Abstract
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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$9\text{km}\times 9\text{km}$</tex> resolution with vegetation water content less than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5kg/m^{2}$</tex> . The mean unbiased root-mean-square difference (ubRMSD) between CYGNSS and SMAP SM retrievals from 2017 to 2020 is 0.0362 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m^{3}/m^{3}$</tex> with a correlation coefficient of 0.9309 over 5-fold cross-validation.
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