detectors and a correspondingly larger focal plane, it also allows for a much longer detector dwell time (~4 ms for OLI vs. 9.6 µs for ETM+), leading to much higher signal-to-noise ratios. The lack of moving parts in the pushbroom design also allows for a more stable imaging platform and good internal image geometry.

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Figure 102: Schematic view of the FPM layout concept (image credit: BATC, USGS)

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Figure 103: Orientation of the FPMs in the FPA (Focal Plane Assembly) of the OLI instrument (image credit: BATC)

Each FPM contains detectors for each spectral band, silicon for the VNIR bands and HgCdTe for the SWIR bands and a butcher-block filter assembly to provide the spectral bands.

OLI features about 6500 active detectors per multispectral band and 13000 detectors for the panchromatic band. These detectors are organized as blocks ~500 multispectral (1000 panchromatic) detectors wide within 14 focal plane modules (FPMs) that make up the focal plane assembly. Each module has its own butcher-block assembly spectral filter. This provides significantly improved signal to noise performance, but complicates the process of radiometrically matching the detectors responses. Similarly, the lack of a scan mirror removes the need for knowledge of its movement, but requires knowledge of the detectors locations across a much larger focal plane (Ref. 2).

Observation technique

Pushbroom imager

Spectral bands

9 bands in VNIR/SWIR covering a spectral range from 443 nm to 2300 nm

Telescope

- Four-mirror off-axis telescope design with a front aperture stop
- Use of optical bench
- Telecentric design with excellent stray light rejection

FPA (Focal Plane Assembly)

- Consisting of 14 sensor chip assemblies mounted on a single plate
- FPA is passively cooled
- Hybrid silicon / HgCdTe detectors
- Butcher block filter assembly over each SCA (Sensor Chip Assembly)

Swath width (FOV=15º)

185 km

GSD (Ground Sample Distance)

15 m for PAN data; 30 m for VNIR/SWIR multispectral data

Data quantization

12 bit

Calibration

- Solar calibrator (diffuser) used once/week
- Stimulation lamps used to check intra-orbit calibration
- Dark shutter for offset calibration (used twice per orbit)
- Dark detectors on focal plane to monitor offset drift

Instrument, mass, power, size

 

Table 7: Overview of OLI instrument parameters

The OLI will provide global coverage by acquiring ~400 scenes per day in six VNIR and three SWIR bands, all at 12 bit radiometric resolution. In addition to these bands, there will be a tenth band consisting of covered SWIR detectors, referred to as the ‘blind' band, that will be used to estimate variation in detector bias during nominal Earth image acquisitions. The OLI bands are distributed over 14 SCAs (Sensor Chip Assemblies) or FPMs, each with 494 detectors per 30 m band and twice as many for the 15 m panchromatic band - totaling in over 75000 imaging detectors. 143)

OLI calibration:

The OLI calibration subsystem (Figures 104 and 105) consists of two solar diffusers (a working and a pristine), and a shutter. When positioned so that the sun enters the solar lightshade, the diffusers reflect light diffusely into the instruments aperture and provide a full system full aperture calibration. The shutter, when closed, provides a dark reference. In addition, two stim lamp assemblies are located at the front aperture stop. Each lamp assembly contains three lamps (per redundant configuration) that are operated at constant current and monitored by a silicon photodiode. The lamp signal goes through the full telescope system. Additionally, the OLI focal plane will include masked HgCdTe detectors, that is, detectors that will be blocked from seeing the Earth's radiance (Ref. 2). 144) 145)

Solar diffusers:

- Full-aperture full system Spectralon diffuser, designed to be used at different frequencies to aid in tracking the system and diffuser changes. The pristine diffuser will be used to check degradation of main diffuser.

- The primary solar diffuser will nominally be deployed every 8 days to track the calibration of the OLI sensor and perform detector-to-detector normalization.

- The solar diffuser based calibration requires a spacecraft maneuver to point the OLI solar calibration aperture towards the sun. The pristine diffuser will be used on a less frequent basis, about every six months, as a check on the primary diffuser's degradation.

Stimulation lamps:

- Multi—bulbed tungsten lamp assemblies, that illuminate the OLI detectors through the full optical system, similarly designed to be used at different frequencies to separate lamp and system changes. The working lamp will be used daily for intra-orbit calibration/characterization; the reference lamp set approximately monthly, and the pristine lamp set approximately twice a year.

- The lamb assembly can also be compared to solar diffuser measurements to check stability.

Dark shutter:

- Used twice per orbit for offset calibration

• Dark detectors on focal plane to monitor offset drift

• Linearity checked by varying detector integration time.

The LDCM operational concept also calls for the spacecraft to be maneuvered every lunar cycle to view the moon, providing a "known" stable source for tracking stability over the mission. A side-slither maneuver, where the spacecraft is rotated 90º to align the detector rows with the velocity vector, is also planned. These data will provide an additional method to assess the detector-to-detector radiometric normalization.

Pre-launch spectro-radiometric characterization and calibration (Ref. 144):

The spectral characterization of the OLI instrument is being performed at the component, focal plane module and fill instrument levels. The components, which have all completed testing, include detector witness samples, spectral filters prior to dicing into flight filter sticks, the focal plane assembly window witness samples and telescope mirror witness samples.

The FPM (Focal Plane Module) level tests, which are also complete, are specifically designed to characterize the spectral out-of-band response. The FPM level tests measure the spectral response of all the detectors by illuminating the full focal plane at approximately the correct cone angle.

An integrating sphere is used in the pre-launch radiance calibration of the OLI. The traceability of the calibration of this sphere will start with the 11" OLI transfer sphere directly calibrated at the NIST Facility for Spectroradiometric Calibration (FASCAL). While still at NIST, this OLI transfer sphere is checked by independently NIST calibrated University of Arizona (UAR VNIR transfer radiometer), NASA and NIST (Government Transfer Radiometers) radiometers. Also, the Ball Standard Radiometer (BSR), that has filters matching the OLI bands, views the sphere.

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Figure 104: OLI block diagram illustrating the calibration subsystem in front of the telescope (image credit: NASA, BATC)

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Figure 105: Blow-up of the calibration subsystem illustrating the solar diffuser and shutter assemblies (image credit: NASA, BATC)

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Figure 106: Illustration of the OLI instrument (image credit: NASA, BATC)

In Nov. 2008, the OLI instrument passed the ICDR (Instrument Critical Design Review). 146)

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Figure 107: Photo of the completed OLI instrument with electronics (image credit: BATC, NASA, USGS)

Delivery of the OLI instrument in the summer of 2011 (Ref. 3).

 

TIRS (Thermal Infrared Sensor)

The TIRS instrument is providing continuity for two infrared bands not imaged by OLI. NASA/GSFC is building the TIRS instrument inhouse. TIRS is a late addition to the LDCM mission, the requirements call for a GSD (Ground Sample Distance of 120 m for the imagery; however, the actual GSD will be 100 m.

