![AGB estimated by Artificial Neural Network (ANN) using CYGNSS data plotted on global map. Results limited to within +/-45° latitude due to inclination of CYGNSS satellites [33]](https://www.researchgate.net/publication/352419048/figure/fig2/AS:1035184401027074@1623818602790/AGB-estimated-by-Artificial-Neural-Network-ANN-using-CYGNSS-data-plotted-on-global-map_Q320.jpg)
![AGB estimated by Artificial Neural Network (ANN) presented as a function of reference from a pan-tropical biomass map [33].](https://www.researchgate.net/publication/352419048/figure/fig1/AS:1035184401051650@1623818602769/AGB-estimated-by-Artificial-Neural-Network-ANN-presented-as-a-function-of-reference_Q320.jpg)
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An Introduction to the HydroGNSS GNSS Reflectometry Remote Sensing Mission
Abstract and Figures
HydroGNSS has been selected as the second ESA Scout Earth Observation mission to demonstrate the capability of small satellites to deliver science. This paper summarises the case for HydroGNSS, as developed during its System Consolidation study. HydroGNSS is a high value dual small satellite mission, which will prove new concepts and offer timely climate observations that supplement and complement existing observations and are high in ESAs Earth Observation scientific priorities. The mission delivers observations of four hydrological Essential Climate Variables (ECVs) as defined by the Global Climate Observing System (GCOS) using the new technique of GNSS Reflectometry. These will cover the worlds land mass to 25 km resolution, with a 15 day revisit. The variables are soil moisture, inundation or wetlands, freeze / thaw state and above ground biomass.
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... Previous study [62] has shown that the SMAP-R system has difficultly observing any RHCP signal above the noise floor in its RHCP DDM over ocean in both a typical sea state with approximately 7 m/s wind speed and in a very rough sea state during a hurricane with wind speed higher than 35 m/s. The SMAP-R system has a fixed observation incidence angle at 40 • and an antenna cross-pol ratio G RL /G LL better than 25 dB within its main beam [63]. This implies that the Rongowai measured NBRCS cross-pol ratio term in (13) can be neglected below 40 • due to Rongowai's worse (higher) antenna crosspol isolation G RL /G LL and lower Γ RR /Γ LR which decreases rapidly as it moves to near nadir incidence angles. ...
... Distributions of retrieved surface effective co-pol reflectivity from Lake Taupo measurements, before (blue) vs after (orange) calibration from flat desert regions where the co-pol RR reflectivity over dry sands is much higher than that over water surfaces and so is easy to model. However, no such desert is found in New Zealand, but future spaceborne polarimetric GNSS-R missions (such as HydroGNSS [63]) could collect data from large, flat deserts in other parts of the world to calibrate antenna crosspol gain. ...
... The following methods are recommended to improve the calibration of polarimetric GNSS-R systems for future missions such as the upcoming HydroGNSS spaceborne polarimetric GNSS-R by ESA [63]: ...
Polarimetric GNSS-R systems, equipped with an additional polarization channel, offer enhanced capabilities for separating vegetation and surface scattering effects, thereby improving GNSS-R land remote sensing applications such as soil moisture retrieval in vegetated and forested areas and biomass estimation. However, the effectiveness of these applications relies on accurate calibration of the polarimetric GNSS-R sensor. In the Rongowai mission, a newly developed Next Generation GNSS-R Receiver (NGRx) is installed on a domestic Air New Zealand airplane to collect data during its commercial flights. The NGRx processes multi-GNSS satellite signals simultaneously and utilizes a dual-channel (LHCP and RHCP) antenna, thereby improving spatial coverage and retrieval accuracy. The dual-polarized antenna also provides the possibility to examine the polarimetric GNSS-R system. In this article, a new methodology is developed to calibrate the Level-1 power measurement and the on-board antenna cross-pol gain by comparing measurements from inland lakes and ocean with modeled results. The calibration results in a 34% decrease in the uncertainty in co-pol reflectivity retrieval. The retrieved cross-pol and co-pol reflectivity after calibration are examined by their statistical distribution and spatial mapping with 1.5 km resolution, with multi-land surface types and incidence angles. These results validate the effectiveness of the calibration method and pave the way for future terrestrial science applications.
