随机森林回归模型用于土壤重金属含量多光谱遥感反演

本文以陕西省柞水县大西沟矿区为研究区域，通过实地采集土壤样本，结合在Landsat 8多光谱遥感影像上提取的辐射亮度值和光谱衍生指数，以及从ASTER GDEM提取的3种地形因素，通过相关性分析确定了建模因子，并以K折交叉验证法建立了砷、铜、铅3种重金属元素的随机森林回归模型。试验结果表明，所建立模型的预测精度优于多元线性回归模型和CART模型，可见随机森林回归模型适用于在小样本情况下的矿区重金属含量反演。经现场调查，空间反演结果与实际情况较符合，证明了基于多光谱遥感的随机森林回归模型在矿区土壤重金属反演中的准确性。

Multi-spectral remote sensing inversion of soil heavy metal content using random forest regression model

This paper takes Daxigou mining area in Zhashui county, Shaanxi province as the research area. By collecting soil samples on the spot, combining with radiation luminance value and spectral derivative index extracted from Landsat 8 multi-spectral remote sensing images, and three topographic factors extracted from ASTER GDEM, modeling factors are determined through correlation analysis. The random forest regression model of arsenic, copper and lead is established by K-fold cross validation. The experimental results show that the prediction accuracy of the established model is better than that of the multiple linear regression model and the CART model, which verifies that the random forest regression model is suitable for the heavy metal content inversion in the case of small samples. Through the field investigation, the spatial inversion results are in good agreement with the actual situation, which proves the accuracy of the random forest regression model based on multi-spectral remote sensing in the soil heavy metal inversion of mining area.