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.


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)




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)


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).


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


Figure 4: Block diagram of the C&DH subsystem (image credit: NASA, USGS, Ref. 88)

- 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.


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.


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. 88).

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


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. 88).

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).


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


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


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)

Figure 11: Anatomy of Landsat 8. Have you ever wondered what all the parts of a satellite do? This video identifies a few of the main components onboard Landsat 8 and tells you about their role in flying the satellite and capturing images of the Earth's surface below (video credit: USGS, NASA) 28)

Figure 12: The Landsat Data Continuity Mission (LDCM), a collaboration between NASA and the USGS (U.S. Geological Survey), will provide moderate-resolution measurements of Earth's terrestrial and polar regions in the visible, near-infrared, short wave infrared, and thermal infrared. There are two instruments on the spacecraft, the Thermal InfraRed Sensor (TIRS) and the Operational Land Imager (OLI). LDCM will provide continuity with the nearly 40-year long Landsat land imaging data set, enabling people to study many aspects of our planet and to evaluate the dynamic changes caused by both natural processes and human practices (image credit: NASA, USGS) 29)


Note: As of May 2019, the previously single large Landsat-8 file has been split into three files, to make the file handling manageable for all parties concerned, in particular for the user community.

This article covers the Landsat-8 mission and its imagery in the period 2019, in addition to some of the mission milestones

Landsat-8 imagery in the period 2018

Landsat-8 imagery in the period 2017 to June 2013



Mission status and imagery of 2019:

• May 15, 2019: While most Himalayan glaciers are retreating, about 200 in the Karakoram Range are doing the opposite. Scientific and military authorities in Pakistan are monitoring one of them closely due to the potential for flooding. 30)

- About 1 percent of the world's glaciers surge. These glaciers cycle through periods when they abruptly flow several times faster than usual. At peak speeds, surging glaciers can advance several meters per day—fast enough to block streams, bulldoze trees, crash into roads, and damage infrastructure. Surges typically last for a few months (and sometimes several years), and are then followed by a period of little or no movement that can last for a decade or longer.


Figure 13: OLI on Landsat-8 acquired this image on 1 April 2019 [image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the USGS. Story by Adam Voiland, with information from Jeff Kargel (Planetary Science Institute), Cameron Watson (University of Arizona), Andreas Kääb (University of Oslo), and Umesh Haritashya (University of Dayton)]

- One surging glacier in northern Pakistan sits near Mount Shishpar (also Shisparé or Shishper), a 7,611-meter peak in the Hunza District. In April 2018, the debris-covered glacier started to accelerate, with certain parts moving as fast as 13 to 18 meters (43 to 59 feet) per day. Since the surge started, the front of Shishpar Glacier has advanced by about 1 kilometer. As the ice pushed south past an adjacent valley, it blocked a meltwater stream flowing from the neighboring Muchuhar Glacier. By autumn 2018, the water had pooled up and formed a sizable lake.


Figure 14: These images, acquired by OLI on on Landsat-8, show the position of the glacier and lake on April 1, 2019 (right), compared to April 5, 2018. The ice appears gray because dust, soil, and other debris are piled on top of it [image credit: NASA Earth Observatory, images by Lauren Dauphin, using Landsat data from the USGS. Story by Adam Voiland, with information from Jeff Kargel (Planetary Science Institute), Cameron Watson (University of Arizona), Andreas Kääb (University of Oslo), and Umesh Haritashya (University of Dayton)]

- Generally, ice-dammed lakes like this are unstable and do not last for more than one season; most drain slowly and do not to cause any problems. But sometimes the ice dams collapse suddenly or lake water spills over the dam, causing fast-moving, dangerous floods. Because of this, scientists are conducting frequent ground surveys near Shishpar and analyzing satellite imagery daily.

- In a release on April 27, the Gilgit-Baltistan Disaster Management Authority indicated that the risk of a damaging flood had decreased due to falling lake levels. In an earlier release, the group noted that hot weather in the summer could cause rapid melting and hazardous overflows, and they outlined several steps to reduce the risk of a flood disaster in communities downstream. In the case of a severe flood, a nearby section of the Karakoram Highway, large numbers of homes in the village of Hasanabad, important irrigation channels, and two power plants could all be affected.

- The glacier's surge has already had some consequences. One nearby power station went offline due to a lack of incoming water. Also, a key pathway that miners and cattle once used to cross the glacier safely became impassable. In August 2018, that change trapped cattle in summer pastures and prevented miners from reaching a work site, the Pamir Times reported.

- This is not the first time that this glacier has surged. Field research and analysis of satellite imagery indicate that Shishpar also surged in 1904-1905, 1972-1976, and 1993-2002.

• May 8, 2019: Following a severe drought in 2018, the unusually wet winter and spring of 2019 has swollen Iraq's rivers, lakes, and reservoirs. Since January, many parts of the country have seen rainfall amounts that are double or triple the norm. 31)

- All of that water has to go somewhere. In northern Iraq, a principal destination has been the lake behind Mosul Dam, the largest reservoir in the country. According to data collected by the CNES/NASA Jason-2 and Jason-3 satellites, water levels in April 2019 at the reservoir reached the highest levels in at least a decade.


Figure 15: These observations and analyses were recorded by the Global Reservoir and Lake Monitor (G-REALM), a project sponsored by NASA and the U.S. Foreign Agricultural Service. FAS uses such water level measurements to assess irrigation potential and long-term drought conditions around the globe (image credit: NASA Earth Observatory, image by Joshua Stevens, using JASON-2 and JASON-3 altimetry data from NOAA and the G-REALM project)


Figure 16: The Operational Land Imager (OLI) on Landsat 8 acquired images of the reservoir in April 2015 and April 2019. Beyond the water levels, notice how much greener the land surface was in 2019. Note also how much suspended sediment flowed into the northern end of the reservoir through the Tigris River [image credit: NASA Earth Observatory images by Joshua Stevens, using Landsat data from the U.S. Geological Survey, Story by Adam Voiland, with information and factchecking from Charon Birkett (University of Maryland), William Empson (U.S. Army), and William Baker (USDA FAS)]

- Government officials and engineers monitor the stability of Mosul Dam since some areas beneath it contain gypsum, a water-soluble rock. To strengthen the dam, Iraq's Ministry of Water Resources has been injecting cement into the foundation to replace any gypsum that has dissolved. When these maintenance operations were halted in 2014 due to a takeover of the dam by ISIS militants, scientists used radar to observe whether the dam was sinking.

