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.

This study presents a comparison of Spire Global, Inc. carrier phase Global Navigation Satellite System-Reflectometry (GNSS-R) dual-frequency signals under grazing angle (GG-R) signal coherency and altimetric retrievals, with ice concentration from January to October 2021 over the Hudson Bay region. Monthly analysis reveals higher coherency during periods when ice is present. Additionally, a greater number of retrievals are derived from January to May (winter) compared to the period from July to October (summer), with the exception of July, which exhibits exceptionally larger coherency. The comparison between summer and winter periods show a difference of approximately 6 cm in Root Mean Square Error (RMSE) and 21 cm of standard deviation between the reference surface model and the GG-R altimetry results. Moreover, there are 62% fewer retrievals from January to October in the months with no ice presence.

Global Navigation Satellite System Reflectometry (GNSS-R) represents an emerging concept to retrieve geophysical parameters. In this contribution, we describe and assess the University of Luxembourg (UL) large-scale near-surface soil moisture product. The model applies Cyclone GNSS (CYGNSS) observations to retrieve daily soil moisture content at 9 km, using a linear regression and Soil Moisture Active Passive (SMAP) as reference. It selects a subset of the delay-Doppler maps (DDMs) to estimate surface reflectivity, which are normalized with respect to incidence angle using a strategy that accounts for the spatial variations of surface roughness. Our assessment of almost three years of data showed a median unbiased root-mean-square error (ubRMSE) of 0.043 cm3cm-3with respect to SMAP and of 0.056 cm3cm-3with respect to 224 in-situ sites. Our product is available upon request.

In this work, Spire Global, Inc. carrier phase of GNSS dual-frequency reflected signals under grazing angle (GG-R) are used for sea surface altimetry over the Java Sea, Indonesia. Processing of 714 GG-R coherent profiles from January 2020 to October 2021 is carried out to retrieve the relative surface height from WGS84 applying two methods to remove the ionospheric delay: an ionosphere-free combination and smoothing of the frequency-specific correction based on dual-frequency carrier phase observations. A substantial improvement of ∼ 8 cm is observed in the root mean square error (RMSE) between the GG-R retrievals and the reference surface model using the frequency-specific ionospheric correction method.

Large-scale near-surface soil moisture can be retrieved from Global Navigation Satellite System Reflectometry (GNSS-R) surface reflectivity observations, which are dependent on the signal incidence angle and therefore need to be normalized. Using 4 years of Cyclone GNSS (CYGNSS) data, in this study we propose a new method for this normalization, accounting for the spatially varying effects of coherent and incoherent scattering. The method is based on a linear regression between the gridded incidence angle and surface reflectivity. We applied the normalized surface reflectivity observations in our soil moisture retrieval algorithm and found a median unbiased root-mean-square error (ubRMSE) of 0.0504 cm3cm-3using the Soil Moisture Active Passive (SMAP) as the reference, an improved result compared to other incidence angle correction methods described in the literature.

This paper presents high-precision phase altimetric retrievals using the GNSS reflected signals off of sea-ice under grazing angle geometries collected by the Spire Global, Inc. satellite constellation. The region and period selected for this study (Hudson Bay and James Bay from January to March 2021) were determined to be the most suitable and steady region as the starting point for the analysis of sea-ice covered areas. The difference between grazing angle GNSS-R retrievals from more than 500 coherent reflection profiles and a reference surface (composed of the DTU mean sea surface and TPXO ocean tide model) has a root-mean-square error (RMSE) of 16.4 cm. This demonstrates that consistently meaningful altimetry retrievals can be achieved under grazing angle GNSS reflectometry (GG-R) geometries using dual-frequency phase measurements collected by nanosatellites in low-Earth orbit over sea-ice covered areas.

peer reviewed

Global navigation satellite system (GNSS) multipath reflectometry (MR) has emerged as a new technique that uses signals of opportunity broadcast by GNSS satellites and tracked by ground-based receivers to retrieve environmental variables such as snow depth. The technique is based on the simultaneous reception of direct or line-of-sight (LOS) transmissions and corresponding coherent surface reflections (non-LOS). Until recently, snow depth retrieval algorithms only used legacy and modernized GPS signals. Using multiple GNSS constellations for reflectometry would improve GNSS-MR applications by providing more observations from more satellites and independent signals (carrier frequencies and code modulations). We assess GPS and GLONASS for combined multi-GNSS-MR using simulations as well as field measurements. Synthetic observations for different signals indicated a lack of detectable interfrequency and intercode biases in GNSS-MR snow depth retrievals. Received signals from a GNSS station continuously operating in France for a two-winter period are used for experimental snow depth retrieval. We perform an internal validation of various GNSS signals against the proven GPS-L2-C signal, which was validated externally against in situ snow depth in previous studies. GLONASS observations required a more complex handling to account for topography because of its particular ground track repeatability. Signal intercomparison show an average correlation of 0.922 between different GPS snow depths and GPS-L2-CL, while GLONASS snow depth retrievals have an average correlation that exceeds 0.981. In terms of precision and accuracy, legacy GPS signals are worse, while GLONASS signals and modernized GPS signals are of comparable quality. Finally, we show how an optimal multi-GNSS combined daily snow depth time series can be formed employing variance factors with a ~59%-90% precision improvement compared to individual signal snow depth retrievals, resulting in snow depth retrieval with uncertainty of 1.3 cm. The developed combination strategy can also be applied for the European Galileo and the Chines BeiDou navigation systems.