干旱区生态环境知识资源中心(Arid): Radar remote sensing-based inversion model of soil salt content at different depths under vegetation

Arid

DOI	10.7717/peerj.13306
	Radar remote sensing-based inversion model of soil salt content at different depths under vegetation
	Chen, Yinwen; Du, Yuyan; Yin, Haoyuan; Wang, Huiyun; Chen, Haiying; Li, Xianwen; Zhang, Zhitao; Chen, Junying
通讯作者	Du, YY
来源期刊	PEERJ
ISSN	2167-8359
出版年	2022
卷号	10
英文摘要	Excessive soil salt content (SSC) seriously affects the crop growth and economic benefits in the agricultural production area. Prior research mainly focused on estimating the salinity in the top bare soil rather than in deep soil that is vital to crop growth. For this end, an experiment was carried out in the Hetao Irrigation District, Inner Mongolia, China. In the experiment, the SSC at different depths under vegetation was measured, and the Sentinel-1 radar images were obtained synchronously. The radar backscattering coefficients (VV and VH) were combined to construct multiple indices, whose sensitivity was then analyzed using the best subset selection (BSS). Meanwhile, four most commonly used algorithms, partial least squares regression (PLSR), quantile regression (QR), support vector machine (SVM), and extreme learning machine (ELM), were utilized to construct estimation models of salinity at the depths of 0???10, 10???20, 0???20, 20???40, 0???40, 40???60 and 0???60 cm before and after BSS, respectively. The results showed: (a) radar remote sensing can be used to estimate the salinity in the root zone of vegetation (0-30 cm); (b) after BSS, the correlation coefficients and estimation accuracy of the four monitoring models were all improved significantly; (c) the estimation accuracy of the four regression models was: SVM > QR > ELM > PLSR; and (d) among the seven sampling depths, 10???20 cm was the optimal inversion depth for all the four models, followed by 20???40 and 0???40 cm. Among the four models, SVM was higher in accuracy than the other three at 10???20 cm (R2P = 0.67, RMSEP = 0.12%). These findings can provide valuable guidance for soil salinity monitoring and agricultural production in the arid or semi-arid areas under vegetation.
英文关键词	vegetation coverage Soil salt content Radar remote sensing Soil at different depths Best subset selection Support vector machine
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000800121400005
WOS关键词	REFLECTANCE SPECTROSCOPY ; MOISTURE ESTIMATION ; METHODS PLSR ; SALINITY ; SALINIZATION ; CAPABILITY ; XINJIANG ; SPECTRA ; SEASONS ; FUSION
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393917
推荐引用方式 GB/T 7714	Chen, Yinwen,Du, Yuyan,Yin, Haoyuan,et al. Radar remote sensing-based inversion model of soil salt content at different depths under vegetation[J],2022,10.
APA	Chen, Yinwen.,Du, Yuyan.,Yin, Haoyuan.,Wang, Huiyun.,Chen, Haiying.,...&Chen, Junying.(2022).Radar remote sensing-based inversion model of soil salt content at different depths under vegetation.PEERJ,10.
MLA	Chen, Yinwen,et al."Radar remote sensing-based inversion model of soil salt content at different depths under vegetation".PEERJ 10(2022).