已有土壤有机质的光谱预测模型其适用性受建模样本的采样尺度、 土壤类型及光谱参数限制, 需要在大尺度及范围上进一步检验适用性, 并比较分析不同建模方法的建模效果以寻求适用性更好、 精度更高的定量模型。 在黑河上游大尺度范围采得225个土壤样品, 进行了土壤有机质（SOC）及光谱反射率测定后将样本划分为建模集（180个土样）与验证集（45个土样）。 将土壤光谱反射率(R)变换处理后得到连续统去除(CR)、 倒数(REC)、 倒数之对数(LR)、 一阶微分(FDR)及Kubelka-Munck变换系数共6种指标, 针对建模集分别采用逐步线性回归与偏最小二乘回归方法建立12种光谱指标与SOC的数学模型, 并采用验证集进行模型预测效果评价。 结果表明: (1）采用逐步线性回归或偏最小二乘回归方法建模, LR指标对SOC变化的解释效果都是最好, 是SOC的最优预测因子。 (2）基于LR指标建立的SOC模型中, 采用偏最小二乘回归模型比逐步线性回归模型的预测精度更好, 相较于黑河上游已有的经验模型, 偏最小二乘回归法建立的模型的预测效果也更好。 (3）采用本实验的225个土壤样品对比验证了黑河上游仅有的SOC模型。 该模型的SOC预测值与实测值通过了均值T检验且Pearson相关系数达0.826, 表明在局部典型区域建立的SOC预测模型, 可以应用到更大尺度上的土壤有机质预测研究。

Existing prediction models of soil organic matter content (SOC) are restricted by some factors, such as sampling scale, soil type and spectral parameters of samples. Therefore, it is necessary to make a comparative analysis on larger scales to build a quantitative model with better feasibility and greater accuracy. A total of 225 soil samples were collected in an extensive region of the upper reaches of Heihe river basin. SOC and spectral reflectance were being measured. All the samples were divided into 2 subsets-a modeling subset (180 samples) and a validation subset (45 samples). Six indices were obtained through transformation of soil spectral reflectance (R), continuum-removal (CR), reciprocal (REC), logarithm of reciprocal (LR), first-order differential (FDR) and Kubelka-Munck transformation coefficient (K-M). To build the mathematical model of SOC with 12 spectral indices, two methods, i.e., stepwise linear regression and partial least-square regression were used based on the modeling subset, respectively; the validation subset is used for model evaluation. The results indicated that: (1) Regardless of different modeling methods, model between SOC and LR index was always the best among the 6 reflectance-related indices. LR was the best index for predicting SOC; (2) For the model based on the LR index, the accuracy of model using partial least-square regression method was better than that using stepwise linear regression method; (3)225 samples were compared to verify the former available published SOC model. Both the predicted and measured values passed the mean value t-test, and the Pearson correlation coefficient reached 0.826. It shows that local prediction model can be applied to the research of predicting SOC in the larger scale.