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.

Unmanned Aircraft Systems (UAS) play an essential role in providing high-resolution information for precision agriculture (PA). Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) from a UAS can provide higher spatial and temporal resolution for soil moisture (SM) retrievals. This study summarizes and analyzes of a three-year-long field campaign including comprehensive GNSS-R and ancillary data from crop fields. The field data collections were conducted on 210 by 110 m (2.31 ha) corn and cotton fields over 3 years from 2021 to 2023. The results indicate that high-resolution SM measurement can be achieved with a low-cost GNSS-R system onboard a mid-size UAS platform for use in PA applications.

Satellite-based remote sensing observations play an important role in retrieving soil moisture over the earth’s surface. NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission has gained attention as it uses the Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) which can provide higher spatial and temporal resolution. Research is going on to improve retrieval algorithms using CYGNSS observation. In addition to the CYGNSS observations, different land surface products are leveraged to characterize the underlying surface conditions. The most commonly used features are from the Normalized Difference Vegetation Index (NDVI) and the Vegetation Water Content (VWC) from Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. Since the MODIS satellite operates on optical bands that can be greatly affected by cloud coverage, this study proposes using the SENTINEL-1 satellite which offers all-weather, day, and night measurement capability. This study utilized the SENTINEL-1 cross ratio of VH/VV as an alternative to MODIS-based vegetation indices. The results of the study showed that the SENTINEL-1 cross ratio of VH/VV can be significantly useful in CYGNSS-based SM retrieval models by including the effect of vegetation.

NASA Cyclone Global Navigation Satellite System (CYGNSS) mission has gained attention within the land remote sensing community for estimating soil moisture (SM) by using the Global Navigation System Reflectometry (GNSS-R) technique. CYGNSS constellation generates Delay-Doppler Maps (DDM) that contain valuable earth surface information from GNSS reflection measurements. Existing approaches use predefined features from DDMs to estimate SM. This pa-per presents a deep-learning framework to learn optimal features from DDMs for estimating SM. The proposed approach is applied over the Continental United States (CONUS) by leveraging CYGNSS DDM observations with ancillary re-motely sensed geophysical data. The model is trained and evaluated using the Soil Moisture Active Passive (SMAP) mission's enhanced SM products at a$9\text{km}\times 9\text{km}$resolution with vegetation water content less than$5kg/m^{2}$. The mean unbiased root-mean-square difference (ubRMSD) between CYGNSS and SMAP SM retrievals from 2017 to 2020 is 0.0362$m^{3}/m^{3}$with a correlation coefficient of 0.9309 over 5-fold cross-validation.

Global soil moisture mapping at high spatial and temporal resolution is important for its related meteorological, hydrological, and agricultural applications. Using the L-band signals, several satellite-based microwave sensors are providing global soil moisture retrievals at a spatial resolution of about 40 km and a revisit time of 2–3 days. Recent research shows that the forward scattered Global Navigation Satellite System (GNSS) signals at L-band can convey high-resolution information of land surface conditions, including surface soil moisture. However, these signals are often affected by complex land surface characteristics and the bistatic nature of GNSS-R technique, leading to nonlinear relation between the signals and surface soil moisture. In this work, a machine learning (ML) approach is used to map quasi-global soil moisture from Cyclone GNSS (CYGNSS) observables. Specifically, several land surface parameters are obtained and used in combination with CYGNSS data in the ML model by using the Soil Moisture Active Passive (SMAP) data as reference. A good performance of the ML method is achieved with median ubRMSDs of 0.0426 m3/m3and 0.034 m3/m3for global coverage and regions with vegetation water content less than 4 kg/m2, respectively. Moreover, an independent evaluation of the CYGNSS data against in-situ measurements suggests that the overall accuracy of CYGNSS soil moisture is comparable with SMAP data. With an increased sampling frequency of CYGNSS, the generated products can supplement current global soil moisture database. In addition, the ML-based CYGNSS products are published via a website portal for future users11https://www.gri.msstate.edu/research/ssm/.

High Spatio-temporal soil moisture is essential for many meteorological, hydrological, and agricultural applications and studies. Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising opportunity for high-resolution soil moisture retrievals. NASA's Cyclone Global Navigation Satellite System is a preeminent GNSS-R application that offers high spatial and temporal resolution observations from Earth's surface. However, the quasi-random sampling of land surface by the CYGNSS constellation circumvents obtaining fully observed daily soil moisture predictions. This work investigates multidimensional spatial and temporal interpolation of the CYGNSS soil moisture estimates using methods such as linear, nearest, and natural interpolation. The results indicate that the interpolation error (RMSE) was 0.032$m^{3}/m^{3}$, 0.038$m^{3}/m^{3}$, and 0.030$m^{3}/m^{3}$for linear, nearest, and natural interpolation, respectively. The results also show that interpolated and observed CYGNSS SM values have the similar performance metrics when validated with the SMAP 9-km gridded SM product.

High spatio-temporal soil moisture is critical for the understanding of land-atmosphere interactions and affects meteorological, hydrological and agricultural applications. Currently, most satellite-based microwave sensors provide global soil moisture retrievals at ~40 km spatial and 2-3 days temporal resolution. Using the forward scattered L-band Global Navigation Satellite System (GNSS) signals, surface soil moisture can be estimated at higher spatial and temporal scales. However, due to the complex land surface characteristics and bistatic nature of GNSS signals, the retrieval algorithms for deriving surface soil moisture from GNSS signals are still under development. In this work, a machine learning (ML) algorithm has been used for estimating soil moisture from Cyclone Global Navigation Satellite System (CYGNSS) measurements. The in-situ data from International Soil Moisture Network and global soil moisture data from Soil Moisture Active Passive (SMAP) have been deployed as the reference data in the ML algorithm. In particular, various remote sensing-based land surface parameters have been included and facilitate a robust soil moisture retrieving process. The proposed approach has achieved an ubRMSD of 0.0523 m3/m3between the retrieved soil moisture from CYGNSS and in-situ measurements in a 5-fold cross-validation over 129 ground-based soil moisture sites, suggesting a satisfactory performance of the ML-based approach. Moreover, the global median ubRMSD of 0.042 m3/m3is obtained between SMAP and CYGNSS ML predictions. Surface soil moisture can be retrieved at ~9 km spatial and 1-2 days temporal scales through the presented framework.