The LDCM ground system will merge the data from both sensors into a single multispectral image product. These data products will be available for free to the general public from the USGS enabling a broad scope of scientific research and land management applications. 147) 148)

TIRS is a QWIP (Quantum Well Infrared Photodetector) based instrument intended to supplement the observations of the OLI instrument. The TIRS instrument is a TIR (Thermal Infrared) imager operating in the pushbroom mode with two IR channels: 10.8 µm and 12 µm. The two spectral bands are achieved through interference filters that cover the FPA (Focal Plane Assembly). The pushbroom implementation increases the system sensitivity by allowing longer integration times than whiskbroom sensors. The two channels allow the use of the "split-window" technique to aid in atmospheric correction.

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Figure 108: Functional block diagram of TIRS (image credit: NASA, Ref. 145)

The focal plane consists of three 640 x 512 QWIP GaAs arrays mounted on a silicon substrate that is mounted on an invar baseplate. The two spectral bands are defined by bandpass filters mounted in close proximity to the detector surfaces. The QWIP arrays are hybridized to ISC9803 readout integrated circuits (ROICs) of Indigo Corporation. The focal plane operating temperature will be maintained at 43 K (nominally). 149) 150) 151)

Instrument type

Pushbroom imager

Two channel thermal imaging instrument

10.8 and 12.0 µm band centers

Bandwidths

10.3-11.3 µm,
11.5-12.5 µm

GSD (Ground Sample Distance)

100 m (nominal), 120 m (requirement)

Swath width

185 km, FOV = 15º

Operating cadence

70 frames/s

Instrument calibration

- Scene select mirror to select between 2 calibration sources
- Two full aperture calibration sources: onboard internal calibration and space view

Detector

- Three SCA (Sub-Chip Assembly) QWIP detectors built in-house at Goddard
- FPA consists of three 640 x 512 detector arrays
- Pixel size of 25 µm producing an IFOV of 142 µrad
- The FPA consists of an invar "spider" which is bonded to the silicon interface board
containing the QWIPs and on which the "daughter boards" are mounted.
- Actively cooled FPA operating at 43 K
- Two-stage cryocooler provided by BATC

Telescope

- The telescope is a 4-element refractive lens system.
- Passively cooled telescope operating at 185 K

Telescope f number

f/1.64

Data quantization

12 bit

Instrument mass, size, power

236 kg, approx: 80 cm x 76 cm x 43 cm, 380 W

Table 8: TIRS instrument parameters

QWIP detector: The development of the QWIP detector technology has made great strides in the first decade of the 21st century. In 2008, NASA/GSFC revised the design of the infrared detector concept of the TIRS (Thermal Infrared Sensor) imager, under development for the LDCM (Landsat Data Continuity Mission). The initially considered HgCdTe-based detector design was changed to a QWIP design due to the emergence of broadband QWIP capabilities in the MWIR and TIR (LWIR) regions of the spectrum. The introduction of QWIP technology for an operational EO mission represents a breakthrough made possible through collaborative efforts of GSFC, the Army Research Lab and industry (Ref. 150).

An important advantage of GaAs QWIP technology is the ability to fabricate arrays in a fashion similar to and compatible with the silicon IC technology. The designer's ability to easily select the spectral response of the material from 3 µm to beyond 15 µm is the result of the success of band-gap engineering. 152)

Advantages of QWIP technology:

- Large lattice-matched substrates

- Mature materials technology

- No unstable mid-gap traps

- Inherently, radiation hard.

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Figure 109: QWIP quantum state diagram (image credit: NASA/JPL)

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Figure 110: TIRS 10-13 µm QWIP spectral response requirement (image credit: NASA)

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Figure 111: Overview of the TIRS focal plane layout (image credit: NASA, Ref. 145)

The three arrays are precisely aligned to each other in the horizontal and vertical directions (to within 2 µm). There is a requirement that the detection region within the QWIP array be within 10 µm of a common focal plane altitude. This specification is challenging since it includes surface non-uniformities of the baseplate, substrate, the QWIP/ROIC hybrid and the epoxy bond lines between these components. Nonetheless, since there are three discreet arrays they must all fall within a single focus position.

The filter bands are further confined to specific regions of the QWIP array. Although each array contains 512 rows, after all the operational requirements are satisfied (frame rate, windowing, co-registration, scene reconstruction, etc.) only 32 rows are available under each filter band separated by 76 rows of occluded pixels (for dark current subtraction). Once all these requirements are incorporated into the focal plane design, eligible rows on any given array are pre-determined. Of these eligible rows, there must be three that can be combined to make two perfect rows, or preferably, at least two perfect rows (that is, rows where all pixels meet every specification).

TRL (Technology Readiness Level) tests: An important and essential process for qualifying new or previously unused technology in a NASA space mission is the technology readiness level demonstration. There are nine levels with level 6 (TRL 6) being the level at which new hardware must be demonstrated. Typically, this means qualification in the environment which the instrument will be subjected through out the mission; radiation effects, vibration, thermal cycling and (in some cases) shock. Both the readout and QWIP hybrids were subjected to gamma, proton and heavy ion radiation equivalent to 35 krad or almost 10 times the expected mission dose. At these levels and at the operating temperature of 43 K minimal effects were observed and none were considered to be a mission risk.

A fully functioning focal plane assembly was subjected to 40 thermal cycles from 300 K to 77 K and back to 300 K. Every tenth cycle went to 43 K to collect the array performance data. After the completion of the 40 cycles there was essentially no change in any of the three QWIP arrays (2 grating QWIP hybrids and one C-QWIP hybrid). - The final environmental test performed was vibration to simulate the effect of the launch. Since this is a qualification test the vibration loads are specified 3db above the expected loads. The focal plane assembly was subjected to a series of vibration input loads including x, y and z-axis random vibration for 2 minutes/axis, a sine sweep and sine burst test (15 g at 20 Hz). No failures occurred and this assembly and the overall design was certified by an independent review panel as having met the requirements for TRL 6.

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Figure 112: Schematic view of the FPA (Focal Plane Assembly), image credit: NASA

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Figure 113: Photos of the FPA (image credit: NASA)

Legend to Figure 113: The left photo of the FPA is without filters showing the 3 QWIPs in the center. The daughter boards are the red and green assemblies to the left and right, respectively. The invar spider is the component with the 4 arms. - The right picture of the FPA comes with the filters attached. Note that there are two filters over each array with a thin dark strip between them.

Optical system: The imaging telescope is a 4-element refractive lens system. A scene select mechanism (SSM) rotates a scene mirror (SM) to change the field of regard from a nadir Earth view to either an on-board blackbody calibrator or a deep space view. The blackbody is a full aperture calibrator whose temperature may be varied from 270 to 330 K.

The optical system, consisting of a lens with three Ge elements and one ZnSe element, produces nearly diffraction-limited images at the focal plane. All but 2 of the surfaces are spherical, which simplifies fabrication. The optics are radiatively cooled to a nominal temperature of 185 K to reduce the contribution of background thermal emission to the measurement noise. Because of the fairly strong thermal dependence of the index of refraction of Ge, the focus position of the lens is a function of the optics temperature. This provides a method of adjusting focus so that, in the unlikely event that launch conditions or some other effect defocus the system, the temperature of the optics may be changed by ±5 K to refocus. That is, thermal control of the lens provides a non-mechanical focus mechanism. A +5 K change does not significantly degrade the noise performance.

