Predicting spatial distribution of soil organic carbon and total nitrogen in a typical human impacted area. Issue 15 (22nd August 2022)

• Record Type: Journal Article
• Authors: Ye, Yingcong; Jiang, Yefeng; Kuang, Lihua; Han, Yi; Xu, Zhe; Guo, Xi
• Is Part Of: Geocarto international. Volume 37:Issue 15(2022)
• Page Start: 4465
• Page End: 4482
• Publication Date: 2022-08-22
• Subjects: Soil organic carbon -- soil total nitrogen -- agricultural land use -- radial basis function neural network; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2021.1886344
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: The relationship between soil properties and environmental covariates is mostly nonlinear for human impacted areas. Herein, we proposed a nonlinear model for mapping soil organic carbon (SOC) and total nitrogen (TN) in a typical human impacted area (a small watershed of Poyang Lake, China), namely radial basis function neural network combined with agricultural land use (RBFNN_ALU). The results showed that the RBFNN_ALU performs better than the ordinary kriging (OK), the OK combined with agricultural land use (OK_ALU), the geographically weighted regression (GWR), the multiple linear regression (MLR), the MLR combined with agricultural land use (MLR_ALU) and the RBFNN. In addition, RBFNN_ALU provided a more detailed and accurate description of the spatial SOC and TN patterns. The results indicate that when predicting spatial distribution of SOC and TN for human impacted areas, non-linear models are critical for predicting the spatial distribution of soil properties.