Abstract
GNSS reflection measurements can be calibrated with data from SMAP to yield estimates of soil moisture with enhanced spatiotemporal resolution useful to certain hydro-logical/meteorological studies. Current approaches use simple models of the relation between the DDM (delay-Doppler map) and soil moisture and can fail in certain regions of the planet. Complex information contained in the complete 2D DDM could help in these areas, and can be extracted through the application of deep learning based techniques. Our work explores the data-driven approach of convolutional neural networks to determine complex relationships between the reflection measurement and surface parameters. We developed a neural network trained using CYGNSS DDMs and ancillary datasets aligned with SMAP soil moisture values; the results of which are analyzed and compared to existing global soil moisture products.
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