- In 2016, with the Iraqi government back in control of the dam, the Ministry of Water Resources enlisted an Italian firm, Trevi, and the U.S. Army Corps of Engineers to begin a three-year intensive program to purchase new equipment and aggressively treat the rock foundation with cement to ensure the stability of the dam.


Figure 17: The upstream Mosul Dam Lake image acquired with OLI on Landsat-8 on 25 April 2015 (image credit: (image credit: NASA Earth Observatory images by Joshua Stevens, using Landsat data from the U.S. Geological Survey, Story by Adam Voiland)


Figure 18: The upstream Mosul Dam Lake image acquired with OLI on Landsat-8 on 04 April 2019 (image credit: NASA Earth Observatory images by Joshua Stevens, using Landsat data from the U.S. Geological Survey, Story by Adam Voiland)

• May 7, 2019: Over the past 20 years, a lot of things have changed. But through all those changes, there's been the same place to find daily images of our planet: Earth Observatory. 32)

Figure 19: In two decades, NASA's daily Earth magazine has shared 15,000 images, showing the latest satellite imagery, unique visuals, global maps, and easy-to-understand data visualizations so you can have a better understanding of our dynamic planet (video credit: NASA Earth Observatory: where every day has been Earth Day since April 1999) 33)

• April 23, 2019: In October 1946, thirteen engineers arrived at a small U.S. Army air field at the edge of a dry lake bed in Southern California to work on the experimental X-1 supersonic aircraft. They were some of the first people to work at what would became Armstrong Flight Research Center, one of NASA's centers for flight research and operations. 34)

- The site for Armstrong and the surrounding Edwards Air Force Base was chosen because Rogers Dry Lake offers an expanse of land so smooth and flat that it can be used for emergency landings. As Armstrong has been a major site for testing experimental aircraft, the natural runways on the lake bed have saved hundreds of lives and many aircraft.

- Formerly known as Dryden Flight Research Center, Armstrong has been the site of several aviation firsts. In 1947, the X-1 became the first piloted aircraft to go faster than the speed of sound. Armstrong also developed the hypersonic X-15, a rocket-powered plane that holds the record for being the fastest manned aircraft to ever fly. Armstrong tested the first electronically controlled aircraft—the F-8 DFBW—in 1972. And for many years, Armstrong hosted two extensively modified Boeing 747s that carried the Space Shuttle.

- Several aircraft based at Armstrong have played key roles in studying Earth. The modified DC-8 jetliner flies a variety of earth science missions, such as Operation IceBridge. The high-altitude ER-2 carries science instruments that have collected data on the ozone hole, hurricanes, and wildfires. Other Armstrong-based aircraft that conduct Earth science research include a Gulfstream C-20A, a B200 King Air, an autonomous Global Hawk, and a remotely piloted Ikhana Predator B.


Figure 20: OLI on Landsat-8 captured this image of Edwards Air Force Base and Armstrong Flight Research Center on October 18, 2018. The image showcases the world's largest compass rose, which was placed there to help pilots land even if navigational equipment fails. Several "drawn on" runways are also visible crisscrossing the surface of the dry lake. The main concrete runway at Edwards Air Force Base, in combination with the lakebed, offers pilots one of the longest and safest runways in the world (image credit: NASA Earth Observatory, images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Adam Voiland)

• On 8 April 2019, the Landsat-8 satellite acquired a scene of contrasts: a fire surrounded by ice. Between chunks of frozen land and lakes in the Magadan Oblast district of Siberia, a fire burned and billowed smoke plumes that were visible from space. 35)

- Not much is known about the cause of the fire. Forest fires are common in this heavily forested region, and the season usually starts in April or May. Farmers also burn old crops to clear fields and replenish the soil with nutrients; such fires occasionally burn out of control. Land cover maps, however, show that this fire region is mainly comprised of shrublands, not croplands.


Figure 21: This image and the image of Figure 22 show the fire east of the town of Evensk, as observed by OLI (Operational Land Imager) on Landsat-8 of NASA. The satellite imagery indicates that the fire has been burning since at least April 6. According to Russia's Federal Forestry Agency, one fire of nearly 4,000 hectares (10,000 acres) was reported on 8 April on forest lands in the Magadan Oblast region (image credit: NASA Earth Observatory, image by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Text by Kasha Patel)


Figure 22: Overview of the Magdan Oblast region with the town of Evensk in the far east of Siberia, 8 time zones east of Moscow (image credit: NASA Earth Observatory, image by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Text by Kasha Patel)

• April 2, 2019: As the world's earliest known civilization developed in Mesopotamia... as Genghis Khan worked to create the largest contiguous land empire in history... as the Ottomans occupied European and Asian lands for nearly 600 years... each empire had one thing in common. They all set up camp on a small plot of land in what is now the Kurdistan region of northern Iraq: the Erbil Citadel. 36)

- The Citadel is possibly the oldest continuously occupied human settlement on Earth, dating back at least 6,000 years. Its extensive history is embedded in its own ground. It sits on an oval-shaped mound that stands about 32 meters (100 feet) high, built up from dirt, debris, and collapsed mud houses from previous human settlements. This ancient town within the heart of Erbil was added to the World Heritage List in 2014. It covers just over 10 hectares (24 acres).

- The Citadel is today surrounded by tall 19th-century walls, which once gave it an appearance of a formidable fortress. Within those walls, a maze of narrow alleyways and culs-de-sac branch out from the main gate and connect courtyard houses and public buildings—street patterns carried over from the Ottoman period.


Figure 23: This image of Erbil Citadel and its surroundings was acquired on November 20, 2018, by OLI on the Landsat-8 satellite. From above, Erbil Citadel appears at the center of what looks like a wagon wheel—perhaps more than a coincidence, as evidence suggests humans may have been living there during the Ubaid period, when humans invented the wheel. The Citadel is surrounded by the capital of the Iraqi Kurdistan autonomous region (image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kasha Patel)

- The Citadel is today surrounded by tall 19th-century walls, which once gave it an appearance of a formidable fortress. Within those walls, a maze of narrow alleyways and culs-de-sac branch out from the main gate and connect courtyard houses and public buildings—street patterns carried over from the Ottoman period.