A precision scene select mirror is an essential component of the TIRS instrument and it is driven by the scene select mechanism. It rotates around the optical axis on a 45º plane to provide the telescope with a view of Earth through the nadir baffle and two full aperture sources of calibration, onboard variable temperature blackbody (hot calibration target) and space view (cold calibration target). The onboard blackbody will be a NIST (National Institute of Standards and Technology) certified reference source (Figure 114).

TIRS is able to achieve a 185 km ground swath with a 15º FOV (Field of View) functioning in the pushbroom sample collection method. This method will have the benefit of being able to collect and record data without movement artifacts due to its wide instantaneous field of view. Frames will be collected at an operating cadence of 70 per second. The collected data will be stored temporarily stored on board and periodically sent to the USGS EROS facility for further storage. The instrument is designed to have an expected lifetime of at least a three years.

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Figure 114: The TIRS optical sensor unit concept (image credit: NASA)

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Figure 115: Schematic view of the TIRS instrument internal assembly (image credit: NASA, Ref. 145)

Legend to Figure 115: Model of the TIRS instrument showing the major components of the TIRS sensor. The scene select mechanism rotates the field of regard from the Earth view to either the space view or to the on-board calibrator. The right side provides some detail of optical system showing the 4-element lens, a cut-away view of the SM and the thermal strap connecting the FPA to the cryocooler cold tip. The MEB (Main Electronics Box) and the CCE (Cyrocooler and its associated Control Electronics), not shown, are mounted to the spacecraft.

TIRS instrument calibration:

Consistent with previous Landsat missions, LDCM TIRS will be fully calibrated prior to launch. Calibration measurements will be made at GSFC and will be done at the component, subsystem and instrument level. NIST-traceable instrument level calibration will be done using an in-chamber calibration system. 153) 154)

Among other uses, TIRS data will be used to measure evapotranspiration (evaporation from soil and transpiration from plants); to map urban heat fluxes, to monitor lake thermal plumes from power plants; to identify mosquito breeding areas and vector-borne illness potential; and to provide cloud measurements. The evapotranspiration data may be used to estimate consumptive water use on a field-by-field basis.

TIRS instrument calibration makes use of the following elements:

• Precision scene select mirror to select between calibration sources and nadir view

• Two full aperture calibration sources

- Onboard variable temperature blackbody

- Space view

- Calibration every 34 minutes

• NIST Traceable radiometric calibration

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Figure 116: Schematic of inclusion of NIST standards (image credit: NASA)

TIRS calibration system:

• A 41 cm diameter source is covering full field and aperture of TIRS (Flood Source)

• Target Source Module (GeoRadSource)

- Blackbody point source w/ filter & chopper

- All reflective, off-axis parabola collimator

- Motorized target and filter wheels

- A square steering mirror system (33 cm side length) is permitting coverage of the full aperture and field

• Cooled enclosure over entire system

• External monochromator (spectral source)

• Components are mounted to common base plate.

The TIRS radiometric response is determined via the prelaunch characterization relative to the laboratory blackbody. This approach provides the highest accuracy calibration. The calibration philosophy is then to evaluate (or validate) the calibration parameters once TIRS is on orbit. If the calibration of TIRS is demonstrated to change significantly while on orbit using measurements during the checkout period, then the on-board blackbody (OBB) will be used as the primary pathway to NIST traceability.

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Figure 117: Illustration of the TIRS calibration system (image credit: USGS)

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Figure 118: Illustration of TIRS on the LDCM spacecraft (image credit: NASA, Ref. 3)

SSM (Scene Select Mechanism) of TIRS:

The SSM for the TI RS instrument, developed at NASA/GSFC, is a single axis, direct drive mechanism which rotates a 207 mm scene mirror from the nadir science position to the 2 calibration positions twice per orbit. It provides pointing knowledge and stability to ~10 µradians. The SSM can be driven in either direction for unlimited rotations. The rotating mirror is dynamically balanced over the spin axis, and does not require launch locking. 155)

The design of the SSM is straightforward; it is a single axis rotational mechanism. The operational cadence was to hold the scene mirror stationary for ~40 minutes staring at nadir, rotate 120º to the space view aperture and stare for 30 seconds, rotate 120º to the internal blackbody and stare for 30 seconds and then rotate the mirror to the back to nadir. Then the entire process would start again. The mechanism would be operating all of the time, or have a 100% duty cycle. Since LDCM/TI RS was to be in a highly-inclined polar orbit, the general idea was to calibrate twice per orbit while over the poles.

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Figure 119: Cutaway view of the SSM (image credit: NASA)

Instrument mass, power

15 kg, 6 W average

Pointing knowledge, stability

±9.7 µradians over 34 minutes, ±9.7 µradians over 2.5 seconds

Duty cycle

100%

Thermal operational

0 / +20ºC stable to ±1ºC

Thermal survival range

-50 / +40ºC

Lifetime

3.25 years on orbit

Redundancy

A/B side block redundancy

Operational cadence

Stare nadir for 30-40 minutes
Rotate 120º in < 2 minutes to space view
Stare for ~30 seconds,
Rotate 120º in < 2 minutes to blackbody view
Stare for ~30 seconds
Rotate to 120º in < 2 minutes to nadir view

Table 9: SSM driving requirements

 

Parameter

Landsat ETM+

LDCM OLI

GMES/Sentinel-2 MSI

Spectral bands

Band

µm

Band

µm

Band

µm

 

 

1 (blue)

0.43-0.45

B1 (blue)

0.43-0.45

1 (blue)

0.45–0.52

2 (blue)

0.45–0.52

B2 (blue)

0.46–0.52

2 (green)

0.52–0.60

3 (green)

0.52–0.60

B3 (green)

0.54–0.58

3(red)

0.63–0.69

4 (red)

0.63–0.68

B4 (red)

0.65-0.68

 

 

 

 

B5 (red edge)

0.70-0.71

 

 

 

 

B6 (red edge)

0.73-0.75

 

 

 

 

B7 (red edge)

0.77-0.79

4 (NIR)

0.76–0.90

 

 

B8 (NIR)

0.78-0.90

 

 

5 (NIR)

0.84-0.88

B8a (NIR)

0.86-0.88

 

 

 

 

B9 (water vapor)

0.93-0.95

 

 

9 (cirrus)

1.36-1.39

B10 (cirrus)

1.37-1.39

5 (SWIR1)

1.55–1.75

6 (SWIR1)

1.56-1.66

B11 (SWIR1)

1.57-1.66

7 (SWIR2)

2.08–2.35

7 (SWIR2)

2.10-2.30

B12 (SWRIR2)

2.10-2.28

 

 

LDCM TIRS

 

 

6 (TIR)

10.4–12.5

10 (TIR1)

10.3-11.3

 

 

 