... HydroGNSS is a new ESA Scout mission which targets four hydrological variables: surface soil moisture, inundation (focusing on wetlands), seasonal freeze and thaw state (focusing on permafrost areas), and above-ground biomass [138]. The launch is expected in early 2025. ...
Permafrost degradation in the Arctic is accelerating and is forecast to enhance greenhouse gas (GHG) emissions from the large permafrost carbon pool. Earth Observation has a key role in determining GHG sources and sinks, and multiple current and future missions are useful to track baseline parameters for determining GHG fluxes. NASA and ESA have initialized the Arctic Methane and Permafrost Challenge (AMPAC) as a transatlantic networking action that strives to promote related scientific work and improve observation capabilities. Key variables observable from space include methane concentrations as well as landcover properties to inform process-based models as proxy for sources as well as temperature-related constraints for microbial activity. Upcoming missions are expected to advance these capabilities significantly with increased sampling intervals through future synthetic aperture radar missions and constellations of multiple multispectral sensors. This will allow better representation of seasonality and advance methane source attribution in general. In addition, continuity of current missions which provide greenhouse gas observations, including methane, is crucial. Hyper- and superspectral sensors targeting primarily landsurface observation are expected to complement methane retrievals through identification of emission hotspots. Arctic monitoring also requires active optical instruments for concentration retrieval, a type of instrumentation which is still novel for satellite-based observations. A comprehensive portfolio of hyper-spectral, passive microwave, synthetic aperture radar, altimeter and landsurface temperature and lidar measurements in addition to imaging spectrometers will be available by 2032/33, at the time of the International Polar Year. This will allow for advanced experiments when also accompanying in situ observations become available.
... However, the fused methodology can be applicable to other CYGNSS-covered regions even without Spire dataset. Future integration with missions like Hydrology using Global Navigation Satellite System reflections (HydroGNSS, [79]) could further reduce reliance on interpolation, potentially extending soil moisture coverage to mid-and high-latitude regions beyond CYGNSS's current scope. ...
High-resolution, large-scale near-surface soil moisture information is critical for many hydrology and climate applications, yet traditional radars and radiometers often fall short of providing information at the required spatial and temporal scales. This study proposes a method for fusing Soil Moisture Active Passive (SMAP) data with spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) measurements from the Cyclone GNSS (CYGNSS) and Spire near-nadir GNSS-R missions, generating soil moisture products at 3- and 9-km resolutions. GNSS-R uses L-band signals that are sensitive to changes in biogeophysical parameters, such as soil moisture. A linear regression-based algorithm retrieves soil moisture from both CYGNSS and Spire data, which, despite showing biases relative to one another, exhibit similar sensitivities to soil moisture variations. The 9-km fused product integrates observed and interpolated GNSS-R estimates to complement daily SMAP 9-km maps, while the 3-km product refines GNSS-R retrievals using available SMAP data. This approach is validated against in-situ measurements and the SMAP/Sentinel 3-km product over mainland Australia for 2021. Our findings indicate a median unbiased root-mean-square error (ubRMSE) of 0.049 cm3cm-3 for the 3-km product and 0.054 cm3cm-3 for the 9-km product, both of which are comparable to SMAP's ubRMSE of 0.054 cm3cm-3. The fused products provide daily soil moisture retrievals with accuracy comparable to SMAP while significantly improving temporal resolution. The 3-km product, in particular, captures finer spatial variability, offering a more detailed representation of soil moisture dynamics.
... H YDROGNSS is a Scout mission of the European Space Agency (ESA) designed to provide measurements of key variables on land for hydrology and climate [1]. HydroGNSS conforms to the objectives of the ESA Scout programme by demonstrating the capability of small satellites to deliver valuable scientific data while adopting a cost-effective and agile development process, with the two-satellites mission scheduled for launch in the second half of 2025. ...
