Data from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission have shown promise for the retrieval of soil moisture, and many soil moisture products using CYGNSS data have been developed. In this work, we present a merged product that combines several CYGNSS soil moisture products using a minimum variance estimator (MVE). The MVE identifies an optimal weighted averaging scheme based on the error covariance characteristics of the CYGNSS soil moisture products. The error covariance matrix is computed using two reference datasets: soil moisture data from the Soil Moisture Active Passive (SMAP) radiometer and in situ soil moisture data. The results from each of these provide insights into both the performance of the merged product and the individual input CYGNSS products. Overall, the merged product offers better performance than any individual CYGNSS product while also offering better temporal resolution than SMAP. The results of this work also demonstrate that the use of the MVE is a compelling technique for soil moisture applications.

In this paper, we compare water surface height profiles measured by the Ice, Cloud, and Elevation Satellite-2 (ICESat-2) ATL13 data product on April 25, 2023 that intersect with a GNSS-Reflectomety (GNSS-R) altimetry track collected on April 17, 2023 over the Tonlé Sap Lake in Cambodia. Overall, we find reasonable agreement between the two datasets, with the nearest segment of ICESat-2 and GNSS-R tracks having a correlation of 0.79. However, some deviations exist, with the GNSS-R track measuring 7 cm lower than the ICESat2 track at an intersection point, potentially due to different lake surface conditions at the time of each measurement. This comparison is motivated by the interest in applying GNSS-R altimetry for a broader study of large inland lakes. Our focus on the Tonlé Sap Lake is inspired by the lake’s key role in the region combined with the difficulty in monitoring it by other techniques due to its large size, frequent cloud cover, and surrounding dense mangrove forests. GNSS-R (operating in L-band) can penetrate these environmental factors while accumulating frequent and spatially diverse water surface measurements. In total, we find 53 GNSS-R tracks collected by Spire Global between 2021 and 2023 that pass our altimetry criteria. In contrast, we find only 27 ICESat-2 tracks (each laser counted individually) through 12 total passes, likely limited by cloud cover.

SigNals Of Opportunity: P-band Investigation (SNOOPI) will be the first in-space validation of P-band (240–380 MHz) SoOp techniques and a prototype science instrument. These techniques have the potential to enable remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE). SNOOPI technology validation goals will be met by targeting observations within 9 km of the SMAP calibration/validation sites in the continental United States. A second priority is collection of continuous phase data over snow-covered regions. These goals are evaluated under constraints of a limited data budget and mission lifetime, with a launch readiness in August 2022. This presentation will review the instrument science plans aimed at achieving the validation objectives defined for the mission. Mission planning and data processing approaches are described.

A spaceborne P-band signals of opportunity synthetic aperture radar concept is proposed for the remote sensing of terrestrial snow. We have completed a performance analysis assuming a formation flight of 3 to 5 SmallSats on one orbit plane. The spacing between the SmallSats is chosen so that their ground tracks will be separated by 50 to 100 m to allow the use of interferometric synthetic aperture radar processing technique to obtain a spatial resolution of a few hundred meters. A point system design has been completed to determine the antenna concept and to indicate the dependence of spatial resolution and signal to noise ratio on the number of receivers. The performance for range delay determination was analyzed to assess the impact of various error sources, including instrument receiver noise and ionospheric delay. The dominant error source is the ionospheric delay, which will be corrected using the split-spectrum algorithm. Our overall error budget analysis indicates that an accuracy of about 3 cm for the snow water equivalent in dry snow and 5 cm for the snow depth of wet snow can be achieved.

Coherent reflections of global navigation satellite system (GNSS) signals have a measurable carrier phase, enabling higher precision for certain GNSS-based Earth remote sensing applications. In this letter, we explore the dependence of coherence on three land surface characteristics: surface water, topography, and soil moisture (SM). Carrier phase measurements are obtained by tracking raw intermediate frequency data collected by the cyclone GNSS (CYGNSS) mission. In total, several hundred data collections between 2017 and 2019 are analyzed. The phase coherence, quantified using statistics of the tracked carrier phase, is compared to the corresponding land characteristics on a per-track basis and across the entire dataset. On a per-track basis, we find that the level of coherence can often be explained by the presence of surface water, with no obvious dependence on topography or SM. However, by analyzing the entire dataset, we show that topography and SM have a weak but noticeable impact on the coherence.

Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission augmented with a physical surface scattering model were analyzed to develop a semiempirical model, which consists of three main modeling components for soil moisture, vegetation, and surface roughness. CYGNSS data collected from March 2017 to March 2020 were collocated with the soil moisture data from the Soil Moisture Active Passive (SMAP) mission and climatology vegetation water content (VWC) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. The matchup data were binned as a function of soil moisture, VWC, and incidence angle. The CYGNSS data were calibrated using a coherent reflection equation to obtain an effective reflectivity. The response of CYGNSS effective reflectivity to soil moisture changes is consistent with the change of the Fresnel reflectivity based on Mironov’s soil dielectric constant model used by the SMAP and Soil Moisture Ocean Salinity (SMOS) missions for soil moisture retrieval. The CYGNSS effective reflectivity decreases approximately linearly (in dB) with respect to the NDVI-VWC. The estimated values of vegetation attenuation parameter ($b$) agree with values published in the literature and are corroborated with the estimated values of$b$using the SMAP dual-polarized channel algorithm based on land cover types. A CYGNSS surface scattering map has been derived and reveals a mixed contribution of coherent and incoherent scattering effects and the effects of topography. The semiempirical model, leveraging two of the key modeling functions used by microwave radiometry, will pave the way for a synergistic use of reflectometry and radiometry data for multiparameter retrieval and development of consistent soil moisture products.