- Today, only one family lives within the walls, an arrangement by Kurdish officials in order to preserve the Citadel's title of "continuously occupied." The town currently contains one mosque and various museums open for business. Several organizations are working to rehabilitate and bring new activities to the Citadel.

• March 26, 2019: The honking, fluttering spectacle of tens of thousands of snow geese in flight is a breathtaking sight—like watching "snowflakes drifting lazily across the azure sky," in the words of naturalist and historian George Bird Grinnell. It is also a sight that would be far less common in the Sacramento Valley if the region was not one of the largest rice-growing areas in the United States. 37)

- In Grinnell's day, the meandering Sacramento River wound through marshy wetlands in the valley, becoming what amounted to an inland sea during big winter and spring floods. Sacramento has the scars to prove it; the city has routinely suffered through devastating floods since the 1840s.

- Decades of development and levee construction eventually tamed the worst of the flooding, but it took the wetlands with it. According to one estimate, the Sacramento Valley lost more than 90 percent of its wetlands to farming and development.

- However, one crop—rice—helps preserve some of the valley's watery history. Growing rice requires the flooding of fields for several months in the summer. And since crop burning was restricted by the state of California in the 1990s, many rice growers flood their fields in winter to soften the stubble and makes it easier to till in the spring. This has extended the period when standing water covers parts of the Sacramento Valley to about eight months, explained Daniel Sousa, a researcher at Columbia University working on a project to monitor rice farming by satellite.


Figure 24: OLI on Landsat-8 acquired this false-color image using a combination of shortwave infrared, near infrared, and visible light (bands 6-5-4) on 26 December 2018. The image highlights the patchwork of flooded rice fields along the Sacramento and Feather Rivers. Inundated fields appear dark blue; vegetation is bright green. A series of raised levees form the grid pattern between the fields (image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey, story by Adam Voiland)

- Note that some of the flooded fields may be flooded due to winter rains. Though rainfall was below average in December 2018, water tends to pool up easily in the clay-rich, impermeable soils in this area after storms. Circular Sutter Buttes, an eroded volcanic lava dome, is visible in the center of the image. Some of the flooded areas, particularly just north of Sutter Buttes, are marshland. These areas generally appear darker than the rice fields.


Figure 25: This photo shows a massive flock of snow geese congregating in a rice field in the Sacramento Valley on February 22, 2014, an unusually dry year (image credit: NASA Earth Observatory, Photographs by Leslie Morris and Brian Baer for the California Rice Commission (used with permission). Story by Adam Voiland)

- Rice fields provide food and a resting place for nearly 230 wildlife species. According to the California Rice Commission, they are the source of 60 percent of the food for 7 to 10 million ducks and geese that migrate along the Pacific Flyway each winter.

- "We often hear of land use being at odds with the natural rhythms and legacy of ecosystems," said Sousa. "This is a nice case where farming rice on a large scale is actually returning the landscape to a more natural state."


Figure 26: The photo below shows a large flock of geese in rice fields near Willow, California, on 13 December 2012. The largest flocks of birds congregate in rice fields during dry years, explained Jim Morris of the California Rice Commission. Snow geese typically spend winters in southern California and summers in Canada and Alaska (image credit: NASA Earth Observatory, Photographs by Leslie Morris and Brian Baer for the California Rice Commission (used with permission). Story by Adam Voiland)

- Landsat 8 is especially useful for tracking rice crops because its Thermal Infrared Sensor (TIRS) can distinguish the cool temperatures of a flooded field from the warmer temperatures of dry land. By analyzing Landsat thermal imagery of flooded fields and comparing it to ecological data on the resting and feeding patterns, researchers found that late-March and April—peak migration season—are particularly difficult periods for migrating birds. The amount of flooded habitat has been shrinking during the past 30 years. As little as 3 percent of the landscape is flooded during April, according to one recent study.

- BirdReturns, a program managed by The Nature Conservancy and California Rice Commission that pays some farmers to keep fields flooded during this key part of the year, helps address this problem. Fields participating in the program since 2014 have had 40 times more birds in March than neighboring fields, according to The Nature Conservancy.

Figure 27: NASA Landsat Helps Feed the Birds: Over the last century, California's Central Valley has lost 95% of the wetlands habitat, which is needed for the shorebirds while on their migration. The solution involves big data, binoculars and rice paddies. The Nature Conservancy of California has in innovated program called Bird Returns that works with rice farmers to create temporary wetlands just during the weeks that they are needed (video credit: NASA Goddard)

• March 19, 2019: Although floods hit Nebraska the hardest, March 2019 brought unusually swollen, ice-choked rivers to Iowa as well. With the state blanketed with snow and just beginning to thaw out from an unusually cold February, many of Iowa's waterways were capped with thick ice in the beginning of the month. 38)

- After an intense "bomb cyclone" delivered heavy rain and a blast of warm air, the ice began to break up and flow downstream. However, ice chunks tend to clump together in sharp meanders or behind bridges, slowing the flow of water and creating "ice jams." In addition to exacerbating flooding, ice jams can cause significant damage as chunks of ice grind against levees, dams, homes, and other infrastructure.

- OLI on Landsat-8 acquired this false-color image (bands 6-5-4) of flooding around Des Moines on March 18, 2019. For comparison, the second image shows the same area in March 2018. Large amounts of water (dark blue) and ice (light blue) had backed up behind the Saylorville and Red Rock dams. Of the waterways in the Des Moines area, the Raccoon River faced some of the most serious flooding, partly because its sharp meanders make it especially prone to ice jams.


Figure 28: OLI image of Des Moines, Iowa region, acquired on 15 March 2018 (image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Adam Voiland)


Figure 29: OLI image of Des Moines, Iowa region, acquired on 18 March 2019 (image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Adam Voiland)

- The image of Figure 30, acquired by Landsat on the same date, shows an ice jam in the Iowa River near Iowa Falls. Volunteers came together to build sandbag barriers around wells and other key infrastructure as floodwater and ice chunks as large as dinner tables invaded roads and yards in the town, according to local news reports.


