基于北京市52个潮土样本的高光谱数据和Landsat TM、 环境减灾卫星(HJ)影像的波段响应函数, 生成宽波段多光谱模拟数据, 对比分析了室内实测光谱数据、 宽波段模拟数据与土壤有机质含量的相关性, 筛选敏感波段, 利用偏最小二乘法构建北方潮土有机质含量预测模型。 研究表明: 在宽波段模拟数据建立的模型中, 由Landsat TM模拟数据的差值土壤指数（DSI）、 比值土壤指数（RSI）、 归一化土壤指数（NDSI）及其第3波段共同构建的模型最优, 其决定系数与均方根误差分别为0.586和0.280； 与实测光谱数据相比, 模拟数据的最佳预测模型, 均优于除一阶微分、 弓曲差以外的其他10种高光谱模型。 因此, 利用多光谱数据预测潮土有机质含量是可行的。

The present study aims to assess the feasibility of multi-spectral data in monitoring soil organic matter content. The data source comes from hyperspectral measured under laboratory condition, and simulated multi-spectral data from the hyperspectral. According to the reflectance response functions of Landsat TM and HJ-CCD (the Environment and Disaster Reduction Small Satellites, HJ), the hyperspectra were resampled for the corresponding bands of multi-spectral sensors. The correlation between hyperspectral, simulated reflectance spectra and organic matter content was calculated, and used to extract the sensitive bands of the organic matter in the north fluvo-aquic soil. The partial least square regression (PLSR) method was used to establish experiential models to estimate soil organic matter content. Both root mean squared error (RMSE) and coefficient of the determination (R2) were introduced to test the precision and stability of the modes. Results demonstrate that compared with the hyperspectral data, the best model established by simulated multi-spectral data gives a good result for organic matter content, with R2=0.586, and RMSE=0.280. Therefore, using multi-spectral data to predict tide soil organic matter content is feasible.