河南农业科学2025，Vol.54Issue(2)：145-153,9.DOI:10.15933/j.cnki.1004-3268.2025.02.017
基于Sentinel-2A影像和XGBoost模型的滇中高原地区土壤有机碳含量反演研究
Inversion of Soil Organic Carbon Content in the Central Yunnan Plateau Based on Sentinel-2A Images and XGBoost Model
摘要
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).关键词
土壤有机碳/Sentinel-2A/遥感反演/机器学习/XGBoost算法/滇中高原Key words
Soil organic carbon/Sentinel-2A/Remote sensing inversion/Machine learning/XGBoost algorithm/Central Yunnan Plateau分类
农业科技引用本文复制引用
严正飞,杨明龙,唐秀娟,夏永华,杨赈,李万涛..基于Sentinel-2A影像和XGBoost模型的滇中高原地区土壤有机碳含量反演研究[J].河南农业科学,2025,54(2):145-153,9.基金项目
国家自然科学基金项目(62266026) （62266026）
