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DOI	10.1016/j.envsoft.2022.105505
	SMETool: A web-based tool for soil moisture estimation based on Eo-Learn framework and Machine Learning methods
	Jarray, Noureddine; Ben Abbes, Ali; Rhif, Manel; Dhaou, Hanen; Ouessar, Mohamed; Farah, Imed Riadh
通讯作者	Jarray, N
来源期刊	ENVIRONMENTAL MODELLING & SOFTWARE
ISSN	1364-8152
EISSN	1873-6726
出版年	2022
卷号	157
英文摘要	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.
英文关键词	Soil moisture estimation Open source data Web-based tool Eo-learn Machine learning Sentinel-1A and 2A
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000862375900002
WOS关键词	SENTINEL-1
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Environmental ; Environmental Sciences ; Water Resources
WOS研究方向	Computer Science ; Engineering ; Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392468
推荐引用方式 GB/T 7714	Jarray, Noureddine,Ben Abbes, Ali,Rhif, Manel,et al. SMETool: A web-based tool for soil moisture estimation based on Eo-Learn framework and Machine Learning methods[J],2022,157.
APA	Jarray, Noureddine,Ben Abbes, Ali,Rhif, Manel,Dhaou, Hanen,Ouessar, Mohamed,&Farah, Imed Riadh.(2022).SMETool: A web-based tool for soil moisture estimation based on Eo-Learn framework and Machine Learning methods.ENVIRONMENTAL MODELLING & SOFTWARE,157.
MLA	Jarray, Noureddine,et al."SMETool: A web-based tool for soil moisture estimation based on Eo-Learn framework and Machine Learning methods".ENVIRONMENTAL MODELLING & SOFTWARE 157(2022).