基于Landsat-8数据的土壤颜色预测方法研究

Soil Color Prediction Method Based on Landsat-8 Data

选择黑龙江省292个自然风干土壤样品,室内测定土壤高光谱反射率,然后依据三刺激值法计算土壤CIE XYZ色彩空间各颜色分量,用于土壤颜色预测和验证。同时,提取各样品采集点位的Landsat-8 OLI原始反射率数据,计算归一化差值植被指数、归一化差值水体指数、归一化差值湿度指数、归一化差值不透水面指数,并据此提出建模光谱筛选阈值。进一步采用偏最小二乘回归模型,结合提取出的遥感光谱进行各土壤颜色分量的预测。结果显示:CIE XYZ颜色各分量的验证R2分别为0.76、0.76和0.69,RPD分别为1.74、1.76和1.68,表明利用偏最小二乘法建立的模型可以对土壤颜色进行近似预测。不同土地利用类型预测结果拟合显示,耕地土壤颜色各分量预测效果均优于林地和草地。在不同有机碳含量下分别进行土壤颜色建模预测,当有机碳含量较低时,土壤颜色预测结果较好。

A total of 292 air-dried soil samples were used from Heilongjiang Province,and the hyper-spectral reflectance data were also measured in the laboratory.Meanwhile,color components of all soil samples were calculated in the CIE XYZ color space according to the three-stimulus method to predict and validate soil color.Then,Landsat-8 OLI original reflectance data of each soil sample site were extracted to calculate the normalized difference vegetation index,normalized difference water index,normalized difference moisture index and normalized difference impervious surface index.Based on these remote sensing indexes,the threshold value of modeling spectra screening is proposed.Combined with the extracted remote sensing spectra,the partial least squares regression model was used to predict soil color components.The results showed that for the color components of CIE X,Y and Z,the validation R^(2) value were 0.76,0.76 and 0.69,and the RPD value were 1.74,1.76 and 1.68,indicating that the model established by partial least squares can be used to predict soil color.The prediction of different land use types showed that cultivated land soil color was better in prediction than those of forest and grassland.Soil color was also predicted under different organic carbon contents,showing the prediction was better in lower organic carbon content.