干旱区生态环境知识资源中心(Arid): A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network

Arid

DOI	10.1016/j.geoderma.2021.115656
	A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network
	Wang, Nan; Peng, Jie; Xue, Jie; Zhang, Xianglin; Huang, Jingyi; Biswas, Asim; He, Yong; Shi, Zhou
通讯作者	Shi, Z
来源期刊	GEODERMA
ISSN	0016-7061
EISSN	1872-6259
出版年	2022
卷号	409
英文摘要	Soil salinization causes a deterioration in soil health and threatens crop growth. Rapid identification of salini-zation in farmlands is of great significance to improve soil functions and to maintain sustainable land man-agement. As salt moves in soil profiles during plowing and irrigation, the commonly used protocol for measuring and monitoring salt content in topsoil does not provide a thorough assessment. In order to quantify and comprehensively evaluate the salt content in deep soil, this study developed a novel framework for monitoring total salt content in the soil profile to a depth of 1 m by combining information from time-series satellite images and machine learning. The field experiments were conducted in Alar, Southern Xinjiang, with a total of 120 soil samples and 582 measurements of EM38-MK2 apparent electrical conductivity in 2019 and 2020 to quantify the vertical variation in the salt content. A total of 42 covariates derived from time-series Sentinel-2 images, including 20 salinity indices, 10 soil indices, and 12 vegetation indices were used for modeling salinity in the soil profile. From the total covariates, 22 were selected using the Random Forest. Soil salinity which was modeled using a Temporal Convolution Network in 2019 and 2020 and forecast for 2021. The model effectively revealed the spatial and temporal variability of the salt content in the soil profile with R-2 of 0.71 and 0.65 for 2019 and 2020, respectively. The proposed new framework provides an effective method to estimate the salt content in the soil profile for precision agriculture in arid and semi-arid regions.
英文关键词	Soil salinity Soil profile Random Forest Temporal Convolution Network Time-series images
类型	Article
语种	英语
开放获取类型	Green Published
收录类别	SCI-E
WOS记录号	WOS:000788135400002
WOS关键词	SALINITY ASSESSMENT ; ORGANIC-CARBON ; WET SEASONS ; VEGETATION ; INDEX ; OPTIMIZATION ; AGRICULTURE ; PARAMETERS ; REGRESSION ; XINJIANG
WOS类目	Soil Science
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392908
推荐引用方式 GB/T 7714	Wang, Nan,Peng, Jie,Xue, Jie,et al. A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network[J],2022,409.
APA	Wang, Nan.,Peng, Jie.,Xue, Jie.,Zhang, Xianglin.,Huang, Jingyi.,...&Shi, Zhou.(2022).A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network.GEODERMA,409.
MLA	Wang, Nan,et al."A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network".GEODERMA 409(2022).