Snow cover plays an important role in climate, hydrological and ecological processes. MODIS sub-pixel snow cover algorithm applies the traditional methods using a binary classification. Large error can be expected when using moderate to coarse resolution imagery to map snow covered areas at a regional scale. The objective of this study is to develop an automatic snow mapping algorithm at sub-pixel resolution for MODIS. We improved the inversion of sub-pixel snow cover based on MESMA( Multiple Endmember Spectral Analysis) by selecting optimal endmembers. Although the original MESMA has a clear physical meaning and high precision, it is difficult to calculate efficiently at large area. Therefore, in order to calculate automatically snow fraction from MODIS, it is necessary to improve the method of unsupervised endmember selection to enhance the computational efficiency of MESMA. The new improved estimation of MODIS sub-pixel snow cover based on MESMA enhanced the accuracy,in contrast to MODIS/NASA standard snow cover product. In addition, from the comparisons with high resolution ETM images, the RMSE could be 16%. With the spatial window increases from 2x2 to 10x10, the estimated error decreases within 6%. The improved algorithm is more efficient for operational processing.%积雪覆盖(snow cover)是气候、水文和生态环境等研究领域中很重要的参数之一.与积雪覆盖二值技术不同,亚像元雪盖反演技术可以在给定卫星观测空间分辨率前提下进一步提高积雪覆盖面积的反演精度.“多端元光谱混合分析”方法反演亚像元积雪覆盖度具有物理意义明确、精度高等优势.但由于其需要通过端元选取、最小二乘法反演等计算,运行效率较低,难以满足大数据量计算.我们发展了一种改进“多端元光谱混合分析”方法反演MODIS亚像元积雪覆盖的算法.该算法通过对MOD09GA数据进行图像端元自动提取,并利用能够代表图像端元类的典型端元库进行“多端元光谱混合分析”反演亚像元积雪覆盖.我们在端元选取、多端元线性混合模型分解等方面进行了改进与发展,不仅保证了产品精度,同时提高了计算的时效性.
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