SigNals Of Opportunity: P-band Investigation (SNOOPI) will be the first on-orbit demonstration of remote sensing using Signals of Opportunity (SoOp) in P-band (240–380 MHz). P-band is needed to penetrate through dense vegetation and into the root zone. The longer wavelength of P-band also increases the unwrapping interval for phase observations. These observations hold the potential for spaceborne remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE), two variables identified as priorities in the 2017–2027 Decadal Survey for Earth Science and Applications from Space. SNOOPI will provide in-space validation of both the P-band SoOp technique and a science instrument prototype. SNOOPI technology validation goals will be met by targeting observations within 9 km of the SMAP calibration/validation sites in the continental United States. A secondary priority is collection of continuous phase data over snow-covered regions. These goals are evaluated under constraints of a limited data budget and mission lifetime, with a launch readiness in early 2022. Updates on the development of measurement models and mission planning to support SNOOPI are provided. A ground-based station will be deployed to monitor the noncooperative sources, in order to reduce risk due to uncertainty in knowledge of the broadcast power, spectrum shape, and orbital position.

GNSS reflection measurements can be calibrated with data from SMAP to yield estimates of soil moisture with enhanced spatiotemporal resolution useful to certain hydro-logical/meteorological studies. Current approaches use simple models of the relation between the DDM (delay-Doppler map) and soil moisture and can fail in certain regions of the planet. Complex information contained in the complete 2D DDM could help in these areas, and can be extracted through the application of deep learning based techniques. Our work explores the data-driven approach of convolutional neural networks to determine complex relationships between the reflection measurement and surface parameters. We developed a neural network trained using CYGNSS DDMs and ancillary datasets aligned with SMAP soil moisture values; the results of which are analyzed and compared to existing global soil moisture products.

The Cyclone Global Navigation Satellite System (CYGNSS) observation received the Global Positioning System (GPS) signals with a revisit time in the range of 2.8 to 7.2 hours and have about a few kilometers of spatial resolution for coherent reflection and ∼25 km for incoherent reflection [1]. The CYGNSS datasets have a great potential to provide the vegetation optical depth (VOD) by combining the soil moisture data from SMAP. In this paper, we developed a physical-based model to retrieve the VOD. The retrieved VOD has been compared with SMAP VOD in both regional and global scale.

This work demonstrates the creation of dynamic inland water body masks at spatial resolutions ranging from 1 to 3 km through the use of a recently developed coherence detector for the delay-Doppler maps produced by the cyclone global navigation satellite system (CYGNSS) constellation. The use of the coherence of the observed measurements reduces many of the uncertainties associated with previous signal-to-noise ratio-based water body detection approaches for CYGNSS. Using data from January 2018 to February 2020 and producing maps representing time intervals ranging from 3 months to 2 years, the water body masks created are found to be associated with a probability of detection that exceeds 80% as compared to the Pekel water mask developed from Landsat observations. The analysis presented in this work highlights the potential of using spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) systems for dynamic inland water body mapping.

In February 2019 a Project Authorization Request was approved by the Institute of Electrical and Electronics Engineers (IEEE) Standards Association with the title “Standard for Global Navigation Satellite System Reflectometry (GNSS-R) Data and Metadata Content”. A Working Group has been assembled to draft this standard with the purpose of unifying and documenting GNSS-R measurements, calibration procedures, and product level definitions. The Working Group (http://www.grss-ieee.org/community/technical-committees/standards-or-earth-observations/) includes members, collaborators, and contributors from academia, international space agencies, and private industry. In a recent face-to-face meeting held during the ARSI+KEO 2019 Conference, the need was recognized to develop a standard with a wide range of operations, providing procedure guidelines independently of constraints imposed by current limitations on geophysical parameters retrieval algorithms. As such, this effort aims to establish the fundamentals of a potential virtual network of satellites providing inter-comparable data to the scientific community.

Precise carrier phase measurements from coherent reflections of Global Navigation Satellite Systems (GNSS) signals are poised to enable new capabilities in Earth remote sensing. However, there is still much to learn about the reflection conditions that produce coherence. For land reflections especially, the degree to which various surface characteristics impact coherence is unclear. In this paper, we explore the dependence of coherence on four surface characteristics: maximum water extent, surface topography, soil moisture, and vegetation water content. To quantify the level of coherence, the phase is tracked from two sets of CYGNSS raw IF data and the circular length of the changes in phase is calculated. A strong link is found between coherence and the presence of surface water; the impact of the other surface characteristics on coherence is still under examination.

