土壤2024，Vol.56Issue(4)：857-865,9.DOI:10.13758/j.cnki.tr.2024.04.020
基于辅助变量的紫色土耕地土壤有机质空间预测
Soil Organic Matter Prediction of Purple Soil Based on Auxiliary Variables
摘要
Abstract
This study collected a total of 135 samples from purple soil farmlands in the hilly region of central Sichuan.Based on the GEE cloud platform,high-resolution Sentinel-2A data,SRTMGLlv3.0 elevation data,and SoilGrids soil attribute data were invoked,and texture feature data was innovatively added.Two prediction models were constructed by using gradient enhancement decision tree(GBDT)and random forest(RF)to invert SOM.The results showed that SOM content of purple soil farmlands in the study area was relatively low,with the level ranging from 2 to 6 levels.The models constructed by GBDT algorithm had higher prediction accuracy(R2=0.687,r=0.829,RMSE=5.668 g/kg)compared to RF algorithm(R2=0.514,r=0.717,RMSE=6.765 g/kg).The R2 with texture features increased by 6.80％and 1.70％,respectively.TGIS study can provide a new scientific approach for SOM prediction.关键词
土壤有机质/机器学习/紫色土/GEEKey words
Soil organic matter/Machine learning/Purple soil/GEE分类
农业科技引用本文复制引用
刘雅璇,于慧,罗勇..基于辅助变量的紫色土耕地土壤有机质空间预测[J].土壤,2024,56(4):857-865,9.基金项目
国家自然科学基金项目(41971273)和四川省地质调查研究院财政资金项目(SCIGS-CZDXM-2024014)资助. （41971273）
