Spatial-Temporal Changes in Soil Organic Carbon and pH in the Liaoning Province of China: A Modeling Analysis Based on Observational Data

Li Qi, Shuai Wang, Qianlai Zhuang, Zijiao Yang, Shubin Bai, Xinxin Jin and Guangyu Lei
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Li Qi: College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China
Shuai Wang: College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China
Qianlai Zhuang: Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN 47907, USA
Zijiao Yang: College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China
Shubin Bai: College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China
Xinxin Jin: College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China
Guangyu Lei: Shaanxi Provincial Land Engineering Construction Group Co., Ltd., Xi’an 710075, China

Sustainability, 2019, vol. 11, issue 13, 1-17

Abstract: Quantification of soil organic carbon (SOC) and pH, and their spatial variations at regional scales, is a foundation to adequately assess agriculture, pollution control, or environmental health and ecosystem functioning, so as to establish better practices for land use and land management. In this study, we used the random forest (RF) model to map the distribution of SOC and pH in the topsoil (0–20 cm) and estimate SOC and pH changes from 1982 to 2012 in Liaoning Province, Northeast China. A total of 10 covariates (elevation, slope gradient, topographic wetness index (TWI), mean annual temperature (MAT), mean annual precipitation (MAP), visible-red band 3 (B3), near-infrared band 4 (B4), short-wave infrared band 5 (B5), normalized difference vegetation index (NDVI), and land-use data) and a set of 806 (in 1982) and 973 (in 2012) soil samples were selected. Cross-validation technology was used to test the performance and uncertainty of the RF model. We found that the prediction R 2 of SOC and pH was 0.69 and 0.54 for 1982, and 0.63 and 0.48 for 2012, respectively. Elevation, NDVI, and land use are the main environmental variables affecting the spatial variability of SOC in both periods. Correspondingly, the topographic wetness index and mean annual precipitation were the two most critical environmental variables affecting the spatial variation of pH. The mean SOC and pH decreased from 18.6 to 16.9 kg −1 and 6.9 to 6.6, respectively, over a 30-year period. SOC distribution generated using the RF model showed a decreasing SOC trend from east to west across the city in the two periods. In contrast, the spatial distribution of pH showed an opposite trend in both periods. This study provided important information of spatial variations in SOC and pH to agencies and communities in this region, to evaluate soil quality and make decisions on remediation and prevention of soil acidification and salinization.

Keywords: spatial variability; environmental variables; digital soil mapping; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
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