 

11 (TIR2)

11.5-12.5

 

 

GSD at nadir

30 m VNIR
15 m Pan
60 m TIR

30 m VNIR
15 m Pan
100 m TIR

10 m (B2, B3, B4, B8)
20 m (B5, B6, B7, B8a, B11, B12)
60 m (B1, B9, B10)

Quantization

8 bit

12 bit

12 bit

Onboard Calibration

Yes

Yes

Yes

Resivit time

16 days

16 days

5 days (2 satellites)

Off-axis viewing

Up to 7.5º off nadir

Up to 7.5º off nadir

Up to 10.3º off nadir (w/o pointing)

Orbit altitude

705 km

705 km

786 km

Swath width

185 km

185 km

290 km

Architecture

Cross-track scanner (Whiskbroom)

Pushbroom

Pushbroom

Table 10: Comparison of Landsat and GMES/Sentinel-2 imager specifications 156)

 

Collection of imagery onboard LDCM:

The co-aligned instruments are nominally nadir pointed and sweep the ground track land surface in contiguous image data collections, also known as image intervals. Each image interval may contain from a few WRS-2 scenes for an island or coastal area up to 77 contiguous WRS-2 scenes for an extended area of interest. For each image interval, the observatory executes a pre-defined imaging and ancillary data collection sequence as shown in Figure 120. 157)

Prior to the image interval, the spacecraft configures the onboard systems for the mission data collection session. A specific number of intervals are pre-defined on the ground based upon the number of WRS-2 scenes scheduled for collection, and allocated in the SSR (Solid State Recorder). Each instrument will transmit focal plane sensor data and instrument ancillary data (voltages, temperatures, etc.), which the spacecraft will interleave with the spacecraft ancillary data (attitude, ephemeris, etc), and record to files in the SSR.

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Figure 120: Data collection sequence (image credit: USGS, NASA)

If the observatory is over an IC (International Cooperatoror) or LGN (Landsat Ground Network) station, it will simultaneously transmit data in real time to the ground. In addition, each instrument performs routine on-board calibrations (blackbody, lamps, etc) before and after each image interval, and during less frequent occasions utilizing the sun and moon as external calibration sources. A representation of the global image collection and calibration opportunities within the WRS-2 grid is shown in the 16-day repeating DRC-16 (Design Reference Case-16) in Figure 121.

The DRC-16 was developed to aid the mission architects in identification of all image and calibration activities and to verify that all are consistent with spacecraft power and mission data management capabilities. Instrument solar, lunar, and internal calibrations are required by ground system processing systems for image reconstruction, and to produce finished and distributable image products.

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Figure 121: Illustration of DRC-16 collections (image credit: USGS, NASA)

End to end mission data flow is represented in Figure122 . Mission data originates as instrument sensor (or "image") data, and are collected and processed by the instrument electronics. The instrument electronics transmits the image data to the spacecraft PIE (Payload Interface Electronics) over a HSSDB (High Speed Serial Data Bus), using a serializer-deserializer integrated circuit pair. OLI image data are compressed using the USES (Universal Source Encoder for Space) ASIC (Application-Specific Integrated Circuit), which implements the Rice algorithm for lossless compression. - TIRS data are not compressed due to the low data rate. Instrument image data are interleaved with spacecraft ancillary data to create a file, which is stored and/or provided to the transmitter for downlink.

Ancillary data are collected at rates up to 50 Hz, and is comprised of GPS (Global Positioning System) data, IMU (Inertial Measurement Unit) data, star tracker data, and select instrument engineering information required by ground system algorithms for image product generation. The ancillary data are multiplexed within the mission data files every second.

Mission data files are intentionally fixed in size at 1GB. A system architecture trade study was performed early in mission definition to establish the optimum file size given the implementation of Class 1 CCSDS (Consultative Committee for Space Data Systems) CFDP (File Delivery Protocol), and a required link BER (Bit Error Rate) of < 10-12. Utilizing the 440 Mbit/s available downlink capacity, each downlinked mission data file requires 22 seconds of continuous transmission for a completed delivery to the ground system. The low BER requirement on the communication link provides the confidence that only one file over several days would require retransmission, well within the available contact time with the ground stations.

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Figure 122: Overview of the mission data flow (image credit: USGS, NASA)

Simultaneous real-time and playback mission data files are transmitted to the ground through virtual channels within a single physical channel. Five data streams on the transmitter interface board may be multiplexed on to the link via an arbiter, which interleaves the data streams according to a pre-established priority scheme. The data streams priorities are:

1) Real-time for the OLI instrument

2) Real-time for the TIRS instrument

3) SSR playback channel 1

4) SSR playback channel 2

5) A virtual channel for fill frames in case no image data are available for downlink.

The five virtual channels are arbitrated in priority order on a frame-by-frame basis. The OLI instrument has the highest priority followed by the TIRS instrument and, since the combined mission data rates are less than the total downlink bandwidth available, there is always residual bandwidth available for mission data playback. This enables maximum utilization of the downlink bandwidth. SSR playback 1 has priority over SSR playback 2. SSR playback 2 is controlled by an autonomous on-board spacecraft flight software task that queues files for playback by a predefined algorithm (i.e. oldest to newest priority files first, oldest to newest non-priority files next).

SSR playback 1 is specifically for ground system intervention, as required to supersede the onboard autonomous SSR playback 1, to downlink files which are a higher priority than originally categorized. SSR playback 2 will resume automatically upon the completion of SSR playback 1 ground commands, and there is no need to stop and restart the autonomous SSR playback 2 queue. As a frame finishes transmission, the priority arbiter selects the highest priority channel that has a frame buffer ready for transmission for the next frame.

To ensure the bandwidth of the space to ground data link is at least twice the bandwidth of the real-time mission data, the spacecraft compresses OLI image data in near real time using CCSDS compression. During early hardware development, using simulated data derived from the ALI (Advanced Land Imager) sensor aboard the EO-1 (Earth Observing-1) satellite (the precursor to the OLI instrument), data sets were constructed and flowed through the compression chip and achieved a nominal 1.55:1 compression. As compression varies on the OLI real-time virtual channel, the playback capacity also varies to use the bandwidth that is available.

The multiplexed virtual channels of mission data are provided to the RF X-band subsystem, where the transmitter adds CCSDS layers and LDPC (Low Parity Density Check) 7/8-rate forward error correction to the 384 Mbit/s data stream, resulting in a 440 Mbit/s stream from the X-band subsystem. The X-band stream is modulated, amplified and down-linked from the spacecraft antenna to the ground station antenna/receivers.

The ground station antenna system receives the 8200.5 MHz OQPSK (Offset-keyed Quadrature Phase Shift Keying) X-band signal from the observatory and forwards the down converted 1.2 GHz or 720 MHz intermediate frequency (IF) signal to a programmable telemetry receiver. The IF signals are routed through a matrix switch, providing signal distribution or routing to redundant equipment as needed.