SigNals of Opportunity: P-band Investigation (SNOOPI) will be an in-space technology demonstration of reflectometry using 240-380 MHz communications transmissions. SNOOPI will both demonstrate essential techniques for root-zone soil moisture (RZSM) and snow water equivalent (SWE) remote sensing as well as provide in-space validation of prototype instrument technology. This paper presents results from studies conducted to define key parameters of the SNOOPI mission, including orbital coverage, signal processing, and the estimated power from the non-cooperative sources.

A low cost, low power, and low mass P-band digital back end (DBE) has been developed at JPL. The design is based upon a Global Navigation Satellite System (GNSS) receiver (called Cion) that has been currently flying on the CICERO CubeSats. This paper describes the design of the DBE as well as use cases for the subsystem. This Cion DBE will be used in NASA InVest mission SNOOPI (SigNals of Opportunity P-Band Investigation) and an instrument incubator project Signals of Opportunity Synthetic Aperture Radar (SoOpSAR) to demonstrate SAR like processing using P-band Signals of Opportunity (SoOp).

Recently, the Signal of Opportunity (SoOp) has been used in monitoring the snow pack from P-band. This technology makes use of existing satellite transmissions and become a cost-effective alternative to existing active technologies. The theoretical principle is based on the phase change of the reflected P-band signal to change in SWE. The P-band radio signals come from geostationary Mobile Use Objective System (MUOS) communication satellites, operating with dual-frequency channels at P-band (360-380 MHz and 240-270 MHz). P-band frequencies have excellent capability in penetration through thick vegetation (a confounding factor in existing SWE retrievals), and will offer unprecedented capability to sense snowpack under forest canopy. This paper provides an end-to-end simulation through OSSEs and support the understanding of physical mechanizes of surface features that contributing to the received signals.

P-band Signals of Opportunity (SoOp) has great potential for remote sensing of root zone soil moisture (RZSM) from space. We have carried out a tower-based experiment with receivers to detect the reflected signals from the communications satellites at the Fraser Experimental Forests (FEFs), Colorado, in 2017. The measured reflectivity data at 260 MHz have a good correlation with in-soil moisture (SM) measurements. Retrieval of SM from the reflectivity data was also performed with results indicating accuracy of about 0.01 bias and 0.02 standard deviation (std) with respect to the average of SM in the upper 10 cm of soil. The experimental data and retrieval analyses lend support to the use of P-band SoOp for the remote sensing of SM. Our data also indicate the limitation of single frequency observations, suggesting the requirement of multiple frequencies to enable RZSM remote sensing because the surface SM plays a critical role on the change of reflectivity even at P-band frequencies.

SigNals of Opportunity: P-band Investigation (SNOOPI) will be the first on-orbit demonstration of remote sensing using Signals of Opportunity (SoOp) in P-band (240-380 MHz). P-band SoOp has the potential for spaceborne remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE), two variables identified as priorities in the 2017-2027 Decadal Survey for Earth Science and Applications from Space. P-band is needed to penetrate through dense vegetation and into the root zone. SNOOPI will provide inspace validation of both the technique of P-band SoOp and a science instrument prototype. This is a necessary risk-reduction step on the path to a science mission, which will verify important assumptions about reflected signal coherence, robustness to the RFI environment, and our ability to capture and process the reflected signal from orbit. SoOp observations will be used to estimate the complex reflection coefficient over various land surface conditions. These will be used to verify models and show that P-band SoOp can meet working requirements for future RZSM and SWE missions. The SNOOPI instrument design builds upon the heritage of a low noise front end (LNFE), developed from an airborne demonstrator, and a digital back end (DBE) evolved from the Cion, TriG and Blackjack GPS receivers. Success with SNOOPI will retire the critical risks associated with a P-band SoOp satellite instrument and exit at TRL-7. Not only would this instrument enable direct measurements of RZSM and SWE which are not presently possible, it's size, weight, power and cost (SWaP-C) would also be orders of magnitude smaller than comparable monostatic radars due to the re-utilization of existing, powerful, anthropogenic signals.

Spaceborne GNSS Reflectometry (GNSS-R) measurements have shown strong coherent scattering over inland waters. It has been recognized that GNSS-R could be utilized for monitoring the global surface water distribution by making dynamic maps of wetlands as well as rapid response to flood events. Using the strength of the reflected signals, one can make maps that reveal the presence of water over land. In this paper, we used simulations to analyze the accuracy of these maps. The CYGNSS End-to-end Simulator (E2ES) was extended to include coherent scattering in the heterogeneous scenes where the region around the specular point is composed of both land and water in complex geometries. The simulation is then used to evaluate the accuracy of a simple fractional water in footprint approach to mapping wetland extent. We find that scattering from outside the first Fresnel zone and CYGNSS measurement processing effects significantly impact the accuracy of this approach. However, the accuracy can be improved by combining multiple measurements into a gridded map.