Figure 30: Landsat image showing an ice jam in the Iowa River near Iowa Falls acquired with OLI on 18 March 2019 (image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Adam Voiland)

• March 12, 2019: A major part of Brazil's economy depends on a grass-fed animal—the cow. Between 1970 and 2006, Brazil doubled its cattle farm productivity, increasing income and helping with global food demands. Today, it is one of the largest cattle producers in the world. But cattle production has come with an environmental cost: the creation of pastures is a main driver for Amazon deforestation. 39)

Figure 31: Pastureland in Brazil between 1982 and 2018, nearly 31% of Brazilian territory was used as pastureland (video credit: Joshua Stevens, using data courtesy of Arantes, A. E., et al. (2018), story by Kasha Patel)

- A major part of Brazil's economy depends on a grass-fed animal—the cow. Between 1970 and 2006, Brazil doubled its cattle farm productivity, increasing income and helping with global food demands. Today, it is one of the largest cattle producers in the world. But cattle production has come with an environmental cost: the creation of pastures is a main driver for Amazon deforestation.

- Researchers have recently been using NASA satellite data to monitor the spread and quality of pasturelands and to estimate how many cattle those lands can support. The recent study shows that many existing pasturelands can actually sustain more cattle.

- "The main goal of our work is to produce more meat per hectare," said Laerte Ferreira of the Federal University of Goiás (Brazil), whose work has been supported by the Gordon and Betty Moore Foundation and is part of the MapBiomas initiative. "If we can improve the use of these pastures, we can avoid more deforestation and promote livestock in a more sustainable way."

- The animation of Figure 31 shows the expansion of Brazilian pasturelands from 1985 to 2017; it is based on an analysis of more than 200,000 Landsat images by Ferreira's group. Note how pasturelands have expanded towards the Amazon in the north. Approximately 264 million hectares (652 million acres) were mapped as pasture sometime between 1985 and 2017, which is around 31 percent of Brazilian territory. (This analysis does not account for losses of pastureland.)

- As it turns out, many of these pasturelands are older or not well maintained, which leads to faster degradation. Degraded pastures tend to have bare soil, more invasive species, and less nutrients to support livestock. They also retain less carbon in the soil and contribute more to greenhouse gas emissions.


Figure 32: This map of 2015 highlights areas where the land can support more cattle. Most noticeably, the pastures in the Pampas region in the south can support as much as three times more cattle per hectare. (One cattle unit is estimated at 450 kilograms, or 990 pounds, per hectare.) The information was calculated by estimating the amount of cows on the land, the vegetative health of the pasturelands as observed from the Normalized Difference Vegetation Index (NDVI) and primary productivity values from NASA satellites, and a typical cow's foraging needs. These maps provide only an estimation, as limitations of soil, topography and infrastructure need to be considered (image credit: NASA Earth Observatory, image by Joshua Stevens, using data courtesy of Arantes, A. E., et al. (2018), story by Kasha Patel)

- Ferreira's team found that the northeastern and center-west portions of the country showed the most land degradation. Several other regions showed healthy pasturelands, including many that are underutilized.

- About 45 percent of the pastures show signs of degradation, said Ferreira, but the government is sponsoring plans to revive those lands. "If we can restore the degraded pasture and bring back livestock, we can avoid more deforestation," said Ferreira.

- Brazil's Low-Carbon Agriculture plan aims to rehabilitate 15 million hectares of degraded pastures and introduce less invasive agricultural practices by 2020. The plan will not only make these lands more viable for cattle grazing, but also greatly reduce greenhouse gas emissions.

• March 11, 2019: It's reasonable to think that Jupiter—a gaseous planet more than 11 times the diameter of Earth—would have little in common with our home. But it turns out that the motion of fluids on both planets is governed by the same laws of physics. An eddy on Earth looks a lot like an eddy on Jupiter. 40)

- Scientists think Jupiter has three distinct cloud layers. The left image of Figure 33 shows ammonia-rich clouds swirling in the planet's outermost layer. Citizen scientists Gerald Eichstädt and Seán Doran created the image using data acquired by the JunoCam imager on NASA's Juno spacecraft in December 2018. They applied a series of image processing steps to highlight details that would be difficult for the human eye to discern.

- According to Alberto Adriani, a Juno mission co-investigator from the Institute for Space Astrophysics and Planetology, the eddies in Jupiter's clouds reflect disturbances in the atmosphere caused by the planet's fast rotation and by higher temperatures deeper in the atmosphere. He compares the phenomenon to rapidly rotating a fluid while boiling it.


Figure 33: The similarities are especially evident in these images showing swirls in Jupiter's atmosphere and in Earth's Baltic Sea. "This is all about fluids moving around on a rotating body," said Norman Kuring of NASA's Goddard Space Flight Center. Kuring described the patterns of flow as a combination of laminar (following a smooth path) and turbulent (uneven and chaotic). Flows can be characterized using numbers named for famous physicists, such as Reynolds, Rossby, and Rayleigh. But you don't need a textbook knowledge of fluid dynamics to appreciate its consequences. "Out of all the complexity flows beauty, whether it be images of Earth, Jupiter, or your coffee cup when you pour in the cream," Kuring said (image credit: NASA Earth Observatory image by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Jupiter Juno imagery courtesy of NASA/SwRI/MSSS via Gerald Eichstädt and Seán Doran, story by Kathryn Hansen)

- The patterns in Jupiter's atmosphere appear similar to those in Earth's oceans. The Operational Land Imager (OLI) on Landsat-8 acquired the right image of Figure 33 on July 18, 2018. This natural-color image shows a green phytoplankton bloom tracing the edges of a vortex in the Baltic Sea. In this medium—Earth's ocean—turbulent processes are important for moving heat, carbon, and nutrients around the planet. Models that accurately represent these processes are critical for understanding weather in the air and sea.

- While scientists continue exploring the complexities of Earth's oceans, astronomers are learning more about Jupiter's complex composition—important for understanding how our solar system and other solar systems formed.

- "In interpreting what we see elsewhere in the solar system and universe, we always compare with phenomena that we already know of on Earth," Norman Kuring said. "We work from the familiar toward the unknown."