The water detection algorithm for the dual-polarized HydroGNSS mission was validated using spaceborne Left-Hand Circular Polarization (LHCP) data from Cyclone Global Navigation Satellite System (CYGNSS). With the public availability of dual-polarization data from Rongowai, an airborne Global Navigation Satellite System-Reflectometry (GNSS-R) mission, a unique opportunity arises to evaluate the contribution of Right-Hand Circular Polarization (RHCP) data to surface water detection. This analysis can offer a deeper understanding of RHCP data and yield predictive insights prior to the HydroGNSS launch. In this study, we initially analyzed coherence indicators in incoherently averaged dual-polarized signals and subsequently applied these indicators to a random forest classifier, similar to the HydroGNSS surface inundation algorithm. The findings have been compared with existing flooding products, showing promising results with over 91% agreement in water detection. The analysis revealed that, while the LHCP data exhibit a higher sensitivity to water, the incorporation of the RHCP data enhances the robustness and reliability of the classification. This reinforces the hypothesis that HydroGNSS, operating at dual-polarization, might produce a more effective surface water detection product than single-polarization GNSS-R missions.
... Since July 2021, China has launched the FY-3E/3F/3G satellite [20,21], and in the same year, China also launched the Jilin-1 Wideband-01B (J1-01B) satellite [22]. There are also FORMOSAT [23], soil moisture active passive (SMAP-R) [24], SPIRE [25], as well as Demonstration of Technology 1 (DOT-1) satellite in preparation for the expected launch of HydroGNSS by the end of 2024 [26,27], and so on. The Tianmu-1 2 constellation is China's first commercially built low Earth orbit meteorological satellite system, with its first satellite, TM00, launched on October 14, 2021 It is operated by Aerospace Tianmu (Chongqing) Satellite Technology Co., Ltd., a subsidiary of China Aerospace Science and Industry Corporation Limited (CASIC). ...
The Tianmu-1 constellation is mainly based on BeiDou/GNSS radio occultation (GNSS-RO) and BeiDou/GNSS reflectometry (GNSS-R) remote sensing technology, which can simultaneously achieve integrated stereo detection of GNSS-RO and GNSS-R. It is the first commercial GNSS remote sensing detection constellation in the world to achieve integrated business detection of “land surface, ocean, three-dimensional atmosphere, and ionized environment”. Furthermore, it is the first GNSS remote sensing detection constellation in China that is compatible with receiving the five major GNSS satellite navigation systems (i.e., Global Positioning System, BeiDou, Galileo, GLONASS and Quasi-Zenith Satellite System). This study used Tianmu-1 GPS-R/BDS-R/GAL-R/GLO-R global wind speed data from July 2023 to December 2023, and used five datasets as evaluation data, including the ERA5 reanalysis, CYGNSS L2, SMOS, CCMP and NDBC buoy wind speed products, to present the results of the mission's first evaluation of sea surface wind speed estimates. The results show that the Tianmu-1 retrieval wind speed has good consistency with the CYGNSS verification wind speed. When the wind speed range is 0-35 m/s, its minimum RMSE can reach 1.74 m/s. The RMSE of the Tianmu-1 retrieval wind speed and the other four verification wind speeds (ERA5, CCMP, SMOS_SCA, SMOS_SCD) are 2.25 m/s, 2.33 m/s, 2.74 m/s, and 3.17 m/s, respectively. For the wind speed products of the four systems in the Tianmu-1 constellation, GAL showed the best accuracy results. In addition, a comparative verification was conducted with buoy wind speeds, the BDS system demonstrated the best accuracy results, with an RMSE of 1.97m/s.
