A Deep Learning Approach for Short-Term Soil Moisture Retrieval Using CYGNSS

In this study, short-term soil moisture values were estimated in the CONUS region using Cyclone Global Navigation Satellite System (CYGNSS) observations between August 2018 and October 2022 along with ancillary data including precipitation, temperature, normalized difference vegetation index (NDVI), land cover classification (LCC), elevation, and soil type. The study employs Long Short-Term Memory (LSTM) neural networks to demonstrate the effectiveness of this method in accurately estimating daily soil moisture at International Soil Moisture Network (ISMN) stations. Furthermore, the study analyzes SHapley Additive exPlanations (SHAP) values to understand how each feature contributes to the model's predictions.