Using the first full annual cycle of Cyclone Global Navigation Satellite System (CyGNSS) observations, we investigated the limitations and capabilities of CyGNSS observations for soil moisture (SM) estimates (0‐5 cm). A relative signal‐to‐noise ratio (rSNR) value from a CyGNSS‐derived delay‐Doppler map is introduced to improve the temporal resolution of SM derived from Soil Moisture Active Passive (SMAP) data. We then evaluated the CyGNSS‐derived rSNR using ground‐based SM measurements and the triple collocation method with SMAP and modeled SM products. We found that CyGNSS can provide useful SM estimates over moderately vegetated regions (correlation coefficient of the individual data (R_i): 0.77) but shows degraded performance over arid and densely vegetated regions (R_i: 0.68 and 0.67). However, when rSNR data is combined with SM data from SMAP, daily SM estimates can be achieved. These results show that synergistic use of CyGNSS observations can improve on SM estimates from current satellite systems.

Microwave remote sensing technology has been applied to produce soil moisture (SM) retrievals on a global scale for various studies and applications. However, due to the limitations of current technology, the native spatial resolution of currently available passive microwave SM products is on the order of tens of kilometers, and this resolution cannot be used to characterize SM variability on a regional scale. To overcome this limitation, a downscaling algorithm based on the thermal inertia theory–derived relationship between SM and temperature difference was developed using outputs from the Global Land Data Assimilation System–Noah Land Surface Model and the land long‐term data record–Advanced Very High Resolution Radiometer normalized difference vegetation index (NDVI) dataset and applied to the Aqua Moderate Resolution Imaging Spectroradiometer land surface temperature/NDVI data to produce a downscaled 1‐km Soil Moisture Active Passive (SMAP) radiometer daily SM product, respectively, at 6:00 a.m. and 6:00 p.m. on a global scale from 2015 to 2020. The evaluation results reveal that the downscaling model performs better in the middle or low latitudes than in high latitudes. It also performs better in warm months than in cold months. The in situ SM observations from dense networks around the world were used to validate the 1‐km and enhanced 9‐km SMAP SM data. The validation metrics indicated that both the 1‐km and 9‐km SM data have overall overestimation trends, and the unbiased RMSE (0.063 m ³ m ^–3 on average), mean absolute error (0.052 m ³ m ^–3 on average), and spatial standard deviation (0.025 m ³ m ^–3 on average) of the 1 km data are generally more accurate than the metrics of the 9‐km SM data, which indicates that the downscaled data provide reliable observed SM information.

Numerous large river basins of the world have few and irregular observations of the components of the terrestrial hydrological cycle with the exception of stream gauges at a few locations and at the outlet along with sparsely distributed rain gauges. Using observations from satellite sensors and output from global land surface models, it is possible to study these under-observed river basins. With populations greater than a billion people, some of these rivers (e.g., the Ganga-Brahmaputra, the Yangtze, the Nile and the Mekong) are the economic engines of the countries they transect, yet thorough assessment of their flow dynamics and variability in regard to water resource management is still lacking. In this paper, we use soil moisture (0-2m) and surface runoff from the NASA Global Land Data Assimilation System (GLDAS), evapotranspiration, and Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and rainfall from the Tropical Rainfall Measuring Mission (TRMM) and total water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) to examine variability of individual water balance components. To this end, understanding the inter-annual and intra-seasonal variability and the spatial variability of the water balance components in the major river basins of the world will help to plan for improved management of water resources for the future.