As part of the continued refinement of the Cyclone Global Navigation Satellite System (CYGNSS) mission’s Level-1 calibration practices, this work reports on recent progress to compensate for thermal artifacts associated with reported Effective Isotropic Radiated Power (EIRP) tracked by the receivers’ zenith channels in the form of oscillations with a dominant 40-60 day principal component. The use of the uncorrected EIRPs in the calibration of Normalized Bistatic Radar Cross Section (NBRCS) estimates is shown to lead to commensurate non-geophysical oscillations. A simple scheme for using in-orbit measurements to rederive prelaunch zenith channel LNA gain tables as a function of the relevant temperature is overviewed. These are subsequently used as part of the EIRP calibration process and to implicitly correct reported NBRCS estimates. The corrected NBRCS estimates are markedly more consistent. It is estimated that the revisions introduced as part of this work, and adopted for the mission’s latest v3.2 Level-1 data release, reduce the errors in the temperature dependence of the transmitter power estimates by an average of 88% while also reducing NBRCS relative spectral densities over the relevant frequency components by an average of 36%. The remaining energy is attributed to a combination of smaller residual, unaccounted for, calibration imperfections but more importantly to real geophysical phenomena causing oscillations over similar time scales whose minimization is not of interest.
Remote sensing using global navigation satellite system (GNSS)-reflectometry (GNSS-R) delay-Doppler maps (DDMs) over land is an emerging field. Understanding the sensitivity of DDMs to geophysical variables of interest, such as soil moisture and vegetation, is needed to understand the performance limitation of their retrievals. This work presents an analytical sensitivity analysis of GNSS-R DDMs to land surface variables using the improved geometric optics with topography (IGOT) model and the Cyclone GNSS (CYGNSS) data over three areas with various topographical terrains. The IGOT model is an electromagnetic DDM model for land applications that is valid for topographical terrains with bare-to-intermediate vegetation cover. It divides the surface roughness into three scales. The large scale is governed by a digital elevation model (DEM), whereas the other two are stochastic. The vegetation effect is modeled as an attenuation layer. Sensitivity to soil moisture, multiple scales of roughness, and vegetation are among the studied land surface variables. Furthermore, numerical results are obtained using realistic scenarios derived from CYGNSS observations. This work finds that the DDM peak reflectivityivity to the large-scale surface roughness parameter and incidence angles varies depending on the geometry of the transmitter, receiver, and specular point., the DDM peak reflectivity is inversely proportional to vegetation (due to attenuation effects) and small-scale surface roughness, which is consistent with the literature. Additionally, CYGNSS data over the study are analyzed to study the effect of incidence and azimuth angles. The analysis confirms that the behavior of peak reflectivity varies depending on both angles.
River slope, a crucial parameter in hydrological modeling, has historically been difficult to measure continu-ously on a regional or global scale. Satellite altimetry missions often have long revisit times, such as 10 to 20 days for the Surface Water and Ocean Topography (SWOT) mission. In this paper, a novel approach is presented utilizing spaceborne GNSS Reflectometry (GNSS-R) to measure river slopes with high accuracy and potentially short revisit times. Our Earth is enveloped in radio signals from over 100 GNSS satellites. These signals can be coherently reflected from river surfaces and detected by low Earth orbit (LEO) satellites with sufficient energy to estimate carrier phase. The carrier phase measurement captures water surface height variations, which can be extracted through modeling of the reflection signal propagation geometry and space environment effects to estimate river slopes. This study processes both the raw intermediate frequency (IF) data obtained by NASA’s Cyclone GNSS (CYGNSS) microsatellites and the grazing-angle GNSS-R data generated by Spire Global nanosatellites to demonstrate the feasibility and performance of the GNSS-R based river slope retrieval. This paper focuses on selected river sections with width greater than ∼500 meters. Detailed methodologies and error analyses are presented, indicating total uncertainty of approximately 0.38 cm/km plus ionospheric TEC model error for CYGNSS and 0.69 cm/km for Spire (with dual-frequency ionospheric correction) over an ideal 5-km river section at 30◦ elevation angle. The retrieval results are validated in areas with nearby flat water surfaces (such as lakes or wide and slow river sections) and against in situ gauge measurements and satellite altimetry, consistently demonstrating the high accuracy and reliability of spaceborne GNSS-R for measuring river slopes.
