干旱区生态环境知识资源中心(Arid): Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China

Arid

DOI	10.3390/rs13020305
	Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China
	Wang, Jiaqiang; Peng, Jie; Li, Hongyi; Yin, Caiyun; Liu, Weiyang; Wang, Tianwei; Zhang, Huaping
通讯作者	Peng, J (corresponding author), Jiangxi Univ Finance & Econ, Sch Tourism & Urban Management, Nanchang 330032, Jiangxi, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:2
英文摘要	Accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of arid regions. As a branch of artificial intelligence, machine learning acquires new knowledge through self-learning and continuously improves its own performance. The purpose of this study is to combine Sentinel-2 Multispectral Imager (MSI) data and MSI-derived covariates with measured soil salinity data and to apply three machine learning algorithms in modeling to estimate and map the soil salinity in the study sample area. According to the convenient transportation conditions, the study area and sampling quadrat were set up, and the 5-point method was used to collect the soil mixed samples, and 160 soil mixed samples were collected. Kennard-Stone (K-S) algorithm was used for sample classification, 70% for modeling and 30% for verification. The machine learning algorithm uses Support Vector Machines (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The results showed that (1) the average reflectance of each band of the MSI data ranged from 0.21-0.28. According to the spectral characteristics corresponding to different soil electrical conductivity (EC) levels (1.07-79.6 dS m(-1)), the spectral reflectance of salinized soil in the MSI data ranged from 0.09-0.35. (2) The correlation coefficient between the MSI data and MSI-derived covariates and soil EC was moderate, and the correlation between certain MSI data sets and soil EC was not significant. (3) The SVM soil EC estimation model established with the MSI data set attained a higher performance and accuracy (R-2 = 0.88, root mean square error (RMSE) = 4.89 dS m(-1), and ratio of the performance to the interquartile range (RPIQ) = 1.96, standard error of the laboratory measurements to the standard error of the predictions (SEL/SEP) = 1.11) than those attained with the soil EC estimation models established with the RF and ANN models. (4) We applied the SVM soil EC estimation model to map the soil salinity in the study area, which showed that the farmland with higher altitudes discharged a large amount of salt to the surroundings due to long-term irrigation, and the secondary salinization of the farmland also caused a large amount of salt accumulation. This research provides a scientific basis for the simulation of soil salinization scenarios in arid areas in the future.
英文关键词	soil salinization Sentinel-2 MSI remote sensing machine learning arid area
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:000611547700001
WOS关键词	LANDSAT 8 OLI ; SEMIARID REGION ; WET SEASONS ; SALINIZATION ; LAKE ; OPTIMIZATION ; CAPABILITY ; INDEXES ; OASIS ; MODEL
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/2XILL650/348141
作者单位	[Wang, Jiaqiang; Wang, Tianwei] Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China; [Wang, Jiaqiang; Liu, Weiyang] Tarim Univ, Coll Plant Sci, Alar 843300, Peoples R China; [Peng, Jie; Li, Hongyi] Jiangxi Univ Finance & Econ, Sch Tourism & Urban Management, Nanchang 330032, Jiangxi, Peoples R China; [Yin, Caiyun; Zhang, Huaping] First Div Xinjiang Prod & Construct Corps, Agr Technol Extens Stn, Alar 843300, Peoples R China
推荐引用方式 GB/T 7714	Wang, Jiaqiang,Peng, Jie,Li, Hongyi,et al. Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China[J],2021,13(2).
APA	Wang, Jiaqiang.,Peng, Jie.,Li, Hongyi.,Yin, Caiyun.,Liu, Weiyang.,...&Zhang, Huaping.(2021).Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China.REMOTE SENSING,13(2).
MLA	Wang, Jiaqiang,et al."Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China".REMOTE SENSING 13.2(2021).