一个地区的不透水面覆盖度不仅是该地区城镇化程度重要指示因子，也是该地区生态环境状况的重要指示因子。 现有的不透水面遥感提取方法，多集中在城区尺度上。而流域尺度上快速、准确的不透水面遥感提取方法在国内外还鲜有 研究。本研究以覆盖海河流域同一季节的Landsat影像为数据源，利用已有土地利用数据集中的道路、城市、农村和工业 用地对非不透水区进行掩膜，通过选取亮暗植被、高低反照度不透水面、干湿土壤端元，采用多端元光谱混合分解模型提 取了流域尺度上的不透水面。精度评价结果显示，该方法估算的不透水面与真实结果之间的平均相对误差为12.1%，相关 系数为0.83，精度较高，适合于流域尺度的不透水面提取。
Impervious surface coverage in a region is not only an indicator of the degree of urbanization but also a major indicator of environmental quality. Most of the existing methods of extracting impervious surface based on remote sensing concentrate on an urban scale, but the rapid and accurate methods of extracting impervious surfaces in a basin scale are nearly nonexistent in China and abroad. In this study, we used Landsat images acquired in same season covering the entire Hai Basin as data source, and generated a mask for removing the non-impervious surfaces using a land-use data set of roads, and urban, rural, and industrial land. Then, by selecting bright and dark vegetation endmember, high albedo and low albedo impervious surface endmember, and dry and wet soil endmember, we applied a Multiple Endmember Spectral Mixture Analysis (MESMA) model to extract impervious surfaces in the basin scale. The accuracy assessment results showed high accuracy, in that the mean relative error (MRE) and correlation coefficient (R) of all samples were 12.1% and 0.83, respectively, which indicated that the method of extracting impervious surfaces in a basin scale was feasible.