This study presents a new retrieval approach for obtaining wind speeds from CyGNSS level-1 observables. Unlike other existing approaches, (1) this one is a variational technique that is based on a physical forward model, (2) it uses uncalibrated bin raw counts observables, (3) the geophysical information content comes from only one pixel of the broader delay-Doppler map, finest achievable resolution in level-1 products over the sea, and (4) calibrates them against track-wise polynomial adjustments to a background numerical weather prediction model. Through comparisons with the background model, other spaceborne sensors (SMAP, SMOS, ASCAT-A/B), and CyGNSS wind retrievals by other organizations, the study shows that this approach has skills to infer wind speeds, including hurricane force winds. For example, the Pearson’s correlation coefficient between these CyGNSS retrievals and ERA5 is 0.884, 0.832 with NOAA CyGNSS results, and 0.831 with respect to SMAP co-located measurements. Furthermore, the variational retrieval algorithm is a simplified version of the more general equations that are used in data assimilation, and the calibration scheme could also be integrated in the assimilation process. Therefore, this approach is also a good tool for analyzing the potential performance of ingesting uncalibrated level-1 single-pixel observables into NWP.
Currently, the ability to use remotely sensed soil moisture to investigate linkages between the water and energy cycles and for use in data assimilation studies is limited to passive microwave data whose temporal revisit time is 2–3 days or active microwave products with a much longer (>10 days) revisit time. This paper describes a dataset that provides soil moisture retrievals, which are gridded to 36 km, for the upper 5 cm of the soil surface at sparsely sampled 6-hour intervals for +/− 38 degrees latitude for 2017–present. Retrievals are derived from the Cyclone Global Navigation Satellite System (CYGNSS) constellation, which uses GNSS-Reflectometry to obtain L-band reflectivity observations over the Earth’s surface. The product was developed by calibrating CYGNSS reflectivity observations to soil moisture retrievals from NASA’s Soil Moisture Active Passive (SMAP) mission. Retrievals were validated against observations from 171 in-situ soil moisture probes, with a median unbiased root-mean-square error (ubRMSE) of 0.049 cm3 cm−3 (standard deviation = 0.026 cm3 cm−3) and median correlation coefficient of 0.4 (standard deviation = 0.27). For the same stations, the median ubRMSE between SMAP and in-situ observations was 0.045 cm3 cm−3 (standard deviation = 0.025 cm3 cm−3) and median correlation coefficient was 0.69 (standard deviation = 0.27). The UCAR/CU Soil Moisture Product is thus complementary to SMAP, albeit with a larger random noise component, providing soil moisture retrievals for applications that require faster revisit times than passive microwave remote sensing currently provides.
An assessment of the National Aeronautics and Space Administration NASA’s Cyclone Global Navigation Satellite System (CyGNSS) mission for biomass studies is presented in this work on rain, coniferous, dry, and moist tropical forests. The main objective is to investigate the capability of Global Navigation Satellite Systems Reflectometry (GNSS-R) for biomass retrieval over dense forest canopies from a space-borne platform. The potential advantage of CyGNSS, as compared to monostatic Synthetic Aperture Radar (SAR) missions, relies on the increasing signal attenuation by the vegetation cover, which gradually reduces the coherent scattering component σcoh,0. This term can only be collected in a bistatic radar geometry. This point motivates the study of the relationship between several observables derived from Delay Doppler Maps (DDMs) with Above-Ground Biomass (AGB). This assessment is performed at different elevation angles θe as a function of Canopy Height (CH). The selected biomass products are obtained from data collected by the Geoscience Laser Altimeter System (GLAS) instrument on-board the Ice, Cloud, and land Elevation Satellite (ICESat-1). An analysis based on the first derivative of the experimentally derived polynomial fitting functions shows that the sensitivity requirements of the Trailing Edge TE and the reflectivity Γ reduce with increasing biomass up to ~ 350 and ~ 250 ton/ha over the Congo and Amazon rainforests, respectively. The empirical relationship between TE and Γ with AGB is further evaluated at optimum angular ranges using Soil Moisture Active Passive (SMAP)-derived Vegetation Optical Depth (VOD), and the Polarization Index (PI). Additionally, the potential influence of Soil Moisture Content (SMC) is investigated over forests with low AGB.