• March 6, 2019: As another round of severe rainstorms doused California in late February 2019, the Russian River approached record levels and brought catastrophic flooding. More than 2,000 businesses and homes in Sonoma County were flooded and the river valley towns of Guerneville and Monte Rio were turned into islands, temporarily cut off from all land transportation. 41)

- Scientists from the National Weather Service and the Scripps Institution of Oceanography declared an atmospheric river event, one of several that has brought soaking rain and heavy snowfall to California this winter. Off the West Coast of the United States, an atmospheric river is often referred to as a "pineapple express," because the storm systems and moisture often flow from the tropical Pacific near Hawaii. Such jets of moist air can run into low-pressure weather systems and deliver bursts of precipitation for days at a time.

- Meteorologists reported widespread rainfall totals above 5 inches (13 cm), with the town of Venado, California, seeing 21.36 inches (54.25 cm) in 48 hours. According to a report from NBC Bay Area meteorologist Jeff Rainieri, the mountains around Guerneville saw February rainfall that was more than 400 percent of normal. The Russian River crested at 45.38 feet (~14 m) in Guerneville, the highest level since 1995.


Figure 34: On 28 February 2019, OLI on Landsat-8 acquired a false-color view (bands 6-5-4) of flooding along the Russian River. It shows flood water west of Santa Rosa, near a point where the river takes a hard turn to the west toward Sebastopol and Guerneville (both under cloud cover). For comparison, the left image shows the area on January 27, 2019. Flood waters appear blue; vegetation is green; and bare ground is brown (image credit: NASA Earth Observatory, images by Lauren Dauphin, using Landsat data from the USGS, story by Mike Carlowicz)

• March 4, 2019: More than two centuries ago, Captain James Cook sailed around the Antarctic circle searching for the Southern Continent. Instead, he landed on an isolated island about 1,300 kilometers (800 miles) southeast of the Falkland Islands in the Southern Ocean. He became the first recorded explorer on the remote island, which he claimed for Great Britain and named the "Isle of Georgia" for King George III. 42)

- But the island was "savage," according to Cook. As he described in a manuscript, "Pieces were continually breaking off, and floating out to sea; and a great fall happened while we were in the bay, which made a noise like a cannon. The inner parts of the country were not less savage and horrible." Cook began mapping the coastline, but did not bring the ship into the island due to the dangerous conditions.

- South Georgia is still known for its rugged terrain and inhospitable environment for humans. The island has 11 peaks rising more than 2,000 meters (6,500 feet) above sea level. The mountains shield the north and east coast from prevailing winds that blow out from the Southern Ocean and Antarctica. The island also supports 161 glaciers, several that are in retreat.


Figure 35: OLI on Landsat-8 acquired this image on December 25, 2018 showing a clear view of South Georgia and the South Sandwich Islands (image credit: NASA Earth Observatory images by Joshua Stevens, using Landsat data from the U.S. Geological Survey; story by Kasha Patel)

- The island provides a unique ecosystem for wildlife. South Georgia waters are highly productive, supporting large populations of krill, which feed on phytoplankton and provide food for many marine predators. The steep terrain above and below the water line includes deep bays that shelter substantial populations of penguins, seals, and the globally threatened wandering albatross. Scientists have collected more than 30 years of population data on seabirds and marine mammals at South Georgia—one of the longest and most detailed scientific datasets in the Southern Ocean.


Figure 36: This image gives a sense of the topography by overlaying the Landsat-8 data of 25 December 2018 on a digital elevation model from the Shuttle Radar Topography Mission (SRTM). The Novosilski and Brøgger Glacier are approximately 13 km and 11 km long, respectively (image credit: NASA Earth Observatory images by Joshua Stevens, using Landsat data from the U.S. Geological Survey; story by Kasha Patel)


Figure 37: Detail image of the southern tip of the island. The discolored water near the shore is possibly due to phytoplankton blooms or sediments from the island mixing into the sea. The southernmost point has the quirky name "Cape Disappointment," a label given by Cook when he realized he had not reached Antarctica (image credit: NASA Earth Observatory, image by Joshua Stevens, using Landsat data from the U.S. Geological Survey, story by Kasha Patel)

• February 27, 2019: In December 2016, the Menindee Lakes of New South Wales were nearly brimming with water. More than two years later, these Australian lakes are almost desiccated. 43)

Figure 38: These satellite images show the dwindling water levels of the Menindee Lakes, a chain of freshwater lakes located 110 kilometers (70 miles) southeast of Broken Hill. The shallow natural depressions were developed into water storage by the Australian government to manage river flows. The images were acquired by the Operational Land Imager on Landsat-8 on January 27, 2017, February 15, 2018, and February 2, 2019 (image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kasha Patel)

- Water levels often fluctuate as the basins collect precipitation or flood water. Evaporation accounts for about 400 gigaliters of water loss from the lakes every year. Other times the water is released into the nearby Darling River by the New South Wales government. During drought, when less water is coming into the lakes, the basins tend to be drier.

- Lake Menindee is the largest of the lakes. But river managers have been keeping as much water as possible upstream in Lake Wetherell and Lake Panamaroo, which supply water to Broken Hill and local communities.

- Recent years have brought exceptional drought to the area. New South Wales has faced extremely hot temperatures and low precipitation, causing one of its worst droughts on record.

- The Lower-Darling River has been experiencing "extreme low inflows" of water from the Menindee Lakes since August 2018. As of 18 February 2019, the Lower-Darling's storage level was 1 percent. Water has stopped flowing in parts of the river.

- Public concerns drastically increased when millions of fish were found floating belly up along the Darling River on three separate occasions in January 2019. The massive fish kills stemmed from a series of events. As water levels fell, the river stopped flowing, and temperatures were high—creating ideal conditions for blue-green algae to bloom. When a cold front swept through the area and killed the algae, the population of bacteria that feeds on the algae then blossomed. The bacteria consumed most of the available oxygen in the water, causing the fish to suffocate. Some sources say the massive fish kills were partly due to how the Menindee Lakes are managed, while others blame global warming and drought.

• February 23, 2019: If you were to stand in the middle of the mines of Brazil's Carajás Mountains (Serra dos Carajás), the dusty red terrain could be mistaken for a Martian landscape. Yet in the images above, indicators of human presence are everywhere. Excavator trucks dig in the deep pits, while off-road trucks move hundreds of tons of ore along dirt roads. This is among the world's largest iron ore mining operations. 44)

- Viewed from space, you get a sense of how the Carajás mines fit into the wider landscape of the Amazon forest. In a scene acquired by the Operational Land Imager (OLI) on Landsat-8 on July 16, 2018, the red-brown earth contrasts starkly with the greens of the surrounding Carajás National Forest.

