A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information

Jiawen Wang; Chunhui Feng; Bifeng Hu; Songchao Chen; Yongsheng Hong; Dominique Arrouays; Jie Peng; Zhou Shi

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

Sci Total Environ. 2023 Dec 10:903:166112. doi: 10.1016/j.scitotenv.2023.166112. Epub 2023 Aug 9.

Authors

Jiawen Wang¹, Chunhui Feng², Bifeng Hu³, Songchao Chen⁴, Yongsheng Hong⁵, Dominique Arrouays⁶, Jie Peng⁷, Zhou Shi⁸

Affiliations

¹ College of Agriculture, Tarim University, Alar 843300, China; College of Life Sciences and Technology, Tarim University, Alar 843300, China.
² College of Horticulture and Forestry, Tarim University, Alar 843300, China. Electronic address: chunhui.f@taru.edu.cn.
³ Department of Land Resource Management, School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China. Electronic address: hubifeng@zju.edu.cn.
⁴ ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China. Electronic address: chensongchao@zju.edu.cn.
⁵ Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China. Electronic address: hys@whu.edu.cn.
⁶ INRAE, Info&Sols, 45075 Orléans, France. Electronic address: Dominique.arrouays@inrae.fr.
⁷ College of Agriculture, Tarim University, Alar 843300, China; Key Laboratory of Genetic Improvement and Efficient Production for Specialty Crops in Arid Southern Xinjiang of Xinjiang Corps, Alar 843300, China; Research Center of Oasis Agricultural Resources and Environment in Southern Xinjiang, Tarim University, Alar 843300, China. Electronic address: pjzky@163.com.
⁸ Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China. Electronic address: shizhou@zju.edu.cn.

PMID: 37567300
DOI: 10.1016/j.scitotenv.2023.166112

Abstract

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 (R²), 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.

Keywords: Annual maximum biomass accumulation index; Arid and semi-arid region; Convolutional neural networks; Cropland; Planting years.