通过实地定点土壤取样和光谱测量, 研究了实测光谱反射率与土壤PH值之间的关系.分别采用反射率Reflectance、一阶导数FDR、倒数之对数log(1／R)和波段深度BD四种光谱指标建立对PH值的多元线性回归预测模型, 并且利用验证样本集对回归模型进行了检验.结果表明: 野外实测反射光谱与PH值呈良好的正相关关系, 建模精度最高, R2可达0.873, 具有快速、高精度估算土壤碱化程度的潜力, 既符合野外实际环境, 也利于今后同遥感影像进行对应分析.光谱倒数之对数( lg(1/R))的建模精度略低于实测反射率建模精度, 因此对实测光谱进行倒数之对数计算对于提高估算精度作用不大.而光谱微分法(FDR) 和波段深度(BD)的模型判定系数分别为0.728和0.648, 预测效果不理想.

Based on the monitored data of soil PH and measured VIS-NIR reflectance on given spots, the relationship between measured reflectance and soil PH was analyzed. Besides original field-measured spectrum (R), several spectral indices were also calculated: first derivative reflectance spectrum (FDR), inverse-log spectrum (lg(1/R)) and band depth (BD). Multivariate linear regression models were built to evaluate soil alkalinization level based on these four spectral indices and the model accuracy of PH fitting was discussed with validated sample group. The results showed that there is a significant positive correlation between soil PH and original reflectance. The accuracy of the model based on original spectrum(R) is the best with a value of R2 as high as 0.873. Thus original spectrum(R) had potential ability of rapid and exact estimation of changes in the alkalinization soil. The model can help to further the analysis of the ability of detecting alkalinization with image reflectance because of the original spectrum (R) measured directly from field .The accuracy of inverse-log spectrum predicting model was slightly lower than the accuracy of original reflectance predicting model, so inverse-log spectrum calculating was of less help to improve the predicting efficiency. The R2 of first derivative reflectance spectrum (FDR) and band depth (BD) were 0.728 and 0.648, which were not ideal for the prediction of alkalinization.