干旱区生态环境知识资源中心(Arid): A Novel Teacher-Student Framework for Soil Moisture Retrieval by Combining Sentinel-1 and Sentinel-2: Application in Arid Regions

Arid

DOI	10.1109/LGRS.2022.3168982
	A Novel Teacher-Student Framework for Soil Moisture Retrieval by Combining Sentinel-1 and Sentinel-2: Application in Arid Regions
	Jarray, Noureddine; Ben Abbes, Ali; Farah, Imed Riadh
通讯作者	Jarray, N
来源期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
EISSN	1558-0571
出版年	2022
卷号	19
英文摘要	Soil moisture (SM) is an important parameter used to control a broad range of environmental applications. An increasing attention has been recently given to machine learning (ML) methods for SM retrieval that provide promising performance. Nevertheless, most of them are based on a supervised learning strategies that depend on the used labeled training samples. In fact, they are unaffordable or costly. In this letter, new teacher-student for SM estimation, called (TS-SME), relying on teacher-student (TS) framework using synthetic aperture radar (SAR) and optical data, was proposed to estimate SM. The main advantage of this framework is to enroll a large amount of unlabeled data together with a small amount of labeled data. Experiments were carried out on two arid areas in southern Tunisia. The input data include the backscatter coefficient in two-mode polarization (sigma(VV)degrees and sigma(VH)degrees) for Sentinel-1A, normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) for Sentinel-2A and in situ measurements. Extensive experimental results demonstrated that TS-SME framework is capable of generating a well-performed student model, with the estimation accuracy is superior to all teacher models [artificial neural network (ANN), eXtreme gradient boosting (XGBoost), random forest regressor (RFR), and water cloud model (WCM)]. It was highly correlated with the in situ measurements with high Pearson's correlation coefficient R (R-RF = 0.86, R-ANN = 0.75, R-XGBoost = 0.77, R-WCM = 0.77, RTS-SME = 0.96) and low root mean square error (RMSE) (RMSERF =1.09%, RMSEANN = 1.49%, R-XGBoost = 1.39%, RMSEWCM = 1.12%, RMSETS-SME = 0.8%), respectively.
英文关键词	Arid regions machine learning (ML) Sentinel 1 and 2 A soil moisture (SM) teacher-student (TS) framework Tunisia
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000790813100006
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393068
推荐引用方式 GB/T 7714	Jarray, Noureddine,Ben Abbes, Ali,Farah, Imed Riadh. A Novel Teacher-Student Framework for Soil Moisture Retrieval by Combining Sentinel-1 and Sentinel-2: Application in Arid Regions[J],2022,19.
APA	Jarray, Noureddine,Ben Abbes, Ali,&Farah, Imed Riadh.(2022).A Novel Teacher-Student Framework for Soil Moisture Retrieval by Combining Sentinel-1 and Sentinel-2: Application in Arid Regions.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19.
MLA	Jarray, Noureddine,et al."A Novel Teacher-Student Framework for Soil Moisture Retrieval by Combining Sentinel-1 and Sentinel-2: Application in Arid Regions".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022).