Within the programmable telemetry receiver, the IF signal from the switching matrix is subject to low-pass filtering to prevent subsequent aliasing followed by an AGC (Automatic Gain Control) action. The AGC action is the last analog handling of the signal prior to the digitizer. The entire ground processing that remains is accomplished in the digital domain. The signal is immediately digitally demodulated within a specially designed modified Costas loop and the resultant baseband signal, now a softbit stream, is sent to the bit synchronizer. The bit stream has ambiguity resolved and is then frame synchronized. The frame synchronizer parses the data stream into equal length frames; queuing on a predefined frame synchronization pattern. The data are de-randomized using the conventional CCSDS algorithm and then stripped of parity and bit-corrected by the LDPC 7/8-rate FEC (Forward Error Correction) decoder.

The frame synchronization processor routes the framealigned data stream to the VCDU (Virtual Channel Data Unit) processor. The VCDU processor identifies the unique virtual channels within the frames and outputs these VCDU into individual data streams for packet processing.

The mission data stream from the VCDU processor is processed through the CCSDS packet processor to separate APID (Application Process Identifiers). The packet processor outputs the resulting mission stream to the CFDP processor.

 


 

LDCM ground system:

The LDCM ground system includes all of the ground-based assets needed to operate the LDCM observatory. The primary components of the ground segment are : 158) 159) 160)

- MOE (Mission Operations Element)

- CAPE (Collection Activity Planning Element)

- GNE (Ground Network Element)

- DPAS (Data Processing and Archive System).

The USGS (United States Geological Survey) -and their associated support and development contractors - will:

- Develop the Ground System (comprised of the Flight Operations and Data Processing and Archive Segments), excluding procurement of the MOE

- Provide ground system functional area expertise across all mission segments

- Lead, fund, and manage the Landsat Science Team

- Acquire the FOT (Flight Operations Team) and produce the FOT products 161)

- Lead the LDCM mission operations, after the completion of the on-orbit checkout period

- Accept and execute all responsibilities associated with the transfer of the LDCM OLI (Operational Land Imager) instrument, TIRS (Thermal Infrared Sensor) instrument, spacecraft bus and Mission Operations Element contracts from NASA following on-orbit acceptance of the LDCM system including assuming contract management"

- Provide system engineering for the USGS-managed segments and elements.

The MOE is being provided by the Hammer Corporation. The MOE contract was awarded in September 2008. The MOE provides capability for command and control, mission planning and scheduling, long-term trending and analysis, and flight dynamics analysis. The overall activity planning for the mission is divided between the MOE and CAPE. The MOE hardware and software systems reside in the LDCM MOC (Mission Operations Center).

The CAPE develops a set of image collection and imaging sensor(s) calibration activities to be performed by the observatory. The CAPE schedules activities on a path-row scene basis. The MOE converts CAPE-generated path-row scenes to observatory activities, schedules these and any other detailed observatory activities, and generates commands necessary to collect the identified scenes and operate the observatory.

The GNE is comprised of two nodes located at Fairbanks, Alaska and Sioux Falls, SD. Each node in the GNE includes a ground station that will be capable of receiving LDCM X-band data. Additionally, each station provides complete S-band uplink and downlink capabilities. The GNE will route mission data and observatory housekeeping telemetry to the DPAS.

The DPAS includes those functions related to ingesting, archiving, calibration, processing, and distribution of LDCM data and data products. It also includes the portal to the user community. The ground system, other than the MOE, is developed by USGS largely through their support service contract. The DAPS will be located at the USGS EROS (Earth Resources Observation and Science) Center in Sioux Falls, SD.

Data access policy: All Landsat data are freely available over the Internet.

Landsat8_Auto3

Figure 123: Illustration of the Landsat-8 mission elements and communication architecture (image credit: NASA) 162) 163)

LGN (LDCM Ground Network) stations: The LGN is a collection of ground stations with state of the art electronics and sophisticated ground software, each providing similar mission services. The configuration of LGN uses the ground stations located at the EROS Center campus in Sioux Falls, South Dakota, the GLC (Gilmore Creek) ground station in Fairbanks, Alaska, and the SvalSat (Svalbard Satellite Station) ground station in Svalbard (Spitsbergen), Norway.

Each LGN ground station consists of a tracking antenna, S-band and X-band communication equipment, mission data storage and a file routing DCRS (Data Collection and Routing Subsystem). The LGN antenna receives X-band mission data files (autonomous playback or commanded) from the observatory, while simultaneously performing file management and subsequent image collection operations over S-band. The S-band and X-band systems of each LGN station interfaces with the MOE and DPAS in a closed loop fashion.

The USGS maintains agreements with several foreign governments referred to as the Landsat ICs (International Cooperators). The ICs are a special user community that has the ability to receive LDCM mission data from the observatory real-time X-band downlink stream. Real-time imaging sensor and ancillary data (including spacecraft and calibration data) required to process the science data are contained within the real-time stream downlink.

The ICs will be capable of receiving real-time X-band imaging sensor data downlinks and sending metadata to the DPAS. The ICs will submit imaging sensor data collection and downlink requests to the CAPE (via the DPAS user portal). ICs participate in a bilateral DV&E (Data Validation & Exchange) program with the DPAS. This program includes exchange of archive data upon request, and validation of IC processed level 1 data products by the USGS.

Landsat8_Auto2

Figure 124: Overview of the LDCM system architecture (image credit: USGS)

Item

Parameter

Total size

System

Daily volume of 400 scenes

390 GByte

Spacecraft

C&DH data rate

260.92 Mbit/s

Space to ground communication

Downlink data rate
LDPC ⅞ rate packet

384 Mbit/s data, 441 Msample/s symbol
8160 bit

Ground station

Minutes per day (14 contacts)

98 minutes

Science archive

5 year archive

~ 400 TB

Table 11: Overview of data volumes for processing and archiving functions

IC (International Cooperator) Ground Stations of the Landsat Program:

• In 41 years, 39 IC stations in 23 countries

• Most still collect and/or distribute Landsat products, reducing the load on U.S. Systems

• More than 215,000 products distributed in 2012

- Represents a nearly 10% off-loading of network bandwidth

- Enhanced regional exploitation of Landsat data

Landsat8_Auto1

Figure 125: Overview of the IC (International Cooperator) network (image credit: USGS, Ref. 108)

 

IAS (Image Assessment System):

Once the LDCM spacecraft is in orbit, the radiometric, geometric and spatial performance of OLI and TIRS sensors will be continually monitored, characterized and calibrated using the IAS (Ref. 143).

Background: The IAS was originally developed to monitor radiometric and geometric performance of the Landsat-7 ETM+ sensor and the quality of the image data in the Landsat-7 archive. The operational performance monitoring is achieved by processing a number of randomly selected Level 0R (raw reformatted) images to Level 1R (radiometrically corrected) and Level 1G (geometrically corrected) products. In that process, image statistics at different processing levels, calibration data, and telemetry data are extracted and stored in the IAS database for automatic and off-line assessment. The IAS also processes and analyzes the pre-selected geometric and radiometric calibration sites and special calibration acquisitions, e.g. solar diffuser or night data needed for radiometric calibration or noise and stability studies. The final and most important product of the IAS trending and processing is the CPF (Calibration Parameter File), the file that contains parameters needed for artifact corrections and radiometric and geometric processing of raw image data. To maintain the accuracy of the dynamic parameters, the CPF is updated at least once every three months.

