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[1]Yuan Ke,Zhang Chen,Zhao Jianlin,et al.Comparative Analysis on Models for Predicting the Spatial Distribution of Soil Organic Carbon Density with Limited Samples[J].Research of Soil and Water Conservation,2024,31(05):173-181,191.[doi:10.13869/j.cnki.rswc.2024.05.041]

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Comparative Analysis on Models for Predicting the Spatial Distribution of Soil Organic Carbon Density with Limited Samples

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 31 Number of periods: 2024 05 Page number: 173-181,191 Column: Public date: 2024-08-10

Title:: Comparative Analysis on Models for Predicting the Spatial Distribution of Soil Organic Carbon Density with Limited Samples

Author(s):: Yuan Ke¹, Zhang Chen¹, Zhao Jianlin¹, Wang Zhenliang¹, Yang Jie¹, Xu Zhongsheng²; (1.College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China; 2.The Second Surveying and Mapping Institute of Anhui Province, Hefei 230031, China)

Keywords:: soil organic carbon; machine learning model; controlling factor; Chinese Loess Plateau

CLC:: S153.621

DOI:: 10.13869/j.cnki.rswc.2024.05.041

Abstract:: [Objective]The aim of this study is to explore the accuracy and applicability of different machine learning models for predicting the spatial distribution of surface soil organic carbon density(SOCD)with limited samples, which can provide a references for the study of watershed scale carbon pool in the Chinese Loess Plateau.[Methods]In this study, we compared the accuracy and stability of the predicted SOCD in topsoil(0—20 cm)by four machine learning models, namely Multiple Linear Stepwise Regression(SR), Random Forest(RF), Extreme Gradient Boosting(XGB)and Support Vector Machine(SVM), based on the limited measured samples in a sub-watershed of Yanhe River in the Chinese Loess Plateau. [Results](1)Under the condition of limited samples, all models successfully and appropriately predicte the spatial distribution of SOCD, among which the SVM model has the best model performance, and the average RMSE, R² and MAE of 50 predictions is 0.74, 0.43 and 0.64, respectively.(2)The average SOCD of different land use types are consistent between measured and predicted values but shows significant difference among land use types. SOCD decreases in the order:shrubland>forestland>grassland>cropland. The total organic carbon of cultivated land in the study area is 2.39×10⁶t(0—20 cm).(3)The evaluation of feature importance shows that terrain factors, NDVI_max, near-infrared surface reflectance(B5)and Brightness index have significant contributions to the accuracy of predictions. [Conclusion]Under the condition of limited samples, the machine learning model combined with controlling features can be effectively applied to the prediction of the spatial distribution of topsoil SOCD at the watershed scale in the Chinese Loess Plateau.

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Last Update: 2024-08-10