- The detailed image shows the largest of these mines, the Serra Norte complex. The terraced appearance is a result of the open pit mining method, in which layers are excavated one at a time. Nine iron ore deposits exist along the Serra Norte (Northern Range). According to a 2013 study, mining at four of Serra Norte's main pits had produced 1.2 billion tons of high-grade iron ore.

- Most of the metallic mineral deposits among the ridges and plateaus of the Carajás Mountains and elsewhere in the Amazon are found in areas of rock that date back to the earliest part of Earth's history. From the time that Earth's surface solidified to about 570 million years ago, in the Precambrian, metals could more easily rise from deep in the planet and close to the surface. In addition to iron ore, the region is also rich in manganese, copper, tin, aluminum, and gold.

- Scientists have been working to better assess how mining affects deforestation of the Amazon—the world's largest remaining tropical forest—as mineral production has increasing value to the Brazil's economy.


Figure 39: The red-brown earth exposed by open pit mines contrast with the greens of the surrounding Amazon forest. The image was acquired on 16 July 2018 with OLI (image credit: NASA Earth Observatory, image by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen)


Figure 40: Overview of a Brazilian mine region in Amazon Forest acquired on July 16 2018 with OLI ((image credit: NASA Earth Observatory, image by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen)

• February 20, 2019: Cracks growing across Antarctica's Brunt Ice Shelf are poised to release an iceberg with an area about twice size of New York City. It is not yet clear how the remaining ice shelf will respond following the break, posing an uncertain future for scientific infrastructure and a human presence on the shelf that was first established in 1955. 45)

- The cracks are apparent by comparing these images acquired with Landsat satellites. The Thematic Mapper (TM) on Landsat-5 obtained the image of Figure 41 on 30 January 1986. The image of Figure 42, from the Operational Land Imager (OLI) on Landsat-8, shows the same area on 23 January 2019.


Figure 41: Antarctica's Brunt Ice Shelf acquired with the Thematic Mapper (TM) on Landsat-5 on 30 January 1986 (image credit: NASA Earth Observatory image by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen, with image interpretation by Chris Shuman)


Figure 42: Antarctica's Brunt Ice Shelf acquired with OLI on Landsat-8 on 23 January 2019. Cracks growing across the ice shelf are poised to release an iceberg about twice size of New York City (image credit: NASA Earth Observatory image by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen, with image interpretation by Chris Shuman)

- The crack along the top of the 23 January 2019 image of Figure 42 —the so-called Halloween crack—first appeared in late October 2016 and continues to grow eastward from an area known as the McDonald Ice Rumples. The rumples are due to the way ice flows over an underwater formation, where the bedrock rises high enough to reach into the underside of the ice shelf. This rocky formation impedes the flow of ice and causes pressure waves, crevasses, and rifts to form at the surface.


Figure 43: The detailed view of Figure 42 shows this northward expanding rift coming within a few kilometers of the McDonald Ice Rumples and the Halloween crack (image credit: NASA Earth Observatory image by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen, with image interpretation by Chris Shuman)

- When the Halloween crack cuts all the way across, the area of ice lost from the shelf will likely be at least 1700 km2 (660 square miles). That's not a terribly large iceberg by Antarctic standards—probably not even making the top 20 list. But it may be the largest berg to break from the Brunt Ice Shelf since observations began in 1915. Scientists are watching to see if the loss will trigger the shelf to further change and possibly become unstable or break up.

- "The near-term future of Brunt Ice Shelf likely depends on where the existing rifts merge relative to the McDonald Ice Rumples," said Joe MacGregor, a glaciologist at NASA's Goddard Space Flight Center. "If they merge upstream (south) of the McDonald Ice Rumples, then it's possible that the ice shelf will be destabilized."

- The growing cracks have prompted safety concerns for people working on the shelf, particularly researchers at the British Antarctic Survey's Halley Station. This major base for Earth, atmospheric, and space science research typically operates year-round, but has been closed down twice in recent years due to unpredictable changes in the ice. The station has also been rebuilt and relocated over the decades. The detailed image (Figure 43) shows the station's location (Halley IV) until it was closed in 1992. In 2016-2017, the Halley VI station was relocated to a safer location (Halley VIa) upstream of the growing crack.

- Calving is a normal part of the life cycle of ice shelves, but the recent changes are unfamiliar in this area. The edge of the Brunt Ice Shelf has evolved slowly since Ernest Shackleton surveyed the coast in 1915, but it has been speeding up in the past several years.

- "We don't have a clear picture of what drives the shelf's periods of advance and retreat through calving," said NASA/UMBC glaciologist Chris Shuman. "The likely future loss of the ice on the other side of the Halloween Crack suggests that more instability is possible, with associated risk to Halley VIa."

• February 11, 2019: About 13 percent of the Alaskan landscape has changed in some way in recent decades. That's the conclusion of a 2018 study led by Neal Pastick, who examined historical aerial photos and satellite images to identify areas of ecological change across the state. 46)

- "We set out to characterize all of these changes by using remote sensing data from the past 32 years," said Pastick, a physical scientist and contractor to the U.S. Geological Survey Earth Resources Observation Science Center. "It's striking how much change is happening, and what we can do with the Landsat satellites to link these changes into one cohesive story."

- The most notable changes in Alaska include the browning of land from forest fires and the greening of the land as vegetation regrows. Pastick also documented vast areas of land affected by erosion, including segments of coastline that have dramatically changed shape.


Figure 44: This image of the north slope of Alaska was acquired by the Landsat-8 satellite on October 5, 2018 [image credit: NASA Earth Observatory image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey and NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) annual trend data courtesy of Neal Pastick. Story by Kathryn Hansen]


Figure 45: This image of the north slope of Alaska was acquired by the Landsat 4 satellite on July 8, 1992. Note that changes in ice cover between images reflect a long-term trend, but also simple seasonal differences (image credit: NASA Earth Observatory image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey and NDVI and NDWI annual trend data courtesy of Neal Pastick. Story by Kathryn Hansen)

- Warming temperatures have played a major role in the coastline retreat. Melting permafrost leaves the ground less stable and more prone to being washed away by heavy rains and pounding ocean waves. Rising temperatures cause the protective sea ice cover to disappear for longer periods each spring and summer, meaning waves and Arctic storms can more easily chew away at the coastline. No one lives along the receding coastline shown above, but coastal communities elsewhere in Alaska have had to make decisions about how to deal with failing infrastructure as permafrost melts and coastlines retreat.


