Comparison of machine learning for predicting and mapping soil organic carbon in cultivated land in a subtropical complex geomorphic region

REN Biwu; CHEN Hanyue; ZHANG Liming; NIE Xiangqin; XING Shihe; FAN Xieyu

doi:10.13930/j.cnki.cjea.200939

May 2021

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Article Navigation > Chinese Journal of Eco-Agriculture > 2021 > 29(6): 1042-1050 > DOI: 10.13930/j.cnki.cjea.200939

REN Biwu, CHEN Hanyue, ZHANG Liming, NIE Xiangqin, XING Shihe, FAN Xieyu. Comparison of machine learning for predicting and mapping soil organic carbon in cultivated land in a subtropical complex geomorphic region[J]. Chinese Journal of Eco-Agriculture, 2021, 29(6): 1042-1050. DOI: 10.13930/j.cnki.cjea.200939

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Comparison of machine learning for predicting and mapping soil organic carbon in cultivated land in a subtropical complex geomorphic region

1.
College of Resources and Environment, Fujian Agriculture and Forestry University/Key Laboratory of Soil Ecosystem Health and Regulation of Fujian Province, Fuzhou 350002, China
2.
School of Public Management, Fujian Agriculture and Forestry University, Fuzhou 350002, China

Funds:

the National Natural Science Foundation of China 41971050

the Science and Technology Planning Project of Fujian Province 2017N5006

the Agricultural Technology Extension Project of the Ministry of Agriculture and Rural Affairs of the People's Republic of China KLD19H01A

the Central Committee Guides Local Science and Technology Development Projects of China 2018L3013

the Special Fund for Science and Technology Innovation of Fujian Agriculture and Forestry University KFA18106A

More Information

Corresponding author:
CHEN Hanyue, E-mail: Chenhanyue.420@163.com
Received Date: 2020-11-21
Accepted Date: 2021-01-19
Available Online: 2021-06-21
Issue Publish Date: 2021-05-31

Abstract

Abstract

Soil organic carbon (SOC) is a key indicator of soil quality and ecosystem health. At present, machine learning (ML) models for predicting soil properties based on environmental variables are increasingly popular; however, the performance of different ML algorithms in predicting and mapping SOC, especially at high spatial resolutions, have not been compared. This study aimed to develop, evaluate, and compare the performance of Support Vector Machine (SVM), Random Forest (RF), and Ordinary Kriging (OK) models for predicting and mapping the SOC contents in the northeast of Fujian Province. Remote sensing vegetation indices were derived from Sentinel-2 image data with a spatial resolution of 10 m. These vegetation indices, along with selected terrain and climate factors, were adopted as environmental variables to map SOC using the SVM and RF models. The results showed that the performance of the RF model (RMSE[root-mean-square error]=2.004, r=0.897) was better than that of the OK model (RMSE=4.571, r=0.623) and explained most of the SOC spatial heterogeneity. The SVM model had the poorest prediction accuracy (RMSE=5.190, r=0.431). SOC mapped from the three models had similar spatial patterns, with an increasing SOC gradient from east to west and from south to north of the study area. SOC in the farmlands predicted with the RF model varied in the range of 15.33±4.07 g·kg^-1. Elevation and rainfall were the most important variables for the RF and SVM models, respectively, whereas the remote sensing vegetation indices were less important than elevation.
- Complex landform,
- Soil organic carbon of cultivated land,
- Machine learning,
- Ordinary kriging,
- Digital soil mapping

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References (41)

References

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