中文摘要：

目前运用高光谱数据估算土壤有机质的模型精度已经可以达到精准农业的要求,但其数据的整理和运算过程较为复杂且观测尺度较小。为节省资源,提高效率并为多光谱遥感估算土壤有机质积累经验,该文将Landsat8_OLI多光谱遥感影像各波段的反射率数据与地面土壤有机质SOM（soil organic matter）实测数据相结合,利用SPSS软件及多元线性回归分析方法建立基于反射率R、反射率倒数1/R、反射率倒数对数LN（1/R）、反射率一阶导数FDR（first derivative reflectance）的土壤有机质定量估算模型,精度检验后择取最优模型通过多光谱遥感波段运算的方式推广至整个研究区。结果表明：FDR模型的精度更高,RMSE为0.215,F检验结果为4.072,预测值与实际值之间的决定系数R2为0.963。基于该模型估算研究区空间范围的土壤有机质含量,得出土壤有机质含量在0~5 g/kg之间的面积占总研究区的84.065%,〉10 g/kg的面积仅仅为0.001 5%。在4种土地类型中工矿用地SOM平均含量为最高的7.35 g/kg,受开采的煤炭中有机质影响较大。裸地面积2 674.44 km2,占研究区面积的63%,SOM平均含量6.12 g/kg;盐渍地和荒漠林地SOM含量偏低。总之,运用多光谱遥感数据估算干旱区土壤有机质的方法可行,也为遥感估算其他地表参数提供参考。

英文摘要：

Soil is related closely to human living and vegetation grow. The quality of soil organic matter（SOM） influences plant development. Scientists take a variety of researcheson soil. Many findings focus on the estimation of SOM using remote sensing data, which are usually hyperspectral and multispectral. The former has a detailed result of band information, while the latter provides a macroscopical and convenient way to get in whole area. In addition, processing hyperspectral data needs a strong mathematical background and software technology, while processing multispectraldata needs less. To apply the multispectral method to make decisions on buildings and planning is of great significance. In order to save resources, increase efficiency and accuracy, in May 2014, we collected soil samples in the various layers of 0~10,10 ~20 and 20 ~30 cm, and there were totally 45 points marked by GPS（global positioning system） on Google Earth. The weighed aluminum box was used to hold some soil in each layer. The collections were taken back and dried for 24 h. Then the dried soil was weighed and the soil moisture was calculated. Meanwhile, the image needed pretreatment. The atmospheric correction should be taken to remove bands′ noises to get clear data. Then the pixels of the image for each sample point were used to establish models. And at the same time, other soil was crushed and sieved in 2 mm, and the SOM was measured by the potassium dichromate volumetric method. The final work was to combine the reflectance data of multispectral image and the measured SOM data. We used the reflectance（ R）, the reflectance reciprocal（1/ R）, the reflectance reciprocal′s logarithm（ln（1/R））, the reflectance′s first derivative（FDR） and the measured SOM to build multiple linear regression models, and then, it was found that the FDR model had a better precision with the R2 of 0.963 between the predicted and the measured. This meant that the more effective approach could be applied to express the regional SOM if n