Radar measurements of the Earth's land surface are sensitive to water in the vegetation and soil: each frequency is jointly sensitive to a range of soil moisture and vegetation water content terms, which are often ignored in order to retrieve a single quantity of interest. Here, we explore the joint capability of multifrequency radar observations of the terrestrial land surface through an observing system simulation experiment (OSSE) case study. Specifically, we investigate the added value of temporal constraints on the carbon (C) and water (H2O) cycles through the joint use of K, C, L and P band measurements to retrieve fundamental land surface C and H2O state variables and process parameters. We use the CARbon DAta-MOdel fraMework (CARDAMOM) to represent the temporal evolution of C and H2O state variables and associated process inter-dependencies. Our results indicate that overall, the assimilation of 4-bands leads to substantial uncertainty reductions relative to single band experiments.

Arctic and Antarctic sea ice covers are in a sharp contrast in terms of characteristics, distributions, and processes with a drastic decrease in the Arctic versus the opposite increase in the Antarctic in a changing climate. In quantifying polar sea ice differences to address the contrasted sea ice behaviors, two key parameters are sea ice thickness and melt pond fraction, which remain challenging to measure extensively in time and in space with a sustainable approach. Here, we present a new paradigm for such measurements using bistatic radar reflectometry, thanks to developments of low-cost receivers to acquire reflected signals from numerous existing transmitter systems operated at multiple frequencies to be replenished and sustained indefinitely into the future. For sea ice thickness measurement to determine ice volume, reflected signals likely come from the bottom ice-water interface avoiding large errors inherent in current altimetry techniques due to uncertainty in free-board height and snow cover. Regarding melt pond faction on sea ice to estimate albedo and insolation, the bistatic reflection can be dominated by melt pond water with permittivity that is one order of magnitude larger compared to that of snow or ice. These are examined by a combination of numerical Kirchhoff (KA) simulator and Numerical Maxwell Model of 3D simulations (NMM3D) to preserve phase and amplitude information and thereby account for both coherent and incoherent effects. Physical insights from the rigorous theory for bistatic radar reflectometry will be valuable to develop future satellite missions to resolve cryospheric science issues concerning the polar sea ice differences.

This paper shows results from a proof-of-concept tower experiment that computed phase and reflectivity from a reflected P-band signal. The change in phase of the reflected signal is related to SWE for dry snow and the rate of change of phase is directly correlated to frequency of observation. The effect of vegetation was also evaluated by using measurements from two towers: one with bare soil and one surrounded by small trees with heights of up to 3 meters. SWE has been retrieved from two sites with RMSD of 1.15-1.6 cm for different frequencies and sites. In addition, sensitivity in reflectivity measurement at 260 MHz due to changes in soil moisture was observed in summer 2018 data.

A proof-of-concept experiment has demonstrated that wideband (400 MHz) signals of opportunity (SoOp) transmitted in K- and Ku-bands from geostationary satellites can be used for coastal altimetry. An essential finding from this experiment is that the full broadcast spectrum consisting of multiple digital channels can be processed as a single wideband signal source. An established error model for Global Navigation Satellite System interferometric altimetry was shown to accurately represent the sea surface height (SSH) retrievals when evaluated using the full bandwidth. This experiment was conducted over a 72-h period at Platform Harvest off the Pacific Coast. Colocated tide gauge and LiDAR measurements were used as in situ data. Two anomalies were observed in the experiment: 1) multiple peaks in the cross correlation waveform from one polarization of Ku-band frequency and 2) decrease in signal-to-noise ratio from loss of a data channel. When the instances of multiple peaks were eliminated and the equivalent bandwidth recomputed using only the active channels, SSH error from these cases agreed well with the model prediction. Application of SoOp wideband altimetry will, therefore, require a monitoring capability to identify changes in the transmission spectrum, total power, and waveform shape, for quality control and setting an appropriate observation error covariance. Measurement precision from a satellite receiver is predicted to be between 4 and 6 cm using the error model. SoOp altimetry with these signals may improve coastal measurements and increase the sampling and revisit rate through the use of a constellation of small satellites.

Microwave radiometers measure weak thermal emission from the Earth, which is broadband in nature. Radio frequency interference (RFI) originates from active transmitters and is typically narrow band, directional, and continuous or intermittent. The Global Precipitation Measurement (GPM) Microwave Imager (GMI) has seen RFI caused by ocean reflections from direct broadcast and communication satellites in the shared 18.7-GHz allocated band. This paper focuses on the use of a complex signal kurtosis algorithm to detect direct broadcast satellite (DBS) signals at 18.7 GHz. An experiment was conducted in August 2017 at the Harvest oil platform, located about 10 km off the coast of central California. Data were collected for direct and ocean reflected DBS transmissions in the K- and Ku-bands from a commercial geostationary satellite. Results are presented for the complex kurtosis performance for a five-channel quadrature phase-shift keying (QPSK) signal versus the seven-channel case. As the spectrum becomes more occupied, detector performance decreases. Filtering of RFI in the fully occupied spectrum is very difficult, and detection using the complex kurtosis detector is only possible for very large interference-to-noise ratio (INR) values at -5 dB and higher. This corresponds to over 100 K in a real system such as GMI; therefore, other detection approaches might be more appropriate.

