干旱区生态环境知识资源中心(Arid): A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information

Arid

DOI	10.1016/j.scitotenv.2023.166112
	A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information
	Wang, Jiawen; Feng, Chunhui; Hu, Bifeng; Chen, Songchao; Hong, Yongsheng; Arrouays, Dominique; Peng, Jie; Shi, Zhou
通讯作者	Peng, J
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2023
卷号	903
英文摘要	Remote sensing is an important tool for monitoring soil information. However, accurate spatial modeling of soil organic matter (SOM) in areas with high vegetation coverage, typically represented by agroecosystems, remains a challenge for field-scale estimation using remote sensing. To date, studies have focused on using single-period or multi-temporal vegetation information to characterize SOM. Thus, the relationship between SOM content and time-series vegetation biomass has not yet been fully explored. In addition, most studies have ignored the effects of critical soil properties and human activities (e.g., soil salinization, soil particle size fractions, history of land use changes) on SOM. By integrating information on vegetation, soil, and human activities, we propose a novel framework for assessing SOM in cotton fields of artificial oases in northwest China, where returned straw is one of the primary sources of SOM coming from vegetation. We developed an Annual Maximum Biomass Accumulation Index (AMBAI) using time-series Landsat images from 1990 to 2019. Subsequently, we quantified the information of the planting years (PY) of cropland using spectral index threshold and incorporated proximal sensing data (soil hyperspectral and apparent conductivity data) and soil particle size fractions to establish a predictive model of SOM using partial least squares regression (PLSR), random forest (RF), and convolutional neural network (CNN). The results revealed that AMBAI had the highest correlation coefficient (r) with SOM (0.76, P < 0.01). AMBAI, soil hyperspectral data, and PY were the most relevant predictors for estimating SOM. The CNN model integrating vegetation, soil, and human activity information performed best, with coefficient of determination (R-2), relative analysis error (RPD), and root mean square error (RMSE) of 0.83, 2.38 and 1.38 g kg(- 1), respectively. This study confirmed that AMBAI and PY had great potential for characterizing SOM in arid and semi-arid regions, providing a reference for other relevant studies.
英文关键词	Arid and semi-arid region Cropland Convolutional neural networks Annual maximum biomass accumulation index Planting years
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001066071300001
WOS关键词	DIFFUSE-REFLECTANCE SPECTROSCOPY ; CARBON STOCKS ; AGRICULTURAL SOILS ; SPECTRAL LIBRARY ; PLAIN ; VARIABILITY ; SATURATION ; REGRESSION ; NORTHEAST ; SOILGRIDS
WOS类目	Environmental Sciences
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/2XILL650/398554
推荐引用方式 GB/T 7714	Wang, Jiawen,Feng, Chunhui,Hu, Bifeng,et al. A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information[J],2023,903.
APA	Wang, Jiawen.,Feng, Chunhui.,Hu, Bifeng.,Chen, Songchao.,Hong, Yongsheng.,...&Shi, Zhou.(2023).A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information.SCIENCE OF THE TOTAL ENVIRONMENT,903.
MLA	Wang, Jiawen,et al."A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information".SCIENCE OF THE TOTAL ENVIRONMENT 903(2023).