The purpose of the LDCM IAS is to maintain accurate spectral, radiometric, spatial and geometric characterization and calibration of LDCM data products and sensors, ensuring compliance with the OLI and TIRS data quality requirements. The IAS will trend results of processing standard Earth images and nonstandard products, such as lunar, solar, dark Earth or stellar images, evaluate image statistics and calculate and store image characteristics for further analysis.

Landsat8_Auto0

Figure 126: The LDCM ground system concept (image credit: USGS)

The IAS will automatically generate calibration parameters, which will be evaluated by the calibration analysts. In addition to standard operations within the IAS, the CVT (Calibration and Validation Team) will use a ‘toolkit' module containing instrument vendor developed code and routines developed by the CVT, as a research and development environment for improvements of algorithm functionality and anomaly investigations.

Compared to the previous IAS versions, the LDCM IAS system will have to handle a significantly larger and more complex database that will include characterization data from all normally acquired images (~ 400 scenes per day, with special calibration acquisitions, e.g solar and lunar) processed through the product generation system. OLI's pushbroom design (~ 75000 detectors), as opposed to an ETM+ whiskbroom design, requires characterization and calibration of about 550 times more detectors than in case of ETM+ (136 detectors) and represents a major challenge for the LDCM IAS. An additional challenge is that the LDCM IAS must handle data from two sensors, as the LDCM products will contain both the OLI and TIRS data.

For radiometric and geometric processing, see Ref. 143).

• Processing latency for real-time downlinks

• Average latency is ~ 5 hours from acquisition to product availability

• Closed loop between ground and space for data management

• The system requirement calls for 85% data availability to the user community through EROS Portal within 48 hours. The actual performance for Landsat-8 averages within 5 hours.

Table 12: Landsat 8 operational characteristics (Ref. 108)

Landsat-8 reprocessing (Ref. 108):

• All Landsat 8 data is being reprocessed to make corrections based on first year data analysis.

• Corrections to both OLI (Operational Land Imager) and the TIRS (Thermal Infrared Sensor) data are being made including:

- all calibration parameter file updates since launch

- improved OLI reflectance conversion coefficients for the cirrus band

- improved OLI radiance conversion coefficients for all bands

- refined OLI detector linearization to decrease striping

- a radiometric offset correction for both TIRS bands

- a slight improvement to the geolocation of the TIRS data

• Approximately 90% of reprocessing is completed with estimated completion by March 30, 2014.

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95) Kasha Patel, “NASA Ocean Data Shows ‘Climate Dance’ of Plankton,” NASA, Sept. 29, 2014, URL: http://www.nasa.gov/content/goddard/nasa-ocean-data-shows-climate-dance-of-plankton/

96) Michael J. Behrenfeld, “Climate-mediated dance of the plankton,” Journal of Nature Climate Change, Vol. 4, 2014, pp. 880–887, Published online, 25 September 2014, doi:10.1038/nclimate2349

97) “Sustainable Land Imaging Architecture Study,” NASA/GSFC, Sept. 5, 2014, URL: http://sustainablelandimaging.gsfc.nasa.gov/

98) Laura Rocchio, “Curiosities of the Danakil Depression,” NASA Earth Observatory, August 27, 2014, URL: http://earthobservatory.nasa.gov/IOTD/view.php?id=84239

99) Adam Voiland, Robert Simmon, “Retreat of Yakutat Glacier,” NASA, Earth Observatory, August 20, 2014, URL: http://earthobservatory.nasa.gov/IOTD/view.php?id=84180

100) Kate Ramsayer, “Taking NASA-USGS’s Landsat 8 to the Beach,” NASA, July 2, 2014, URL: http://www.nasa.gov/content/goddard/taking-nasa-usgs-s-landsat-8-to-the-beach/

101) Laura Rocchio, “Hanhowuz Reservoir, Turkmenistan,” NASA Earth Observatory, released on July 1, 2014, URL: http://earthobservatory.nasa.gov/IOTD/view.php?id=83940

102) “The Loop,” NASA Earth Observatory, released on June 18, 2014, URL: http://earthobservatory.nasa.gov/IOTD/view.php?id=83875

103) “Earth observation image of the week: multiple ice streams on the southwestern coast of Greenland,” ESA, June 13, 2014, URL: http://www.esa.int/spaceinimages/Images/2014/06/Southwestern_coast_of_Greenland

104) “Landsat 8 Thermal Infrared Sensor (TIRS) Update,” USGS, June 6, 2014, in 'Landsat Update - Volume 8 Issue 2 2014,' URL: http://landsat.usgs.gov/about_LU_Vol_8_Issue_2.php#2a

105) “Lake Powell Half Empty,” NASA Earth Observatory, May 22, 2014, URL: http://earthobservatory.nasa.gov/IOTD/view.php?id=83716

106) “Five Volcanoes Erupting at Once,” NASA Earth Observatory, April 16, 2014, URL:
http://earthobservatory.nasa.gov/IOTD/view.php?id=83502

107) “Alluvial Fan in Kazakhstan,” NASA Earth Observatory, Jesse Allen and Robert Simmon, April 8, 2014, URL: http://earthobservatory.nasa.gov/IOTD/view.php?id=83455

108) Jenn Sabers Lacey, “USGS EROS Center - 40 Years of Service to our Plnat,” Proceedings of JACIE 2014 (Joint Agency Commercial Imagery Evaluation) Workshop, Louisville, Kentucky, USA, March 26-28, 2014, URL: https://calval.cr.usgs.gov
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109) Steven Volz, “NASA Earth Science Flight Program Overview,” Proceedings of JACIE 2014 (Joint Agency Commercial Imagery Evaluation) Workshop, Louisville, Kentucky, March 26-28, 2014, URL:
https://calval.cr.usgs.gov/wordpress/wp-content/uploads/Volz_JACIE-Presentation.pdf

110) “Large Landslide Detected in Southeastern Alaska,” NASA Earth Observatory, Feb. 25, 2014, URL: http://earthobservatory.nasa.gov/IOTD/view.php?id=83195

111) Kate Ramsayer, Jon Campbell, “NASA-USGS Landsat 8 Satellite Celebrates First Year of Success,” NASA News, February 11, 2014, URL: http://www.nasa.gov/content
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112) Information provided by Jim R. Irons of NASA/GSFC and by James M. Lacasse of USGS Climate and Land Use Change, Sioux Falls, S.D.

