An Hybrid Approach for Soil Moisture Estimation with Sentinel Data
Résumé
We propose a methodology combining a change detection approach with a neural network algorithm to monitor soil moisture. The methodology utilizes Sentinel-1 and Sentinel-2 data, incorporating various metrics such as radar signals (VV and VH polarization), surface soil moisture index (I_SSM), radar incidence angle, normalized difference vegetation index (NDVI), and VH/VV ratio. In situ data from the International Soil Moisture Network (ISMN) across diverse climatic contexts are used for testing. The results demonstrate improved soil moisture estimations using the hybrid algorithms.