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DOI	10.1016/j.asr.2021.10.024
	Assessing the performance of machine learning algorithms for soil salinity mapping in Google Earth Engine platform using Sentinel-2A and Landsat-8 OLI data
	Aksoy, Samet; Yildirim, Aylin; Gorji, Taha; Hamzehpour, Nikou; Tanik, Aysegul; Sertel, Elif
通讯作者	Sertel, E (corresponding author)，Istanbul Tech Univ ITU, Fac Civil Engn, Dept Geomat Engn, TR-34469 Istanbul, Turkey.
来源期刊	ADVANCES IN SPACE RESEARCH
ISSN	0273-1177
EISSN	1879-1948
出版年	2022
卷号	69 期号:2
英文摘要	Soil salinization caused by natural and anthropogenic factors is an important environmental hazard especially in arid and semi-arid regions of the world. Accumulation of salts in the soil is a major threat to crop production and global agriculture; therefore, rapid and precise detection of salt-affected lands is highly critical for preserving soil sustainability and supporting food production. Advancement in remote sensing techniques and machine-learning algorithms has started to contribute to fast and large-scale monitoring and mapping of soil salinization throughout the world. This paper aims to analyze the performance of three different machine-learning algorithms to map soil salinity using Landsat-8 OLI, Sentinel-2A satellite images, and ground-based electrical conductivity (EC) measurements with the aid of Google Earth Engine (GEE) platform. Classification and regression trees (CART), random forest (RF), and support vector regression (SVR) methods are implemented to create a correlation between ground measurements and satellite-derived environmental variables or spectral indices. After selecting the optimum five variables including wetness band, three soil salinity indices, and one vegetation index, soil salinity maps are generated in three machine-learning algorithms. The output soil salinity map in RF algorithm demonstrated the most reliable spatial distribution of various soil salinity classes in the selected study area. Despite CART provided slightly better prediction of soil salinity with R-squared (R-2) of 0.98 for Sentinel-2A data, and 0.96 for Landsat-8 OLI data in comparison with accuracy results of RF technique with R-2 of 0.96 for Sentinel-2A data and 0.94 for Landsat-8 OLI data, the output map of RF model estimated more reliable salinity levels in salt crusts, agricultural lands, drainage areas, and swamps. The corresponding result highly matched with visual interpretation. Soil salinity maps derived from SVR algorithms by using various combinations of input variables displayed relatively poor estimation of soil EC values compared to the other two methods. (C) 2021 COSPAR. Published by Elsevier B.V. All rights reserved.
英文关键词	Soil salinity Electrical conductivity Machine learning Cross-validation Google Earth Engine
类型	Article
语种	英语
开放获取类型	Bronze
收录类别	SCI-E
WOS记录号	WOS:000736949700009
WOS关键词	SPATIAL PREDICTION ; MEASUREMENT ERRORS ; LAND DEGRADATION ; DERIVATION ; REGION ; INDEX ; MSI ; XINJIANG ; DELTA ; LAKE
WOS类目	Engineering, Aerospace ; Astronomy & Astrophysics ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS研究方向	Engineering ; Astronomy & Astrophysics ; Geology ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376879
作者单位	[Aksoy, Samet; Sertel, Elif] Istanbul Tech Univ ITU, Fac Civil Engn, Dept Geomat Engn, TR-34469 Istanbul, Turkey; [Yildirim, Aylin; Gorji, Taha] Istanbul Tech Univ ITU, Informat Inst, Geog Informat Technol Program, TR-34469 Istanbul, Turkey; [Hamzehpour, Nikou] Univ Maragheh, Fac Agr, Dept Soil Sci & Engn, Maragheh, Iran; [Hamzehpour, Nikou] Swiss Fed Inst Technol, Inst Atmospher & Climate Sci, CH-8092 Zurich, Switzerland; [Tanik, Aysegul] Istanbul Tech Univ ITU, Fac Civil Engn, Dept Environm Engn, TR-34469 Istanbul, Turkey
推荐引用方式 GB/T 7714	Aksoy, Samet,Yildirim, Aylin,Gorji, Taha,et al. Assessing the performance of machine learning algorithms for soil salinity mapping in Google Earth Engine platform using Sentinel-2A and Landsat-8 OLI data[J],2022,69(2).
APA	Aksoy, Samet,Yildirim, Aylin,Gorji, Taha,Hamzehpour, Nikou,Tanik, Aysegul,&Sertel, Elif.(2022).Assessing the performance of machine learning algorithms for soil salinity mapping in Google Earth Engine platform using Sentinel-2A and Landsat-8 OLI data.ADVANCES IN SPACE RESEARCH,69(2).
MLA	Aksoy, Samet,et al."Assessing the performance of machine learning algorithms for soil salinity mapping in Google Earth Engine platform using Sentinel-2A and Landsat-8 OLI data".ADVANCES IN SPACE RESEARCH 69.2(2022).