Soil Moisture Estimation From Smap Observations Using Long Short- Term Memory (LSTM)

Soil Moisture (SM) estimation is of growing interest. In the recent years, different Machine Learning (ML) methods were developed in order to better understand the spatio-temporal variability of SM. Among the different ML methods, neural networks were the most used for SM estimation. The purpose of this paper is to propose a Long Short-Term Memory (LSTM) based methodology to estimate the SM. The input data used in the LSTM model include the Soil Moisture Active and Passive mission (SMAP) Brightness Temperature (TB), the Moderate Resolution Imaging Spectroradiometer Vegetation Water Content (MODIS-VWC) and the soil temperature. The target SM data used to train the LSTM model is provided by the Real-time In situ Soil Monitoring for Agriculture (RISMA) network installed by Agriculture and Agri-Food Canada (AAFC). LSTM shows good ability to estimate the SM values with good accuracy.