The study of the freeze/thaw dynamic of high-latitude Earth surfaces is extremely important and informative for monitoring the carbon cycle, the climate change, and the security of infrastructures. Current methodologies mainly rely on the use of active and passive microwave sensors, whilst very few efforts have been devoted to the assessment of the potential of observations based on signals of opportunity. This work aims at assessing the performance of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) for high-spatial and high-temporal resolution monitoring of the Earth-surface freeze/thaw state. To this aim, reflectivity values derived from the TechDemoSat-1 (TDS-1) data have been collected and elaborated, and thus compared against the SMAP freeze/thaw information. Shallow sub-surface soil temperature values recorded by a network of in situ stations have been considered as well. Even if an extensive and timeliness cross-availability of both types of experimental data is limited by the spatial coverage and density of TDS-1 observations, the proposed analysis clearly indicates a significant seasonal cycle in the calibrated reflectivity. This opens new perspectives for the bistatic L-band high-resolution satellite monitoring of the freeze/thaw state, as well as to support the development of next-generation of GNSS-R satellite missions designed to provide enhanced performance and improved temporal and spatial coverage over high latitude areas. Index Terms-Freeze thaw (FT), GNSS reflectometry (GNSS-R), bistatic radars, TechDemoSat-1 (TDS-1) mission, global positioning system (GPS), soil moisture active passive (SMAP).
This paper presents the temporal evolution of Global Navigation Satellite System Reflectometry (GNSS-R) ocean wind speed retrieval performance during three years of the UK TechDemoSat-1 (TDS-1) mission. TDS-1 was launched in July 2014 and provides globally distributed spaceborne GNSS-R data over a lifespan of over three years, including several months of 24/7 operations. TDS-1 wind speeds are computed using the NOC Calibrated Bistatic Radar Equation algorithm version 0.5 (C-BRE v0.5), and are evaluated against ERA5 high resolution re-analysis data over the period 2015–2018. Analyses reveal significant temporal variability in TDS-1 monthly wind speed retrieval performance over the three years, with the best performance (~2 m∙s⁻¹) achieved in the early part of the mission (May 2015). The temporal variability of retrieval performance is found to be driven by several non-geophysical factors, including TDS-1 platform attitude uncertainty and spatial/temporal changes in GPS transmit power from certain satellites. Evidence is presented of the impact of the GPS Block IIF Flex mode on retrieved GNSS-R wind speed after January 2017, which results in significantly underestimated ocean winds over a large region covering the North Atlantic, northern Indian Ocean, the Mediterranean, the Black Sea, and the Sea of Okhotsk. These GPS transmit power changes are shown to induce large negative wind speed biases of up to 3 m∙s⁻¹. Analyses are also presented of the sensitivity of TDS-1 wind speed retrieval to platform attitude uncertainty using statistical simulations. It is suggested that a 4° increase in attitude uncertainty can produce up to 1 m∙s⁻¹ increase in RMSE, and that TDS-1 attitude data do not fully reflect actual platform attitude. We conclude that the lack of knowledge about the GNSS-R nadir antenna gain map and TDS-1 platform-attitude limits the ability to determine the achievable wind speed retrieval performance with GNSS-R on TDS-1. The paper provides recommendations that accurate attitude knowledge and a good characterisation of GNSS-R nadir antenna patterns should be prioritised for future GNSS-R missions.
