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Session A3: Atmospheric Effects, GNSS Remote Sensing and Scientific Applications

Open Source Soil Moisture Estimation From Spaceborne GNSS Reflectometry Data Fusion
Samuel Christelow, Paul Blunt, Stephen Grebby, Stuart Marsh, Nottingham Geospatial Institute, The University of Nottingham
Location: Seaview Ballroom
Date/Time: Wednesday, Jan. 24, 2:12 p.m.

As the impacts of climate change continue to increase, more and more populations face risks from natural hazards such as droughts, fires and flooding, the monitoring of which all rely on accurate and timely soil moisture data. Many of these areas of increasing risk are located in the global south, where funding for risk management and resilient infrastructure is often low. Therefore, an open source approach to risk management is especially effective - allowing effective action from governments and NGOs with tight budgets. To maximise the impact of this work, a fully open source algorithm and data sources are used. This paper describes a progress update and future direction on the development of an open-source soil moisture estimation product, developed via the data fusion of spaceborne GNSS Reflectometry (GNSS-R) and Synthetic Aperture Radar (SAR) data. Existing GNSS R soil moisture products provide very good temporal resolution but coarse spatial resolution, limiting their usefulness for hydrological modelling and disaster risk management. Therefore, data fusion of the high temporal but coarse spatial resolution GNSS R with lower temporal but very high spatial resolution SAR data. GNSS-R data used in this study is the CYGNSS UCAR/CU Soil Moisture product, alongside SAR backscatter data from Sentinel 1. The validation data product is the SMAP Enhanced L3 Global Daily Soil Moisture product, a well-established radiometry dataset. The GNSS-R and SAR data both have similar responses to the validation data when co-located, indicating that minimal scaling will be required when combining the two soil moisture measurements. However, errors are currently found to be large with an average of 40.8% difference between SAR and the validation data. This is to be expected at this early stage, and a plan for future improvements is laid out.

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