In this paper, we develop a hybrid method combining T matrix of single objects and Fold-Lax multiple scattering theory (FL), for full wave simulations of vegetation/trees. The hybrid method of solving the Maxwell equations consist of off-the-shelf technique for single objects (e.g. HFSS) and newly developed techniques. The newly developed techniques are the three key steps of the hybrid method: (1) extracting the T matrix of each single object, (2) numerical wave transformations and (3) solving the coupled wave interaction equations (i.e. FL) for all the objects. For step (1), we extract the T matrix of a single object by numerical integration with the use of HFSS which is a 3D full-wave electromagnetic field simulation tool. The method of T matrix extraction from HFSS is verified by comparison with the analytical solution of a sphere. The method is applicable to find the T matrix for complicated object where the analytical solution is not available. Then, the wave transformations are performed based on the translation addition theorem. Numerical methods of calculating the transformation coefficients are developed. Finally, the wave interactions among the single objects are accounted for by FL. The results of the hybrid method agree with those of the HFSS brute force method. In comparison, the hybrid method is much more efficient than HFSS for vegetation scattering and applicable to large problems such as full wave simulations of a tree.

The objective of this paper is to model and study the sensitivity of bistatic microwave scattering versus changes in wetland characteristics as observed by a GNSS-R satellite system such as CYGNSS. We develop a simplified scattering model starting from the Water Cloud Model traditionally used in monostatic radar problems. Vegetation is idealized as a cloud of randomly oriented scattering elements over a rough surface representing either soil or water. The bistatic scattering coefficient is modeled as the incoherent sum of soil, water and vegetation scattering weighted by the fraction of each contribution within the CYGNSS footprint. The model is tested against CYGNSS observations across the Everglades National Park for which high-resolution land-cover and water depth maps are available. We show that our simplified model is able to capture to first order the variability of bistatic scattering versus changes in water depth and water fraction. This effort is a step forward towards the development of an effective algorithm to map the dynamic state of tropical wetlands and other regions subject to flooding using CYGNSS measurements.

The CYGNSS mission is a new GNSS-R ocean remote sensing constellation of eight small spacecraft. The measurements from CYGNSS allow for the first time a large, densely sampled, and calibrated GNSS-R dataset to be used for ocean surface altimetry. Re-tracking of the reflected signal correlation waveform is one of the most significant performance limiting components of ocean altimetry retrievals. This work demonstrates the use of empirical methods and physical scattering delay/delay-Doppler waveform models, fit to the measured correlation functions in a least-squares sense, to retrieve the specular reflection delay. We examine the performance of waveform modelling methods as compared to single-point re-tracking methods for the first time with a spaceborne dataset. Particular attention is given to the presence of narrow waveforms in the data set, indicative of coherent, rather than purely diffuse reflection. Re-tracking performance is quantified with statistics of the accuracy and precision and examples are presented for returns demonstrating both diffuse and coherent characteristics.

This paper presents results from a proof-of-concept experiment conducted at Platform Harvest to measure Sea Surface Height (SSH) using Ku- and K-band `Signals of Opportunity' (SoOp) from DirecTV Direct Broadcast Satellite (DBS) system. The retrieved SSH was compared with the SSH measurement from a tide gauge located at the platform; the error in retrieval of Ku-band and K-band was found to be 2.78 cm and 2.58 cm, respectively. This matched the model for error with some differences that can be attributed to the difference in spatial and temporal characteristics of the SoOp and tide gauge measurements. Finally, this paper gives an overview of temporal and spatial sampling possible from a constellation of receivers observing Ku-band SoOp to resolve mesoscale eddies in coastal regions.

This paper shows results from a proof-of-concept tower experiment that computed phase from a reflected P-band signal. The change in phase of the reflected signal is related to SWE for dry snow and snow depth for wet snow and the rate of change of phase is directly correlated to frequency of observation. The effect of vegetation was also evaluated by using measurements from two towers: one with no vegetation and one surrounded by small trees with heights of up to 3 meters. The phase measurement from the two sites had excellent correlation of 0.99 during the accumulation phase. The correlation between SWE and phase measurement was found to be between 0.95 and 0.98 during the accumulation phase. During the melt phase, negative correlation between 0.68 and 0.80 was found between snow depth and phase measurement.

The P-band Signals of Opportunity (SoOp) technique has significant potential for remote sensing of root zone soil moisture from space. We have conducted a proof-of-concept experiment to demonstrate the sensitivity of P-band reflectivity to soil moisture. The reflectivity data has a high correlation (~0.87) with the in situ soil moisture observations. Theoretical forward modeling and retrieval analyses have been carried out to assess the accuracy of using multifrequency SoOp data to retrieve the vertical soil moisture profile. Sensitivity analysis has also been carried out to determine the impact of ancillary data.