In this study, the capability of GNSS Reflectometry in evaluating forest biomass from space has been investigated by using data coming from the TechDemoSat-1 (TDS-1) mission of SSTL and from the CyGNSS mission of NASA. The analysis has been first conducted using TDS-1 data on a local scale, by selecting five test areas located in different parts of the Earth's surface. The areas were chosen as examples of various forest coverages. The analysis has been extended by using CyGNSS to a global scale. The reflectivity has been confirmed as the most suitable parameter and its sensitivity to forest has been evaluated by a comparison with vegetation optical depth (VOD) from the SMAP L-band radiometer, with a pan-tropical aboveground biomass (AGB) map and with a tree height (H) global map derived from the GLAS laser altimeter on-board ICEsat. The analysis confirmed the decrease of reflectivity for increasing biomass, with correlation coefficients 0.31 ≤ R ≤ 0.54 depending on the parameter (VOD, AGB or H) and on the considered dataset. These correlations were not sufficient to retrieve the parameters by simple inversion. The retrieval has been therefore based on Artificial Neural Networks making it possible to add other inputs (e.g., incidence angle, SNR). Although not directly correlated to AGB, these inputs helped in improving the retrieval accuracy. The algorithm was tested on both the selected areas and globally, showing a promising ability to retrieve the target parameter, either AGB or H, with correlation coefficients R ⋍ 0.8.
A significant quantity of space-borne GNSS-R data over land was made available in the last decade, leading to an increasing interest on the assessment of the potentialities of this new remote sensing technique for land monitoring. In this frame, an electromagnetic simulator, such as the Soil And Vegetation Reflection Simulator (SAVERS), has the key role to support the understanding of the physical mechanism involved in the bistatic scattering and to identify the surface features mainly contributing to the observed signal. Originally developed for ground and airborne GNSS-R observations over homogeneous areas, in this study SAVERS was upgraded to account for space-borne systems. The new version of SAVERS takes into account the inhomogeneity characterizing the large area observed from space altitudes, due to a variable surface elevation and land cover. Coherent and incoherent scattering and polarization rotation are computed taking into account the local slope and elevation of the surface. The simulator was validated against TechDemoSat-1 observations over a bare surface with complex topography and over a forested surface with gentle topography. The validation results show the capability of SAVERS to correctly estimate the effect of the topography, enhancing the understanding of the observations. Moreover, it was found that the sensitivity to soil moisture is independent on the topography (about 1.5 dB for a 10% variation of soil moisture). Whereas, a saturation of the GNSS-R reflectivity over a variable topography is reached for lower values of biomass, earlier than in the flat case.
Plain Language Summary
Melt of Greenland ice sheet (GrIS) plays important roles in sea level change, global climate, and other areas of global environmental issues. Satellite microwave remote sensing has been increasingly used to produce maps of snow melt over Greenland during the past decades. The use of signals transmitted by navigation satellites after reflected from the Earth surface, known as Global Navigation Satellite System Reflectometry (GNSS‐R), provides a cheap solution of measuring the Earth surface with exceptional spatiotemporal coverage. Using the data collected from U.K. TechDemoSat‐1 mission, we investigate the feasibility and performance of spaceborne GNSS‐R for GrIS melt detection. The results obtained with GNSS‐R measurements give good agreement with GrIS melt data from other microwave sensors and also show potential of providing complementary melt information in deep layers of the GrIS due to the penetration of GNSS signal into the snow pack. These results, obtained from a technique demonstration platform, show the potential of future GNSS‐R missions for provide high spatial and temporal resolution melt data over the Greenland and Antarctica ice sheet.
In this article, a statistical methodology to estimate wind speed from CYGNSS observables is proposed and implemented. The approach uses the cumulative distribution function (cdf) of the observable and of the ground-truth reference winds. It depends only on the statistical distributions of the CYGNSS data and the wind speed, and therefore, is simpler to implement than alternative approaches requiring coincident matchups between the data and the ground truth. This cdf matching method produces retrieved winds with a probability density function that is very close to that of the ground-truth winds. When compared to the current CYGNSS baseline winds for fully developed seas, the cdf matching winds show better behavior and agreement with reference wind speeds over the low to medium wind speed range, which constitutes the majority of the wind population that drives the statistics used by the algorithm. The performance is robust with respect to measurement geometry and transmitter and receiver hardware parameters, with the exception of a dependence of the error on the GPS satellite identifier (ID), probably due to uncorrected variations in GPS equivalent isotropically radiated power (EIRP). Validation using modeled winds and winds measured by other satellites reveals that CYGNSS winds behave in a very similar manner as the winds modeled by the Global Data Assimilation System (GDAS).
