Integration of IEM_B, ISMN and Sar Sentinel-1 Data for Accurate Soil Moisture Estimation Using Neural Networks

This work focuses on the development of a fully-automated integration approach, which seeks to combine data from multiple sources with the aim of implementing a dependable soil moisture estimation framework based on Neural Networks (NNs). Several papers have dealt with inverse modeling of soil moisture using NNs, often trained by way of Synthetic Aperture Radar (SAR) data generated through the Integral Equation Model (IEM); our approach is designed to harness the newer IEM calibrated version modified by Baghdadi (IEM_B), integrating synthetic data with real data in order to monitor possible improvements in NNs estimation efficiency. The experiment involves two steps: a first NN is trained with SAR Sentinel-1 data (taken from the Google Earth Engine, GEE, Catalog and granted freely by the European Union, EU, under the Copernicus programme) and in situ soil moisture measurements, taken from the International Soil Moisture Network (ISMN); in the second part, a further NN is trained by enlarging the training dataset with IEM_B generated SAR data. Combination of the two large-scale data sources and IEM_B generated data may lead to an improvement in separating soil/vegetation contributions, possibly improving NN soil moisture estimation accuracy; the study presented may serve as a baseline demonstration for future exploitation of the large-scale data sources for both general and field-specific soil moisture estimation.