Figure 46: Pastick and colleagues used satellite and aerial photograph comparisons to verify the type of ecological change responsible for broader trends in the landscape. For example, this map shows the median change in the landscape's wetness per year between 1 January 1984 and 31 December 2015. Analyzing all of the information together led them to conclude that 174,000 square miles (451,000 km2) of the Alaskan landscape has undergone change (image credit: NASA Earth Observatory image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey and NDVI and NDWI annual trend data courtesy of Neal Pastick. Story by Kathryn Hansen)

- More work is needed in order to identify how the changing landscape will continue to affect communities, according to Pastick. He added: "I want to understand how these evolving systems might change in the future, and what that change means for people who live there."

• February 11, 2019: Today marks the 5th anniversary of the launch of Landsat-8 from Vandenberg Air Force Base in California. A joint NASA and USGS mission, Landsat-8 adds to the longest continuous data record of Earth's surface as viewed from space. Since launch, Landsat-8 has completed over 25,500 Earth orbits, traveled over 700 million miles, and contributed over 1.1 million scenes to the USGS Landsat archive. 47)

- Since 2013, Landsat 8 data has been cited in over 600 peer reviewed journal publications supporting studies from agriculture, forest and water quality/use/management, to natural disaster support, and mapping and monitoring land change, to name a few.

• February 10, 2019: With the International Energy Agency forecasting that the number of electric motor vehicles will skyrocket from about 3 million in 2018 to 125 million by 2030, it is a good bet that more of these vividly colored evaporation ponds will dot deserts in South America in the future. That's because they are a key source of lithium. 48)

- Makers of electric vehicles, laptops, cell phones, and other gadgets rely heavily on lithium for rechargeable batteries. Lithium is also used in ceramics, glass, industrial grease, and medication. With 29 percent of the world's reserves, Chile's Salar de Atacama—an enclosed basin with no drainage outlets—is the world's largest source of lithium. Nearby areas in Bolivia and Argentina also have large reserves.

- While the surface of the salar (Spanish for salt flat) is almost always dry, a large reserve of lithium-rich brine lurks below the surface of the ancient sea bed. A combination of snowmelt from nearby mountains and hydrothermal fluids associated with volcanic activity naturally replenishes the aquifer.


Figure 47: On November 4, 2018, OLI (Operational Land Imager) on Landsat-8 captured this image of evaporation ponds. Color variations in the ponds are due to varying concentrations of salt in the water; lighter blue ponds have higher concentrations of lithium. The network of canals and hundreds of pumps around the evaporation ponds form the grid patterns around the clusters of ponds (image credit: NASA Earth Observatory image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Adam Voiland)

- As the number of ponds at Salar de Atacama increases over time to meet the rising demand for lithium, there are concerns and conflicts brewing about how much groundwater companies operating in this area are using.


Figure 48: Overview of Chile's Salar de Atacama with the world's largest reserve of lithium, which is a key ingredient in rechargeable batteries ((image credit: NASA Earth Observatory image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Adam Voiland)

• February 5, 2019: Two hundred million years ago, dinosaurs roamed the land that is now northwest Argentina. Today, those prehistoric reptiles linger as some of the world's oldest and most pristine fossils—spanning 50 million years from when dinosaurs first appeared to when they rose to dominance in the Triassic era. 49)

- Like a South American version of Monument Valley in the United States, Talampaya Park is known for its 200-meter high red sandstone cliffs and 1,500-year-old rock carvings. The image of Figure shows a close-up view of the park in La Rioja province, where the aptly named herbivorous dinosaur Riojasaurus was discovered.


Figure 49: This image shows two notable dinosaur habitats, the Talampaya and Ischigualasto Natural Parks, located near the Argentina-Chile border. This image was acquired by the OLI (Operational Land Imager) by Landsat-8 on August 25, 2018. Together, the parks cover more than 275,000 hectares (2750 km2), image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kasha Patel.

- Talampaya stands in stark contrast to the white and multicolor sediments of the Ischigualasto Provincial Park to the south. Ischigualasto is often called the Valle de la Luna ("Valley of the Moon") because its unique and rugged terrain give an otherworldly appearance.

- Now a basin of sandstone and mudstone, Ischigualasto used to be a volcanically active floodplain with rivers and strong seasonal rainfall. About 230 million years ago, during the age of the dinosaurs, rock deposits filled the basin and fossilized the surrounding flora and fauna. In Ischigualasto, paleontologists have discovered a completely intact dinosaur skull of the Herrerasaurus, as well as the Eoraptor lunensis, one of the most primitive dinosaurs discovered to date.


Figure 50: Image of a portion of Ischigualasto Provincial Park, acquired by OLI on 25 August 2018 (image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kasha Patel)

- Today, the parks are arid scrub deserts located in the Argentine Monte, one of the driest regions in the country. Both parks are listed as UNESCO World Heritage sites because of their archaeological significance.

• February 2, 2019: For the first time in perhaps a decade, Mount Etna experienced a "flank eruption"—erupting from its side instead of its summit—on 24 December 2018. The activity was accompanied by 130 earthquakes occurring over three hours that morning. Mount Etna, Europe's most active volcano, has seen periodic activity on this part of the mountain since 2013. 50)


Figure 51: OLI on the Landsat-8 satellite acquired this image of Mount Etna on 28 December 2018 (image credit: NASA Earth Observatory, image by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Text by Kasha Patel)

- Ash spewing from the fissure cloaked adjacent villages and delayed aircraft from landing at the nearby Catania airport. Earthquakes occurred in the subsequent days after the initial eruption and displaced hundreds of people from their homes, according to news reports.


Figure 52: This image highlights the active vent and thermal infrared signature from lava flows, which can be seen near the newly formed fissure on the southeastern side of the volcano. The image was created with data from OLI (bands 4-3-2) and TIRS (Thermal Infrared Sensor) on Landsat-8 (image credit: NASA Earth Observatory, image by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Text by Kasha Patel)

Minimize Landsat-8 Continued

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. 51)

• 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. 52)

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 53 and 54 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 54 are simply not visible in the natural color image of Figure 53. This new analysis feature will give scientists a better handle to study the changing environment.


