| dc.description.abstract |
In the last few years, Global Navigation Satellite System-Reflectometry (GNSS-R) technology has provided an exciting solution to retrieve fine-scale spatiotemporal soil moisture predictions. The most promising data source for this task has been Cyclone Global Navigation Satellite System (CYGNSS) of the National Aeronautics and Space Administration (NASA). Several methodologies have been proposed in the literature to repurpose CYGNSS data for soil moisture estimation. In this work, we first describe the theoretical background of GNSS-R based soil moisture retrieval. We discuss the challenges associated with using GNSS-R (in particular CYGNSS) data, and present the current issues in the CYGNSS-based methodologies for estimating soil moisture. Some of the key limitations identified are in the areas of observation geometry, improper consideration of coherent and incoherent reflections, effect of vegetation on CYGNSS recording and consideration of ancillary data sources. We finally discuss potential Machine Learning based solutions for soil moisture estimation, which may provide researchers a path forward to achieve low error estimates. |
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