113) “Guinea-Bissau and the Bissagos islands,” ESA Earth from Space video program, released on Jan. 10, 2014, URL: http://spaceinimages.esa.int/Images/2014/01/Guinea-Bissau_and_the_Bissagos_islands

114) Steve Cole, Kate Ramsayer, “ NASA release 13-364, Dec. 09.2013, URL: http://www.nasa.gov/press/2013/december/nasa-usgs-landsat-8-satellite-pinpoints-coldest-spots-on-earth/#.Us_HGfuFf_o

115) “The Shapes that Lavas Take,” NASA Earth Observatory, Nov. 21, 2013, URL: http://earthobservatory.nasa.gov/IOTD/view.php?id=82424

116) “Klyuchevskaya Erupts,” NASA Earth Observatory, Oct. 25, 2013, URL: http://earthobservatory.nasa.gov/IOTD/view.php?id=82227

117) “Garden and Hog Islands, Michigan,” NASA Earth Observatory, August 25, 2013, URL: http://earthobservatory.nasa.gov/IOTD/view.php?id=81913

118) “Mount Sakurajima Volcano Erupts,” USGS, Aug. 22, 2013, URL:
http://landsat.usgs.gov/images/gallery/305_L.jpg

119) Kate Ramsayer, “After a Fire, Before a Flood: NASA's Landsat Directs Restoration to At-Risk Areas,” NASA/GSFC, August 21, 2013, URL: http://www.nasa.gov/content/goddard
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120) Steve Cole, Kate Ramsayer, Jon Campbell, “Landsat 8 Satellite Begins Watch,” NASA Release 13-160, May 30, 2013, URL: http://www.nasa.gov/home/hqnews/2013/may/HQ_13-160_Landsat_8_Begins.html

121) Kate Ramsayer, “NASA's Landsat Satellite Looks for a Cloud-Free View,” NASA, May 22, 2013, URL: http://www.nasa.gov/mission_pages/landsat/news/cloud-free-aral.html

122) Jon Campbell, “Landsat Images Provide the Gold Standard for New Earth Applications,” USGS News rooms, May 9, 2013, URL: http://www.usgs.gov/newsroom/article.asp?ID=3586&from=rss_home

123) Rebecca Moore, “A picture of Earth through time,” Google Official Blog, May 9, 2013, URL: http://googleblog.blogspot.de/

124) Steve Cole, “New Public Application of Landsat Images Released,” NASA, May 9, 2013, URL: http://www.nasa.gov/mission_pages/landsat/news/google-engine.html

125) “LDCM Mission Updates,” NASA, URL:
http://www.nasa.gov/mission_pages/landsat/main/mission-updates.html

126) “Landsat Thermal Sensor Lights Up from Volcano's Heat,” NASA, May 6, 2013, URL: http://www.nasa.gov/mission_pages/landsat/news/indonesia-volcano.html

127) “Landsat Data Continuity Mission,” NASA, News and Features, URL: http://www.nasa.gov/mission_pages/landsat/news/index.html

128) “LDCM Status Update for May 2, 2013,” NASA, May 15, 2013, URL: http://www.nasa.gov/mission_pages/landsat/main/mission-updates.html

129) Matt Radcliff, Rob Simmon, Jesse Allen, Holli Riebeek, Paul Przyborski, “Come Fly With the Newest Landsat,” NASA Earth Observatory, URL:
http://earthobservatory.nasa.gov/Features/LDCMLongSwath/?src=features-recent

130) Tom Holm, “Landsat: Building a Future on 40 Years of Success - April 14, 2013, 705 km Orbit,” 12th Annual JACIE (Joint Agency Commercial Imagery Evaluation) Workshop , St. Louis, MO, USA, April 16-18, 2013, URL: http://calval.cr.usgs.gov/wordpress
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131) “LDCM Underfly with Landsat 7,” USGS, March 29, 2013, URL: http://landsat.usgs.gov/LDCM_Underfly_with_Landsat_7.php

132) Steve Cole, Jon Campbell, “First Images Released From Newest Earth Observation Satellite,” NASA, USGS, March 21, 2013, URL:
http://www.nasa.gov/home/hqnews/2013/mar/HQ_13-080_LDCM_Images.html

133) “A Closer Look at LDCM's First Scene,” NASA, March 21, 2013, URL: http://www.nasa.gov/mission_pages/landsat/news/first-images-feature.html

134) “LDCM Status Update for Feb. 21,” NASA, Feb. 21, 2013, URL: http://www.nasa.gov/mission_pages/landsat/main/index.html

135) “NASA Completes Critical Design Review Of One Landsat Instrument,” Space Daily, May 28, 2010, URL:
http://www.spacedaily.com/reports
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136) Bryant Cramer, “USGS Perspectives on LDCM and Landsat,” Landsat Science Team Meeting,” Jan. 19-21, 2010, Mountain View, CA, USA, URL: http://landsat.usgs.gov/documents/Jan_2010_Cramer_01_19_10Landsat_Future_BriefingLSTtnv2.pdf

137) Tom Loveland, “Landsat and LDCM Status,” 2008 NASA Carbon Cycle & Ecosystems Joint Science Workshop, April 28-May 2, 2008, University of Maryland, Adelphi, MD, USA

138) James Storey, Michael Choate, Kenton Lee, “Geometric performance comparison between the OLI and ETM+,” Proceedings of the Pecora 17 Memorial Remote Sensing Symposium, Denver, Co, USA, Nov. 16-20, 2008

139) Jeanine Murphy-Morris, “Operational Land Imager ,” Landsat Science Team Meeting, Sioux Falls, SD, Jan. 8, 2008, URL: http://landsat.usgs.gov/documents/Murphy_Morris_Science_Team_OLI_chart.ppt

140) Edward J. Knight, “OLI Overview and Status,” Landsat Science Team Meeting, July 15, 2008, Reston, VA, URL: http://landsat.usgs.gov/documents/Knight_OLI.pdf

141) Bill Ochs, “Status of the Landsat Data Continuity Mission,” Landsat Science Team Meeting, July 15, 2008, Reston, VA, URL: http://landsat.usgs.gov/documents/Ochs_LDCM_Status.pdf

142) Edward J. Knight, Brent Canova, Eric Donley, Geir Kvaran, Kenton Lee, “The Operational Land Imager:Overview and Performance,” 10th Annual JACIE ( Joint Agency Commercial Imagery Evaluation) Workshop, March 29-31, 2011, Boulder CO, USA, URL: http://calval.cr.usgs.gov/JACIE_files/JACIE11/Presentations/TuePM/325_Knight_JACIE_11.070.pdf

143) Esad Micijevic, Ron Morfitt, “Operational Calibration and Validation of Landsat Data Continuity Mission (LDCM) Sensors using the Image Assembly System (IAS),” Proceedings of IGARSS (IEEE International Geoscience and Remote Sensing Symposium) 2010, Honolulu, HI, USA, July 25-30, 2010

144) Brian L. Markham, Philip W. Dabney, Edward J. Knight, Geir Kvaran, Julia A. Barsi, Jeanine E. Murphy-Morris, Jeffrey A. Pedelty, “The Landsat Data Continuity Mission Operational Land Imager (OLI) Radiometric Calibration,” Proceedings of IGARSS (IEEE International Geoscience and Remote Sensing Symposium) 2010, Honolulu, HI, USA, July 25-30, 2010, URL: http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20100026050_2010028396.pdf

