Inversion of Soil Organic Carbon Content in the Central Yunnan Plateau Based on Sentinel-2A Images and XGBoost Model
YAN Zhengfei
YANG Minglong
TANG Xiujuan
XIA Yonghua
YANG Zhen
LI Wantao
Abstract：Soil organic carbon(SOC)plays a crucial role in maintaining soil fertility,promoting plant growth,and supporting sustainable agricultural development.Therefore,efficient and accurate acquisition of SOC content is of great significance.This study utilized Sentinel-2A multispectral remote sensing imagery combined with measured SOC content,Sentinel-1 backscattering coefficients,vegetation indices and topographic factors(elevation,slope,aspect)to investigate the inversion of SOC content in the Yao'an irrigation district using Random forest(RF),Deep forest(DF),and XGBoost models.The results indicated that,from the perspective of different combinations of auxiliary variables,incorporating various factors(vegetation indices,topographic factors,backscattering coefficients,etc.)significantly improved the prediction accuracy of SOC content.Specifically,the inclusion of topographic factors increased the R2 values of the RF,DF and XGBoost models by 0.052 3,0.039 8,0.068 9,respectively.Analysis of the prediction results from different models showed that both XGBoost and DF models could effectively predict SOC content in cultivated land.Among them,the XGBoost model combined with the M3 variable set(including 12 bands of Sentinel-2A spectral image,vegetation indices,Sentinel-1 backscattering coefficients,and topographic factors)achieved the highest prediction accuracy(R2=0.810 6,RMSE=1.813 2),followed by the DF model(R2=0.751 2,RMSE=1.925 5),while the RF model exhibited relatively lower predictive performance(R2=0.624 5,RMSE=2.503 1).
Keywords：Soil organic carbonSentinel-2ARemote sensing inversionMachine learningXGBoost algorithmCentral Yunnan Plateau
Publication Date：2025-02-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：9( 145-153 )