混合像元分解法提取积雪盖度

UNMIXING METHOD APPLIED TO NOAA-AVHRR DATA FOR SNOW COVER ESTIMATION

通过对积雪、地物和云进行光谱分析 ,指出积雪在传统的NOAA AVHRR可见光和近红外通道的高反射性特点和新增的 1 .6μm红外通道上的低反射性特点为提取积雪盖度提供了大量的光谱信息。首先对AVHRR数据进行主成分分析 ,提取含 99%信息量的前两个主分量 ,对其进行散点图分析 ,获取终元。最后使用两种策略进行多光谱混合像元分解 ,提取积雪盖度参数 ,结果很相似 。

Based on the spectral analysis of snow, soil, vegetaion and cloud, it is pointed out that the first two channels of traditional NOAA-AVHRR have troubles to distinguish snow from cloud, and the low reflectance of snow in 1.6 μm infrared channel can be used not only to distinguish snow from cloud but also to supply more spectral information to extract snow cover. So the principal components analysis (PCA) was made to AVHRR data, and it was found that the PCA-transformed first two principal components (PCA1, PCA2) contributes about 99% cumulative variance. And the scatter plot to these components was analyzed and the endmembers were given. Also, two different methods were adopted to extract snow cover by using spectral linear mixing model, and results are highly consistent, which indicate that the unmixing method is an effective way to retrieve snow cover parameter.