干旱区生态环境知识资源中心(Arid): Estimation of Soil Salinization by Machine Learning Algorithms in Different Arid Regions of Northwest China

Arid

DOI	10.3390/rs14020347
	Estimation of Soil Salinization by Machine Learning Algorithms in Different Arid Regions of Northwest China
	Jiang, Xiaofang; Duan, Hanchen; Liao, Jie; Guo, Pinglin; Huang, Cuihua; Xue, Xian
通讯作者	Xue, X (corresponding author)，Chinese Acad Sci, Key Lab Desert & Desertificat, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China. ; Xue, X (corresponding author)，Chinese Acad Sci, Drylands Salinizat Res Stn, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:2
英文摘要	Hyperspectral data has attracted considerable attention in recent years due to its high accuracy in monitoring soil salinization. At present, most existing research focuses on the saline soil in a single area without comparative analysis between regions. The regional differences in the hyperspectral characteristics of saline soil are still unclear. Thus, we chose Golmud in the cold-dry Qaidam Basin (QB-G) and Gaotai-Minghua in the relatively warm-dry Hexi Corridor (HC-GM) as the study areas, and used the deep extreme learning machine (DELM) and sine cosine algorithm-Elman (SCA-Elman) to predict soil salinity, and then selected the most suitable algorithm in these two regions. A total of 79 (QB-G) and 86 (HC-GM) soil samples were collected and tested to obtain their electrical conductivity (EC) and corresponding hyperspectral reflectance (R). We utilized the land surface parameters that affect the soil based on Landsat 8 and digital elevation model (DEM) data, selected the variables using the light gradient boosting machine (LightGBM), and built SCA-Elman and DELM from the hyperspectral reflectance data combined with land surface parameters. The results revealed the following: (1) The soil hyperspectral reflectance in QB-G was higher than that in HC-GM. The soils of QB-G are mainly the chloride type and those of HC-GM mainly belong to the sulfate type, having lower reflectance. (2) The accuracies of some of the SCA-Elman and DELM models in QB-G (the highest MAEv, RMSEv, and R-v(2) were 0.09, 0.12 and 0.75, respectively) were higher than those in HC-GM (the highest MAEv, RMSEv, and R-v(2) were 0.10, 0.14 and 0.73, respectively), which has flatter terrain and less obvious surface changes. The surface parameters in QB-G had higher correlation coefficients with EC due to the regular altitude change and cold-dry climate. (3) Most of the SCA-Elman results (the mean R-v(2) in HC-GM and QB-G were 0.62 and 0.60, respectively) in all areas performed better than the DELM results (the mean R-v(2) in HC-GM and QB-G were 0.51 and 0.49, respectively). Therefore, SCA-Elman was more suitable for the soil salinity prediction in HC-GM and QB-G. This can provide a reference for soil salinization monitoring and model selection in the future.
英文关键词	hyperspectral data fractional differential transformation sine cosine algorithm-Elman deep extreme learning machine
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000747846900001
WOS关键词	NEURAL-NETWORK ; REFLECTANCE PROPERTIES ; SALINITY ; VEGETATION ; INDEX ; OPTIMIZATION ; MOISTURE ; SPECTRA ; IMAGES ; AREA
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; 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/2XILL650/376757
作者单位	[Jiang, Xiaofang; Duan, Hanchen; Liao, Jie; Guo, Pinglin; Huang, Cuihua; Xue, Xian] Chinese Acad Sci, Key Lab Desert & Desertificat, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China; [Jiang, Xiaofang; Guo, Pinglin] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Duan, Hanchen; Liao, Jie; Huang, Cuihua; Xue, Xian] Chinese Acad Sci, Drylands Salinizat Res Stn, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China
推荐引用方式 GB/T 7714	Jiang, Xiaofang,Duan, Hanchen,Liao, Jie,et al. Estimation of Soil Salinization by Machine Learning Algorithms in Different Arid Regions of Northwest China[J],2022,14(2).
APA	Jiang, Xiaofang,Duan, Hanchen,Liao, Jie,Guo, Pinglin,Huang, Cuihua,&Xue, Xian.(2022).Estimation of Soil Salinization by Machine Learning Algorithms in Different Arid Regions of Northwest China.REMOTE SENSING,14(2).
MLA	Jiang, Xiaofang,et al."Estimation of Soil Salinization by Machine Learning Algorithms in Different Arid Regions of Northwest China".REMOTE SENSING 14.2(2022).