干旱区生态环境知识资源中心(Arid): Soil Quality Evaluation for Cotton Fields in Arid Region Based on Graph Convolution Network

Arid

DOI	10.3390/land12101897
	Soil Quality Evaluation for Cotton Fields in Arid Region Based on Graph Convolution Network
	Fan, Xianglong; Gao, Pan; Zuo, Li; Duan, Long; Cang, Hao; Zhang, Mengli; Zhang, Qiang; Zhang, Ze; Lv, Xin; Zhang, Lifu
通讯作者	Lv, X ; Zhang, LF
来源期刊	LAND
EISSN	2073-445X
出版年	2023
卷号	12 期号:10
英文摘要	Accurate soil quality evaluation is an important prerequisite for improving soil management systems and remediating soil pollution. However, traditional soil quality evaluation methods are cumbersome to calculate, and suffer from low efficiency and low accuracy, which often lead to large deviations in the evaluation results. This study aims to provide a new and accurate soil quality evaluation method based on graph convolution network (GCN). In this study, soil organic matter (SOM), alkaline hydrolysable nitrogen (AN), available potassium (AK), salinity, and heavy metals (iron (Fe), copper (Cu), manganese (Mn), and zinc (Zn)) were determined and evaluated using the soil quality index (SQI). Then, the graph convolution network (GCN) was first introduced in the soil quality evaluation to construct an evaluation model, and its evaluation results were compared with those of the SQI. Finally, the spatial distribution of the evaluation results of the GCN model was displayed. The results showed that soil salinity had the largest coefficient of variation (86%), followed by soil heavy metals (67%) and nutrients (30.3%). The soil salinization and heavy metal pollution were at a low level in this area, and the soil nutrients and soil quality were at a high level. The evaluation accuracy of the GCN model for soil salinity/heavy metals, soil nutrients, and soil quality were 0.91, 0.84, and 0.90, respectively. Therefore, the GCN model has a high accuracy and is feasible to be applied in the soil quality evaluation. This study provides a new, simple, and highly accurate method for soil quality evaluation.
英文关键词	soil quality assessment machine learning soil nutrients heavy metals soil salinity
类型	Article
语种	英语
开放获取类型	gold
收录类别	SSCI
WOS记录号	WOS:001095524100001
WOS关键词	ECOLOGICAL RISK-ASSESSMENT ; SURFACE SEDIMENTS ; HEAVY-METALS ; AREA ; CONTAMINATION ; PREDICTION ; FRAMEWORK ; RIVER
WOS类目	Environmental Studies
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397699
推荐引用方式 GB/T 7714	Fan, Xianglong,Gao, Pan,Zuo, Li,et al. Soil Quality Evaluation for Cotton Fields in Arid Region Based on Graph Convolution Network[J],2023,12(10).
APA	Fan, Xianglong.,Gao, Pan.,Zuo, Li.,Duan, Long.,Cang, Hao.,...&Zhang, Lifu.(2023).Soil Quality Evaluation for Cotton Fields in Arid Region Based on Graph Convolution Network.LAND,12(10).
MLA	Fan, Xianglong,et al."Soil Quality Evaluation for Cotton Fields in Arid Region Based on Graph Convolution Network".LAND 12.10(2023).