Other Space Activities

GNSS Reflectometry

Measurement Types

Global Navigation Satellite System (GNSS) Reflectometry, or GNSS-R, is a novel remote sensing technique that uses reflected GNSS signals to derive surface characteristics. GNSS constellations used for this technique include the USA’s Global Positioning System (GPS), Russia’s Global Navigation Satellite System (GLONASS), the European Union’s Galileo, and China’s BeiDou.

Figure 1: Hypothetical GNSS Reflectometry Setup (Image credit: Norwegian University of Science and Technology)

When GNSS signals reflect off the Earth’s surface, the surface state affects the frequency and amplitude of the original signal. The reflected GNSS transmissions can be collected by satellites in Low Earth Orbit (LEO), and compared with the original signal to derive surface characteristics. This is known as cross-correlation, where a replica GNSS signal is compared to the reflected data, allowing signal delay from extra path length and Doppler shift from relative motion to be calculated. 1) 2) 4)

GNSS-R instruments consist solely of a receiver, as it is a passive sensing technique. This makes such missions cheaper and more reliable due to reduced complexity, as well as highly scalable for increased coverage and revisit rates.

GNSS-R is an example of a bistatic radar technique - as it requires separate transmitters and receivers. Bistatic radar is more sensitive to surface roughness than monostatic radar, where the receiver and transmitter are located in the same place, as it can observe reflections at an angle where specular, or coherent, reflection is strongest. Meanwhile, small-scale surface roughness, microscopic level surface irregularities, can affect incoherent scattering, which spreads the signal in Delay Doppler Maps (DDMs). By measuring both coherent and incoherent scattering components, variations in surface roughness can be detected. GNSS-R therefore offers a cheaper and less power-intensive alternative to traditional radar imaging missions, as well as providing increased sensitivity for surface roughness estimates. 1) 2) 3)

GNSS-R applications are largely in line with traditional active monostatic radar imaging. In a similar way to Synthetic Aperture Radar (SAR) imagery, GNSS-R can be used for environmental monitoring - to observe forest cover, study wetland dynamics, and track glacial movement. It is also applicable in disaster management, providing timely and accurate data, in agriculture for insights into crop condition and soil moisture content, and in geology and plate tectonics, where SAR interferometry can be used to generate ground deformation maps and monitor surface changes. If deployed in high-inclination orbits, GNSS-R satellites can also be used to observe a number of cryosphere parameters, such as sea-ice extent, snow depth, and ice roughness. Therefore, the propagation of cheaper GNSS-R receivers in LEO can provide enormous benefits to a range of fields at lower costs than traditional SAR satellite missions. 1) 5) 6)

Example Products

Delay Doppler Maps

Delay Doppler Maps (DDMs) are data products generated from the cross correlation process. They are two-dimensional representations of reflected GNSS signal power as a function of the signal delay and Doppler shift. Each pixel in the DDM represents the correlation between the reflected GNSS signal and the original transmission, for a specific delay and frequency shift. Interpretation of the shape and brightness of the DDM allows retrieval of properties such as surface roughness or soil moisture content, as well as wind speed, wave height, and directional sea roughness. 4) 7) 8) 9)

Figure 2: CYGNSS Delay Doppler Map of Atlantic Wind Speeds (Image credit: NASA)

Ocean Surface Wind Speed and Wave Height

GNSS-R, through the generation of DDMs, can be used to derive other data products, such as ocean surface wind speed and wave height. To obtain these, DDM Average (DDMA), the average power within a specific region of the map, and Leading Edge Slope (LES), the steepness of the leading edge of the signal, are extracted from received data. These parameters are then related to wind speed through a Geophysical Model Function (GMF), a mathematical relationship linking measurable geophysical variables, such as radar backscatter, to parameters of interest. For surface winds, the intensity of the reflected signal is a measure of the wave height of capillary waves travelling in the direction of the signal. These capillary waves are generated by local surface wind, allowing an approximation of surface wind vectors at the specular point. Wave height data products are produced from surface roughness estimations, using LES and Trailing Edge Slope (TES) derived from DDMs. 9) 10) 11) 12) 13)

Figure 3: Wind Speed Map of Hurricane Katrina from CYGNSS Data (Image credit: NASA)

Soil Moisture Content

Soil moisture content can be observed using GNSS-R receivers, as the strength and shape of the reflected signal are directly impacted by the dielectric constant of the soil. This parameter increases with soil water content, meaning higher moisture soil will have greater reflectivity. Therefore, DDM peak power increases with soil moisture content, and analysis of DDM peak power and signal arc, soil moisture content can be estimated in the top 5 cm of soil. 14) 15)

Figure 4: High Resolution Global Soil Moisture Map from SMAP (Image credit: NASA)

Related Missions

CYGNSS

The Cyclone Global Navigation Satellite System (CYGNSS) mission is a LEO constellation of eight microsatellites, launched in December 2016 and operated by NOAA and NASA. It measures ocean surface wind speeds to determine how tropical cyclones form and evolve. CYGNSS applies the GNSS-R bistatic radar concept, receiving reflected GPS signals from the Earth’s surface, and direct GPS transmissions for satellite positioning. Each CYGNSS satellite carries a single instrument, the Delay Doppler Mapping Instrument (DDMI). DDMI produces DDMs from which wind speeds can be derived. All eight CYGNSS satellites were launched on December 15, 2016, and operate in a non-sun-synchronous orbit at an altitude of 520 km, with a period of 95 minutes, and an inclination of 35°. The satellites orbit in pairs with a 12 minute orbital separation.

