Soil Organic Matter Prediction of Purple Soil Based on Auxiliary Variables
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.
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