145) Brian Markham, “LDCM On-Orbit Cal/Val Considerations,” Proceedings of the Landsat Science Team Meeting, Mesa, AZ, USA, March 1-3, 2011, URL: http://landsat.usgs.gov/documents/LDCM_Cal_Val_Considerations.pdf

146) “Ball Aerospace Completes CDR For Landsat's Operational Land Imager,” Nov. 26, 2008, Spacemart, URL:
http://www.spacemart.com/reports
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147) “NASA Completes Critical Design Review of Landsat Data Continuity Mission,” Science Daily, June 1, 2010, URL: http://www.sciencedaily.com/releases/2010/06/100601171850.htm

148) Dennis Reuter, Cathy Richardson, James Irons, Rick Allen, Martha Anderson, Jason Budinoff, Gordon Casto, Craig Coltharp, Paul Finneran, Betsy Forsbacka, Taylor Hale, Tom Jennings, Murzy Jhabvala, Allen Lunsford, Greg Magnuson, Rick Mills, Tony Morse, Veronica Otero, Scott Rohrbach, Ramsey Smith, Terry Sullivan, Zelalem Tesfaye, Kurtis Thome, Glenn Unger, Paul Whitehouse, “The Thermal Infrared Sensor on the Landsat Data Continuity Mission,” Proceedings of IGARSS (International Geoscience and Remote Sensing Symposium), Honolulu, Hawaii, USA, July 25-30, 2010, URL: http://landsat.gsfc.nasa.gov/pdf_archive/Reuter_etal-IGARSS2010.pdf

149) Ramsey L. Smith, Kurtis Thome, Cathleen Richardson, James Irons, Dennis Reuter, “Terrestrial Applications of the Thermal Infrared Sensor, TIRS,” URL: http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20100003053_2010002398.pdf

150) M. Jhabvala, D. Reuter, K. Choi, C. Jhabvala, M. Sundaram, “QWIP-Based Thermal Infrared Sensor for the Landsat Data Continuity Mission,” Proceedings of the QSIP (Quantum Structure Infrared Photodetector) 2009 International Conference, January 18-23, 2009, Yosemite, CA

151) M. Jhabvala, D. Reuter, K. Choi, M. Sundaram, C. Jhabvala, A. La, A. Waczynski, J. Bundas, “The QWIP focal plane assembly for NASA's Landsat Data Continuity Mission,” Proceedings of the SPIE, 'Infrared Technology and Applications XXXVI,' edited by Bjørn F. Andresen, Gabor F. Fulop, Paul R. Norton, Volume 7660, April 5-9, 2010, Orlando, FLA, USA, pp. 76603J-76603J-13, doi:10.1117/12.862277

152) M. Jhabvala, K. K. Choi, C. Monroy, A. La, “Development of a 1 k × 1 k, 8–12 µm QWIP array,” Infrared Physics & Technology, Volume 50, Issues 2-3, April 2007, pp. 234-239

153) K. Thome, D. Reuter, A. Lunsford, M. Montanaro, R. Smith, Z. Tesfaye, B. Wenny, “Calibration overview for the Thermal Infrared Sensor (TIRS) on the LandsatData Continuity Mission,” 10th Annual JACIE ( Joint Agency Commercial Imagery Evaluation) Workshop, March 29-31, 2011, Boulder CO, USA, URL:
http://calval.cr.usgs.gov/JACIE_files/JACIE11/Presentations/TuePM/340_Thome_JACIE_11.145.pdf

154) K. Thome, D. Reuter, A. Lunsford, M. Montanaro, R. Smith, Z. Tesfaye, B. Wenny, “Calibration of ther Thermal Infrared Sensor on the Landsat Data Continuity Mission,” Proceedings of IGARSS (International Geoscience and Remote Sensing Symposium), Vancouver, Canada, July 24-29, 2011

155) Jason Budinoff, Konrad Bergandy, Joseph Schepis, Adam Matuszeski, Richard Barclay, “Development of the Scene Select Mechanism for the Thermal Infrared Sensor Instrument,” Proceedings of the 14th European Space Mechanisms & Tribology Symposium – ESMATS 2011, Constance, Germany, Sept. 28–30 2011 (ESA SP-698)

156) Mary Pagnutti, Robert E. Ryan, Kara Holekamp, “Landsat Data Continuity Mission and Sentinel-2 Multi-Spectral Instrument Image Product Simulations for Sensor Comparisons and Data Fusion Research,” Proceedings of the 11th Annual JACIE (Joint Agency Commercial Imagery Evaluation ) Workshop, Fairfax, VA, USA, April 17-19, 2012, URL:
http://calval.cr.usgs.gov/wordpress/wp-content/uploads/Pagnutti_JACIE2012.pdf

157) James Nelson, Robert Patschke, Howard Garon, Alan Ames, Claire Mott, Grant Mah, Jason Williams, James Joseph, “Landsat Data Continuity Mission (LDCM) Space to Ground Mission Data Architecture,” Proceedings of the 2012 IEEE Aerospace Conference, Big Sky, Montana, USA, March 3-10, 2012

158) Landsat Data Continuity Mission (LDCM), Ground System (GS) Integration and Test Plan,” USGS, LDCM-I&T-001, Version 1.1, September 2009, URL: http://www.usgs.gov
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159) Jonathan Gal-Edd, “LDCM Ground System - Network Lessons Learned,” SOSTC GSFC May 24-25, 2010, URL: https://info.aiaa.org/tac/SMG/SOSTC
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160) Dave Hair , Doug Daniels, “Landsat Data Continuity Mission (LDCM) USGS Project Status Report,” Proceedings of the Landsat Science Team Meeting, Mesa, AZ, USA, March 1-3, 2011, , URL: http://landsat.usgs.gov/documents/LandsatScienceTeamLDCMGroundSystemsOverviewv4-1.pdf

161) Susan M. Good, Ann M. Nicholson, Mark A. Woodard, “Landsat Data Continuity Mission (LDCM) Flight Dynamics System (FDS),” Proceedings of SpaceOps 2012, The 12th International Conference on Space Operations, Stockholm, Sweden, June 11-15, 2012

162) G. R. Mah, H. Garon, C. Mott, M. O'Brien, “Ground System Architectures Workshop 2014, Landsat 8 Test as You Fly, Fly as You Test,” Proceedings of GSAW 2014 (Ground System Architectures Workshop), Los Angeles, CA, USA, Feb. 24-27, 2014, URL:
http://gsaw.org/wp-content/uploads/2014/03/2014s04mah.pdf

163) Del Jenstrom, “Status of the Landsat Data Continuity Mission,” Proceedings of the Landsat Science Team Meeting, Mesa, AZ, USA, March 1-3, 2011


The information compiled and edited in this article was provided by Herbert J. Kramer from his documentation of: ”Observation of the Earth and Its Environment: Survey of Missions and Sensors” (Springer Verlag) as well as many other sources after the publication of the 4th edition in 2002. - Comments and corrections to this article are always welcome for further updates (herb.kramer@gmx.net).

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