HydroGNSS

HydroGNSS is a planned ESA GNSS-R mission to collect data on hydrological parameters using reflected L-band GNSS signals. HydroGNSS will carry a single instrument, the HydroGNSS receiver, which will collect dual-polarisation signals at dual frequency. The mission aims to collect soil moisture content, wetland inundation, soil freeze and thaw states, and above ground forest biomass data.

MuSat

MuSat is a constellation of modular smallsats developed by Muon Space. The MuSat-2 and -3 platforms, launched in March and August 2024, respectively, carry a Signals of Opportunity (SoOpr) payload, which uses GNSS-R to produce Earth observation products. DDMs will be produces as the primary data product, and Muon Space is developing its own algorithms for derivation of soil moisture and ocean wind speed products.

Yunyao Aerospace Constellation

The Yunyao Aerospace Constellation, launched in December 2021, is a commercial constellation developed by Yunyao Aerospace, consisting of 80 nanosatellites operating in Low Earth Orbit (LEO). Each Yunyao unit carries a GNSS-R receiver, which supports multi-static observation, receiving multiple reflected GNSS signals at once.

DoT-1

The Demonstration of Technology 1 (DoT-1) mission is a technology demonstration microsatellite produced by Surrey Satellite Technology Ltd (SSTL) and launched in July 2019. DoT-1 aims to demonstrate SSTL’s new Core Data Handling System (Core-DHS). The mission includes a GNSS-R receiver, as it is much cheaper and lighter than an active instrument, and produces observation data for the technology demonstration aspect of the mission.

Spire Global Nanosatellite Constellation

Launched in August 2013, the Spire Global Nanosatellite Constellation is a commercial constellation of Lemur-2 satellites owned and operated by Spire Global Inc., and consisting of more than 180 flight units. The Lemur-2 satellites carry a GNSS-R receiver for DDM generation from reflected signals and precise orbit determination through direct GNSS signals.

3Cat

The Cube-Cat (3Cat) mission is a commercial constellation developed by the Polytechnic Institute of Catalonia. 3Cat-2, launched in August 2016, carries a GNSS-R receiver, aiming to generate altitude maps of Earth containing wind surface over sea, deforestation and soil moisture information.

References

1) Global Geodetic Observing System, “GNSS Reflectometry” URL: https://geodesy.science/item/gnss-reflectometry/

2) Stephen T. Lowe, “Earth Remote Sensing using Surface-Reflected GNSS Signals (GNSS-Reflectometry)”, URL: https://www.gps.gov/governance/advisory/meetings/2017-11/lowe.pdf

3) Amir Azemati et al., “BISTATIC SCATTERING FORWARD MODEL VALIDATION USING GNSS-R OBSERVATIONS”, URL: https://arxiv.org/pdf/1901.07188

4) Liquid Instruments, “What is cross-correlation, and how does it advance spectrum analysis?”, URL: https://liquidinstruments.com/blog/cross-correlation-and-spectrum-analysis/

5) Amy L. Parker et al., “Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia”, URL: https://www.mdpi.com/2072-4292/13/8/1422#:~:text=Hazards%20may%20be%20defined%20as,accessed%20on%2016%20March%202021).

6) Indian Institute of Remote Sensing, “Emerging Techniques and Applications in SAR Remote Sensing”, URL: https://admissions.iirs.gov.in/documents/126/126_course_flyer.pdf

7) Razal Rose et al., “The NASA CYGNSS mission: a pathfinder for GNSS scatterometry remote sensing applications:, URL: https://www.researchgate.net/publication/288451213_The_NASA_CYGNSS_mission_a_pathfinder_for_GNSS_scatterometry_remote_sensing_applications

8) Shuanggen Jin et al., “Interference Techniques and Delay Doppler Map”, URL: https://link.springer.com/chapter/10.1007/978-981-96-4804-7_5?

9) Maria Paola Clarizia et al., “Towards superresolution delay-Doppler maps”, URL: https://congress.cimne.com/gnss-r10/frontal/presentaciones/136.pdf?

10) Milad Asgarimehr et al., “GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet”, URL: https://www.sciencedirect.com/science/article/pii/S0034425721005216

11) Scott Gleason et al., “Overview of the Delay Doppler Mapping Instrument (DDMI) for the cyclone global navigation satellite systems mission (CYGNSS)”, https://www.researchgate.net/publication/308865926_Overview_of_the_Delay_Doppler_Mapping_Instrument_DDMI_for_the_cyclone_global_navigation_satellite_systems_mission_CYGNSS

12) Hans Herbach, “An improved geophysical model function for ERS C-band scatterometry”, URL: https://www.ecmwf.int/sites/default/files/elibrary/2003/9861-cmod5-improved-geophysical-model-function-ers-c-band-scatterometry.pdf

13) Jinwei Bu et al., “Significant Wave Height Retrieval Method Based on Spaceborne GNSS Reflectometry”, URL: https://ieeexplore.ieee.org/document/9723071

14) Mukesh Kumar Rohil et al., “CYGNSS-derived soil moisture: Status, challenges and future”, URL: https://www.sciencedirect.com/science/article/pii/S157495412200070X#:~:text=The%20received%20signal%20is%20cross,maximum%20power%20of%20a%20DDM.

15) Clara Chew et al., “Demonstrating soil moisture remote sensing with observations from the UK TechDemoSat-1 satellite mission”, URL: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016GL068189#:~:text=The%20TDS%2D1%20bistatic%20radar,roughness%20of%20the%20reflecting%20surface.