SMETool: A web-based tool for soil moisture estimation based on Eo-Learn framework and Machine Learning methods

Abstract

Earth Observation (EO) technologies have played an increasingly important role in monitoring the Sustainable Development Goals (SDG). These technologies often combined with Machine Learning (ML) models provide efficient means for achieving the SDGs. The great progress of this combination is also demonstrated by the large number of software, web tools and packages that have been made available for free use. In this paper, we introduce a software architecture to facilitate the generation of EO information targeted towards soil moisture that derive several challenges regarding the facilitation of satellite data processing. Thus, this paper presents a web-based tool for Soil Moisture Estimation (SMETool), designed for the soil moisture estimation using Sentinel-1A and Sentinel-2A data based on Eo-learn library. SMETool implements several ML techniques such as (Artificial Neural Network (ANN), Random Forest (RF), Convolutional Neural Network (CNN), etc.). The SMETool could be very useful for decision makers in the region in assessing the effects of drought and desertification events. Experiments were carried out on two sites in Tunisia during the period from 2016 to 2017. Although the performance of the used models is very close, it is clear that CNN and RF outperformed other ML models. The achieved results reveal that the soil moisture, was highly correlated to the in-situ measurements with high Pearson’s correlation coefficient r (rRF=0.86, rANN=0.75, rXGBoost=0.79, rCNN=0.87) and low Root Mean Square Error (RMSE) (RMSERF= 1.09%, RMSEANN= 1.49%, RMSEXGBoost= 1.39%, RMSECNN= 1.12%), respectively.