干旱区生态环境知识资源中心(Arid): Inversion of Different Cultivated Soil Types' Salinity Using Hyperspectral Data and Machine Learning

Arid

DOI	10.3390/rs14225639
	Inversion of Different Cultivated Soil Types' Salinity Using Hyperspectral Data and Machine Learning
	Jia, Pingping; Zhang, Junhua; He, Wei; Yuan, Ding; Hu, Yi; Zamanian, Kazem; Jia, Keli; Zhao, Xiaoning
通讯作者	Zhao, XN
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:22
英文摘要	Soil salinization is one of the main causes of global desertification and soil degradation. Although previous studies have investigated the hyperspectral inversion of soil salinity using machine learning, only a few have been based on soil types. Moreover, agricultural fields can be improved based on the accurate estimation of the soil salinity, according to the soil type. We collected field data relating to six salinized soils, Haplic Solonchaks (HSK), Stagnic Solonchaks (SSK), Calcic Sonlonchaks (CSK), Fluvic Solonchaks (FSK), Haplic Sonlontzs (HSN), and Takyr Solonetzs (TSN), in the Hetao Plain of the upper reaches of the Yellow River, and measured the in situ hyperspectral, pH, and electrical conductivity (EC) values of a total of 231 soil samples. The two-dimensional spectral index, topographic factors, climate factors, and soil texture were considered. Several models were used for the inversion of the saline soil types: partial least squares regression (PLSR), random forest (RF), extremely randomized trees (ERT), and ridge regression (RR). The spectral curves of the six salinized soil types were similar, but their reflectance sizes were different. The degree of salinization did not change according to the spectral reflectance of the soil types, and the related properties were inconsistent. The Pearson's correlation coefficient (PCC) between the two-dimensional spectral index and the EC was much greater than that between the reflectance and EC in the original band. In the two-dimensional index, the PCC of the HSK-NDI was the largest (0.97), whereas in the original band, the PCC of the SSK400 nm was the largest (0.70). The two-dimensional spectral index (NDI, RI, and DI) and the characteristic bands were the most selected variables in the six salinized soil types, based on the variable projection importance analysis (VIP). The best inversion model for the HSK and FSK was the RF, whereas the best inversion model for the CSK, SSK, HSN, and TSN was the ERT, and the CSK-ERT had the best performance (R-2 = 0.99, RMSE = 0.18, and RPIQ = 6.38). This study provides a reference for distinguishing various salinization types using hyperspectral reflectance and provides a foundation for the accurate monitoring of salinized soil via multispectral remote sensing.
英文关键词	soil electrical conductivity variable projection importance Hetao Plain salinization soil degradation soil quality
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000887799400001
WOS关键词	INFRARED SPECTROSCOPY ; SOUTHERN XINJIANG ; REGRESSION ; PLS
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/2XILL650/394238
推荐引用方式 GB/T 7714	Jia, Pingping,Zhang, Junhua,He, Wei,et al. Inversion of Different Cultivated Soil Types' Salinity Using Hyperspectral Data and Machine Learning[J],2022,14(22).
APA	Jia, Pingping.,Zhang, Junhua.,He, Wei.,Yuan, Ding.,Hu, Yi.,...&Zhao, Xiaoning.(2022).Inversion of Different Cultivated Soil Types' Salinity Using Hyperspectral Data and Machine Learning.REMOTE SENSING,14(22).
MLA	Jia, Pingping,et al."Inversion of Different Cultivated Soil Types' Salinity Using Hyperspectral Data and Machine Learning".REMOTE SENSING 14.22(2022).