The Cyclone Global Navigation Satellite System (CYGNSS) mission was designed to study hurricane intensification by measuring wind speeds in tropical cyclones. However, the delay-Doppler maps (DDM) that are produced can be used to estimate the sea surface height (SSH) at the specular reflection point on the ocean surface. Proof-of-concept studies that DDMs are suitable to solve for SSH have been recently reported (Clarizia et al., 2016; Mashburn et al., 2018), based on data acquired by the demonstration satellite experiment Tech Demo Sat -1 (TDS-1) carrying a GNSS-R receiver similar to the ones onboard CYGNSS. Although the precision of each 1-sec averaged SSH is considerably lower than that of the existing satellite altimeters, by virtue of the dense coverage and frequent revisit time exhibited by the constellation of 8 microsats, the error may be smoothed down considerably by optimal interpolation (Li et al., 2016). Hence the CYGNSS dataset presents a potential opportunity to sample the tropical oceans, and investigate the sensitivity of the SSH measurements to mesoscale eddies.

Characterizing, understanding, and projecting changes in atmospheric methane and terrestrial water storage require information about wetland dynamics, which remains a major gap in existing knowledge. Despite recent advances in satellite monitoring techniques, dynamic wetland mapping needs to be improved for more accurate global inventories and also to monitor variabilities at sub-km scale over multiple decades. It has been shown that Global Navigation Satellite Systems (GNSS) Reflectometry (GNSS-R) signatures off inundated wetlands can be identified under different vegetation conditions including a dense rice canopy and a thick forest with tall trees, where optical sensors and monostatic radars provide limited capabilities. In this study, we will further investigate the capabilities of the GNSS-R technique for wetland monitoring from aircraft platforms.

Spatial and temporal variations in near-surface soil moisture are important to measure for climate studies, numerical weather forecasts, and drought monitoring. Several previous studies have shown success in using ground-reflected Global Navigation Satellite System (GNSS) signals as a form of bistatic radar to sense soil moisture. However, the ability of this type of data to sense soil moisture variations from space is still a nascent field of study. In the past two years, three satellites have been launched that were either designed to capture ground-reflected GNSS signals or have been modified to record these signals. The data provided by these satellites are giving scientists an unprecedented opportunity to investigate their ability to detect changes in Earth's land surface, including but certainly not limited to near-surface soil moisture. This paper will present spaceborne observations of ground-reflected GNSS signals and evaluate their sensitivity to near-surface soil moisture. This sensitivity will be compared to empirical and theoretical sensitivities of monostatic L-band radar measurements to soil moisture. We will also comment on possibilities for retrieval algorithm development, using techniques employed for monostatic radar as a guide.

It is now recognized that the enormous challenge of scientifically understanding the Earth system requires careful strategic decisions on what missions are deployed. In a recent report, the National Research Council developed a “value framework” for Earth observing systems with a focus on prioritizing NASA observations that merit long-term continuity. In this paper, we refer to this framework to discuss how high value observations arise from opportunistic use of signals generated by Global Navigation Satellite Systems (GNSS) such as GPS. The increasing number of GNSS constellations internationally, likely to be permanently deployed, suggests that the geosciences community will benefit by adopting these signals for a variety of remote sensing needs. We describe recent progress in using these observations scientifically and developing technology to exploit them. We conclude that dedicated constellations of GNSS science instruments in low Earth orbit capable of receiving both direct and reflected GNSS signals will provide excellent science return in a broad range of areas, and constitute a high value Earth observing system.

Coastal altimetry plays a prominent role in measuring the total water-level envelope directly, and is one of the key measurements required by storm surge applications and services. It can also provide important information about the wave field, leading to development of more realistic wave models and therefore improving forecasts of wave setup and overtopping processes. Satellite altimeters have a long history of mapping the variability of the Earth's open ocean. However, this is not the case for coastal areas because of the limitations of technology and difficulties in processing and interpretation of data near coastal surface (due land contamination and rapid variations due to tides and atmospheric effects). There is, therefore, a need for more accurate Sea Surface Height (SSH) near coastal areas. Bistatic altimetry using signals of opportunity (SoOp) (e.g. digital communication signals) may provide additional measurements in coastal areas through oblique incidence angles and high bandwidth (400 MHz). In this study, we investigate the capabilities of SoOp technique for coastal altimetry from spaceborne platforms.

Snow water equivalent (SWE) storage is critical parameters of the water cycle and may be important indicators of climate change. Despite their importance in the seasonal and regional terrestrial water cycle, SWE is currently poorly characterized in space and time. We develop a method for observations of these parameters using P-band signals of opportunity (SoOp) concept to measure the SWE. Effect of wet snow on the measurement is analyzed through modeling and it is found that for wet snow, the phase of the SoOp measurement becomes correlated to snow depth while for dry snow, phase is correlated to the Snow Water Equivalent (SWE). In addition, qualitative data analysis from two different site for a proof-of-concept experiment is shown in this paper.

This paper provides a theoretical basis for retrieving snow water equivalent (SWE) and root zone soil moisture (RZSM) by using the coherent reflected signal from the communication satellite at P-band signals of opportunity. Based on theoretical modeling, the wave propagation constant in the snow is proportional to the snow density. It is shown that the phase change of reflected signal from snowpack has a quasi-linear dependence on SWE. The model has been extended to multilayer to accommodate the various vertical snow profiles. In addition, the P-band reflectivity is also sensitive to the change of root zone soil moisture. In the paper, we also shown the reflectivity calculated using coherent wave approach has excellent sensitivity to the change of soil moisture for moderate range of incidence angles. In order to validate the theoretical results, a proof of concept ground-based experiment is conducted at Fraser, CO since 2015.