Figure 53: Natural color image of the Aral Sea region observed on March 24, 2013 (image credit: NASA)


Figure 54: 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. 53) 54) 55) 56)

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. 57) 58)


Figure 55: 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 56).


Figure 56: This thermal image was taken by the TIRS instrument on April 29, 2013 (image credit: USGS, NASA)

Legend to Figure 56: 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. 59)

• 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. 60) 61)

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


Figure 57: 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. 61)

Legend to Figure 57: 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. 62)

• 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. 63)

- 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.


Figure 58: First image of LDCM released in March 2013 (image credit: NASA) 64)

Legend to Figure 58: 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. 61).

• 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. 65)

• 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).



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). 66) 67)

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.


Figure 59: 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: 68)

• 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


Radiance (W/m2 sr μm), typical



New Deep Blue


Aerosol/coastal zone

30 m









30 m
(TM heritage bands)






























Minerals/litter/no scatter






Image sharpening

15 m






Cirrus cloud detection

30 m



Table 2: 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).


ETM+ (Landsat-7)

Band Nr

Wavelength (µm)

GSD (m)

Band No.

Wavelength (µm)

GSD (m)

8 (PAN)

0.500 - 0.680


8 (PAN)

0.52 - 0.90



0.433 - 0.453






0.450 - 0.515



0.45 - 0.52



0.525 - 0.600



0.53 - 0.61



0.630 - 0.680



0.63 - 0.69






0.78 - 0.90



0.845 - 0.885






1.360 - 1.390






1.560 - 1.660



1.55 - 1.75



2.100 - 2.300



2.09 - 2.35


OLI does not include thermal imaging capabilities

6 (TIR)

10.40 - 12.50


Figure 60: Spectral parameter comparison of OLI and ETM+ instruments


Figure 61: OLI and ETM spectral bands (image credit: NASA)

OLI instrument:

The OLI design features a multispectral imager with a pushbroom architecture (Figure 62) 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.. 69) 70) 71) 72) 73)


Figure 62: 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 63). 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 64) 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.


Figure 63: Schematic view of the FPM layout concept (image credit: BATC, USGS)


Figure 64: 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


- 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


- 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 3: 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. 74)

OLI calibration:

The OLI calibration subsystem (Figures 65 and 66) 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). 75) 76)

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. 75):

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.


Figure 65: OLI block diagram illustrating the calibration subsystem in front of the telescope (image credit: NASA, BATC)


Figure 66: Blow-up of the calibration subsystem illustrating the solar diffuser and shutter assemblies (image credit: NASA, BATC)


Figure 67: Illustration of the OLI instrument (image credit: NASA, BATC)

In Nov. 2008, the OLI instrument passed the ICDR (Instrument Critical Design Review). 77)


Figure 68: 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. 78) 79)

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.


Figure 69: Functional block diagram of TIRS (image credit: NASA, Ref. 76)

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). 80) 81) 82)

Instrument type

Pushbroom imager

Two channel thermal imaging instrument

10.8 and 12.0 µm band centers


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


- 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


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

Telescope f number


Data quantization

12 bit

Instrument mass, size, power

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

Table 4: 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. 81).

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. 83)

Advantages of QWIP technology:

- Large lattice-matched substrates

- Mature materials technology

- No unstable mid-gap traps

- Inherently, radiation hard.


Figure 70: QWIP quantum state diagram (image credit: NASA/JPL)


Figure 71: TIRS 10-13 µm QWIP spectral response requirement (image credit: NASA)


Figure 72: Overview of the TIRS focal plane layout (image credit: NASA, Ref. 76)

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.


Figure 73: Schematic view of the FPA (Focal Plane Assembly), image credit: NASA


Figure 74: Photos of the FPA (image credit: NASA)

Legend to Figure 74: 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 75).

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.


Figure 75: The TIRS optical sensor unit concept (image credit: NASA)


Figure 76: Schematic view of the TIRS instrument internal assembly (image credit: NASA, Ref. 76)

Legend to Figure 76: 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. 84) 85)

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


Figure 77: 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.


Figure 78: Illustration of the TIRS calibration system (image credit: USGS)


Figure 79: 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. 86)

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.


Figure 80: 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


Thermal operational

0 / +20ºC stable to ±1ºC

Thermal survival range

-50 / +40ºC


3.25 years on orbit


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 5: SSM driving requirements



Landsat ETM+


GMES/Sentinel-2 MSI

Spectral bands









1 (blue)


B1 (blue)


1 (blue)


2 (blue)


B2 (blue)


2 (green)


3 (green)


B3 (green)




4 (red)


B4 (red)






B5 (red edge)






B6 (red edge)






B7 (red edge)


4 (NIR)




B8 (NIR)




5 (NIR)


B8a (NIR)






B9 (water vapor)




9 (cirrus)


B10 (cirrus)


5 (SWIR1)


6 (SWIR1)


B11 (SWIR1)


7 (SWIR2)


7 (SWIR2)


B12 (SWRIR2)







6 (TIR)


10 (TIR1)






11 (TIR2)




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)


8 bit

12 bit

12 bit

Onboard Calibration




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


Cross-track scanner (Whiskbroom)



Table 6: Comparison of Landsat and GMES/Sentinel-2 imager specifications 87)


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 81. 88)

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.


Figure 81: 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 82.

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.


Figure 82: Illustration of DRC-16 collections (image credit: USGS, NASA)

End to end mission data flow is represented in Figure83 . 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.


Figure 83: 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.



Landsat-8 / 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 : 89) 90) 91)

- 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 92)

- 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.


Figure 84: Illustration of the Landsat-8 mission elements and communication architecture (image credit: NASA) 93) 94)

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.


Figure 85: Overview of the LDCM system architecture (image credit: USGS)



Total size


Daily volume of 400 scenes

390 GByte


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 7: 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


Figure 86: Overview of the IC (International Cooperator) network (image credit: USGS, Ref. NO TAG#


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. 74).

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.


Figure 87: 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. 74).

• 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 8: Landsat 8 operational characteristics (Ref. NO TAG#

Landsat-8 reprocessing (Ref. NO TAG#:

• 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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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 (

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