This paper quantifies the relationship between forward scattered L‐band Global Navigation Satellite System (GNSS) signals, recorded by the Cyclone Global Navigation Satellite System (CYGNSS) constellation and Soil Moisture Active Passive (SMAP) soil moisture (SM). Although designed for tropical ocean surface wind sensing, the CYGNSS receivers also record GNSS reflections over land. The CYGNSS observations of reflection power are compared to SMAP SM between March 2017 and February 2018. A strong, positive linear relationship exists between changes in CYGNSS reflectivity and changes in SMAP SM, but not between the absolute magnitudes of the two observations. The sensitivity of CYGNSS reflectivity to SM varies spatially and can be used to convert reflectivity to estimates of SM. The unbiased root‐mean‐square difference between daily averaged CYGNSS‐derived SM and SMAP SM is 0.045 cm³/cm³ and is similarly low between CYGNSS and in situ SM. These results show that CYGNSS, and future GNSS reflection missions, could provide global SM observations.

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.

The 2017 Atlantic Hurricane Season was one of the most active and destructive on record, leading to significant flooding in many parts of the United States and the Caribbean. During flooding events such as these, there is an urgent need to quickly map in detail which areas have been severely affected, yet current satellite missions are not capable of sampling the global land surface at high enough spatio-temporal scales for flooding applications. Here, we demonstrate a novel approach to high-resolution flood mapping by repurposing data from the new NASA mission, CYGNSS. The CYGNSS multi-satellite constellation was designed for frequent temporal sampling of the ocean surface in the tropics. We demonstrate that CYGNSS data provide clear signals of surface saturation and inundation extent over land at higher spatio-temporal resolution than radiometers like SMAP. Using a simple thresholding technique, we are able to estimate that approximately 32,580 km² of land area in Texas flooded during Hurricane Harvey, and approximately 7210 km² of land area flooded in Cuba during Hurricane Irma, or about 7% of Cuba’s total area.

Information about wetland dynamics remains a major missing gap in characterizing, understanding, and projecting changes in atmospheric methane and terrestrial water storage. A review of current satellite methods to delineate and monitor wetland change shows some recent advances, but much improved sensing technologies are still needed for wetland mapping, not only to provide more accurate global inventories but also to examine changes spanning multiple decades. Global Navigation Satellite Systems Reflectometry (GNSS‐R) signatures from aircraft over the Ebro River Delta in Spain and satellite measurements over the Mississippi River and adjacent watersheds demonstrate that 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. Advantages as well as constraints of GNSS‐R are presented, and the synergy with various satellite observations are considered to achieve a breakthrough capability for multidecadal wetland dynamics monitoring with frequent global coverage at multiple spatial and temporal scales.

GNSS reflection measurements in the form of delay-Doppler maps (DDM) can be used to complement soil measurements from the SMAP Mission, which has a revisit rate too slow for some hydrological/meteorological studies. The standard approach, which only considers the peak value of the DDM, is subject to a significant amount of uncertainty due to the fact that the peak value of the DDM is not only affected by soil moisture, but also complex topography, inundation, and overlying vegetation. We hypothesize that information from the entire 2D DDM could help decrease uncertainty under various conditions. The application of deep-learning-based techniques has the potential to extract additional information from the entire DDM, while simultaneously allowing for the incorporation of additional contextual information from external datasets. This work explored the data-driven approach of convolutional neural networks (CNNs) to determine complex relationships between the reflection measurement and surface parameters, providing the groundwork for a mechanism to achieve improved global soil moisture estimates. A CNN was trained on CYGNSS DDMs and contextual ancillary datasets as inputs, with aligned SMAP soil moisture values as the targets. Data were aggregated into training sets, and a CNN was developed to process them. Predictions from the CNN were studied using an unbiased subset of samples, showing strong correlation with the SMAP target values. With this network, a soil moisture product was generated using DDMs from 2017–2019 which is generally comparable to existing global soil moisture products, and shows potential advantages in spatial resolution and coverage over regions where SMAP does not perform well. Comparisons with in-situ measurements demonstrate the correlation between the network predictions and ground truth with high temporal resolution.

This study presents a framework for spatially interpolating Cyclone Global Navigation Satellite System (CYGNSS) data over land. The method, Previously-Observed Behavior Interpolation (POBI), assumes that reflectivity data can be interpolated at unsampled locations by quantifying how past observations of reflectivity at the unsampled location varied with past observations of reflectivity at nearby locations. The POBI method is able to recreate truth reflectivity maps with a mean error of 0.17 dB (standard deviation 1.96 dB) and preserves the heterogeneous and discontinuous nature of reflectivity observations. This method is useful for applications that necessitate rapidly-updated data but have some tolerance for uncertainty in interpolated points.