This letter provides a proof-of-concept experiment and validation of an error model for bistatic altimetry using signals of opportunity (SoOps). Coastal sea surface height plays a prominent role in measuring the total water-level envelope directly and is one of the key quantities required by storm surge applications and services. Nadir satellite altimeters have a long history of mapping the variability of the earth's open ocean. However, they exhibit problems operating in coastal areas due to the effects, such as land contamination, rapid variations due to tides, and atmospheric effects. One technique for filling this gap is bistatic altimetry using SoOp (e.g., digital communication signal reflections). In this letter, we investigate capabilities of this technique. Twenty three days of data were collected at platform harvest from a single channel of the Ku-Band direct broadcast satellite. The wind speed observed during the experiment was between 4 and 14 m/s and significant wave height was between 0.7 and 4 m as measured by buoy 46 218 located 8 km away. The standard deviation in the estimation of height was found to be 7.2 cm (the same as predicted from theory). Using a least-squares approach improved the precision reducing the standard deviation to 6.8 cm. It is shown that the error in the estimation of height can be reduced to 3.5 cm by utilizing the full bandwidth (all the channels) of the SoOp. Extrapolating these results, we predict a precision of 5.3 cm from a typical (e.g., Jason) orbit of 1380 km.

A proof-of-concept experiment was carried out to demonstrate the feasibility of retrieving snow water equivalent (SWE) using P-band signals of opportunity. The fundamental observation is the change in the phase of the reflected waveforms as related to the change in SWE. Through theoretical modeling it was found that the change in SWE was approximately linearly dependent on the change in phase. This was verified by retrieving SWE data collected and processed from a tower-based experiment at Fraser, CO, USA. A linear regression was performed on measured phase and in situ SWE. The correlation was found to be 0.94 and root mean square deviation was found to be 7.5 mm.

Global Navigation Satellite System (GNSS) signals can be used as a kind of bistatic radar, with receivers opportunistically recording ground-reflected signals transmitted by the GNSS satellites themselves. The ground-reflected signals are sensitive to changes in surface permittivity, which for L-band is primarily a function of the moisture content of the surface. Here, we investigate the ability of GNSS signals, as recorded by a GPS receiver flown on a satellite, to measure changes in wetland extent and flood inundated area. We find that the ground-reflected signals give similar results as flood-inundation maps derived from other sources. Reflected power increases of over 10 dB in the vicinity of wetlands indicates that these signals could successfully map changes in wetlands around the globe.

This paper talks about retrieval of Snow Water Equivalent (SWE) using P-band Signals of Opportunity (SoOp). Modeling is done to show that the phase change in the observed signal is primarily due to change in SWE and is independent of snow density, soil moisture, snow grain size. In order to compare theory to experiment, experiment is conducted at Fraser, CO. Some preliminary data analysis from 1 week of data show that the phase changed when SWE changed.

This paper compares the retrieval of significant wave height (SWH) from reflected signals of opportunity in the L-band, S-band, and Ku-band. The fundamental observation is the time series of the interferometric complex field (ICF) of the reflected signal. A known relationship between the coherence time of the ICF time series (width of the ICF autocorrelation function) and SWH of the ocean is used for this retrieval. This relationship is applied to data recorded at three frequencies in the S-band, L-band, and Ku-band. The accuracy obtained for S-band and L-band were on the order of 0.4 m, but this model saturated around the SWH value of 3 m. A secondary algorithm used spectral bandwidth to estimate SWH. This model showed linear dependence, with the error also around 0.4 m. The ICF coherence time for Ku-band signals showed little sensitivity to SWH. Furthermore, the effect of fully developed sea, wind, and wave directions on SWH retrieval is analyzed.

The Global Navigation Satellite System (GNSS) reflectometry, i.e. GNSS-R, is a novel remote-sensing technique first published in [1] that uses GNSS signals reflected from the Earth's surface to infer its surface properties such as sea surface height (SSH), ocean winds, sea-ice coverage, vegetation, wetlands and soil moisture, to name a few. This communication discusses the scientific value of GNSS-R to (a) furthering our understanding of ocean mesoscale circulation toward scales finer than those that existing nadir altimeters can resolve, and (b) mapping vegetated wetlands, an emerging application that might open up new avenues to map and monitor the planet's wetlands for methane emission assessments. Such applications are expected to be demonstrated by the availability of data from GEROS-ISS, an ESA experiment currently in phase A [2], and CyGNSS [3], a NASA mission currently in development. In particular, the paper details the expected error characteristics and the role of filtering played in the assimilation of these data to reduce the altimetric error (when averaging many measurements).

