SMETool: A web-based tool for soil moisture estimation based on Eo-Learn framework and Machine Learning methods. (November 2022)

• Record Type: Journal Article
• Authors: Jarray, Noureddine; Ben Abbes, Ali; Rhif, Manel; Dhaou, Hanen; Ouessar, Mohamed; Farah, Imed Riadh
• Is Part Of: Environmental modelling & software. Volume 157(2022)
• Publication Date: 2022-11
• Subjects: Soil moisture estimation -- Open source data -- Web-based tool -- Eo-learn -- Machine learning -- Sentinel-1A and 2A; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105505
• Languages: English
• ISSNs: 1364-8152

Abstract

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 ( r RF = 0 . 86, r ANN = 0 . 75, r XGBoost = 0 . 79, r CNN = 0 . 87 ) and low Root Mean Square Error (RMSE) ( RMSE RF = 1.09%, RMSE ANN = 1.49%, RMSE XGBoost = 1.39%, RMSE CNN = 1.12%), respectively. Highlights: A new web-based tool for soil moisture estimation at the scale of arid regions. Simple implementation using Eo-learn framework and machine learning methods. A case study on arid region in southern Tunisia using Sentinel 1-A and Sentinel 2-A products.