Quasi-Global GNSS-R Soil Moisture Retrievals at High Spatio-Temporal Resolution from Cygnss and Smap Data

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 m
³
/m
³
and 0.034 m
³
/m
³
for global coverage and regions with vegetation water content less than 4 kg/m
²
, 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 users
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1
https://www.gri.msstate.edu/research/ssm/.