This paper presents a semi-empirical relationship between the coherence time of ocean-reflected digital satellite radio signals and significant wave height (SWH). An experiment was conducted on Platform Harvest, a petrochemical exploration platform located in the Pacific Ocean off the coast of Southern California. Raw data were recorded from both directly-received and ocean-reflected signals from the XMradio (S-bandwith center frequency 2.342 GHz), and DirecTV (Ku-Band with center frequency 12.239 GHz) from antennas located at an approximate altitude of 27 meters above the ocean surface. Using a technique first developed for Global Navigation Satellite Systems (GNSS) signals, the coherence time of the Interferometric Complex Field (ICF) is computed by cross-correlating the direct and reflected signals. A relationship is developed between the ICF and SWH. This is done by first developing a model showing a relationship between ICF, correlation time of the sea surface and SWH. A Monte Carlo simulation of the ocean surface is used to show that empirical relationship between correlation time of the sea surface and SWH takes a quadratic form. The coefficients of this empirical model are finally determined from fitting experimental data of S-band signals and a limited amount of Ku-band signals, from the XM radio and DirecTV transmission, respectively. The retrievals of SWH from experimental data through inverting this relationship are then compared to the in-situ recordings from the nearest buoy (8 km away). S-band measurements are found to have a standard deviation of 0.51 meters over a range of SWH from 1 to 5.6 meters.

In this paper, we present a study of relevant correlation properties of signals transmitted from commercial DirecTV satellite signals with the purpose of evaluating their potential use as “signals of opportunity” for bistatic remote sensing. The ambiguity function of the DirecTV satellite signal is computed analytically from published information on the modulation schemes and bandwidth, under the assumption that the data modulation is random. This model is experimentally tested by recording the received signals from the satellites. Also, the signal to noise ratio (SNR) is computed from the published information. The coherence time of the DirecTV signal is computed using the experimental data from two direct signals that is recorded with two different clock sources. An experiment is done to record some reflected data from a river and the coherence time of the Interferometric Complex Field (ICF) from this reflected data is computed for the purposes of measuring the sea state.

Remote sensing of ocean roughness using reflected signals from digital communication satellites is demonstrated in an airborne experiment. Transmitted data are approximated as an infinitely long sequence of random bits, which is experimentally a hypothesis confirmed for the S-band XM radio signal. On July 2, 2010, a signal recorder was flown at an altitude of 3.17 km off the coast of Virginia, collecting ocean-reflected signals from both geostationary satellites identified as “Rhythm” and “Blues,” which were broadcasting the XM radio signal. Direct and reflected signals from the same channel were cross-correlated, producing a waveform that agreed well with a model generated at the 7.5-m/s wind speed reported from the Chesapeake Lighthouse. Adjusting this model to fit the experimental data produced an optimal estimate of 6 m/s. A Monte Carlo approach predicted errors of 0.5% from the simulated reflected XM radio signals and 2%-10% from simulated reflected Global Navigation Satellite System (GNSS-R) signals. This improvement was attributed to the higher ( ~ 30 dB) power in the XM radio signal. The availability of communication satellite transmissions, in all frequency bands used for remote sensing, opens the possibility of using signals of opportunity as low-cost alternatives to radiometry or scatterometry.

This paper present experimental results demonstrating the use of "signals of opportunity" from digital communications satellites (XM radio) as bistatic radar for ocean remote sensing. This builds upon the previous work which demonstrated that the shape of the cross-correlation "waveform" of reflected XM radio signals is sensitive to the roughness of the ocean surface. In these new results, we compare this sensitivity between the waveforms produced from the two XM radio satellites, viewed simultaneously at different azimuths, and show that a small discrepancy exists in the mean square slope (MSS) retrievals obtained from each of them. We then investigate the hypothesis that this discrepancy is the result of neglecting anisotropy in the model for the probability density function (PDF) of surface slopes and that this discrepancy might be useful for sensing the wind direction. In order to do so, a two-stage estimation process was applied to data collected on an airborne experiment that recorded the direct line of sight and reflected XM radio signals. In the first step, an isotropic normal distribution was assumed for the PDF and the mean square slope (MSS) was fit to the measured waveform data from each satellite independently. Since the two satellites are located at different azimuths, a difference between the two MSS estimates were observed. The second step involved using a bidirectional normal PDF with MSS constrained to that obtained from the first step, and a value was assumed for the ratio of upwind and crosswind slopes. The direction of the principal axes was varied to minimize the total residuals for both satellites. The results were compared with Chesapeake Lighthouse recordings of the local wind direction.

This paper presents a study of relevant correlation properties of signal transmitted from commercial communication satellites in order to evaluate their potential use as “signal of opportunity” for bistatic remote sensing. The ambiguity function for the XM radio satellites was computed analytically from published information on the modulation schemes and bandwidth, under the assumption that the data modulation is random. The model was then experimentally tested by recording the received signals from these satellites. Next, a cross-correlation waveform for digital signal reflected from random rough surface was simulated. Scattering model that were originally developed for Global Navigation Satellite System (GNSS-R) signals was applied to the modified simulator to incorporate the derived ambiguity function. The simulator was then used to generate synthetic waveform with a realistic signal to noise ratio (SNR). Retrieval algorithms for ocean surface roughness and reflectivity that were derived originally for GNSS-R, were applied to these simulated signals. Non-linear least square methods were applied to invert a scattering model and estimate the slope variances of the probability density function (PDF), which best fits the measurements of the reflected XM signal waveform. The SNR for the experimental data was found to be within 0.5dB of the theoretically calculated SNR.