Comparing four sub-pixel algorithms in MODIS snow mapping

Accurate monitoring of snow cover extent is an important research goal in the science of Earth systems. Now mixture modelling is important tool in the remote sensing community as researchers attempt to resolve sub-pixel, area information. In the paper, I tested four different sub-pixel analysis methods: Linear Mixture Model (LMM), Fuzzy c-means Clustering (FCM), Back-Propagation Neural Network (BPNN), and Support Vector Machine (SVM). Overall, the LMM and SVM method provided better estimates of the snow cover components than others, and the results of this study provide comprehensive information of the utility of sub-pixel analysis for the estimation of snow cover components and suggest that the comparatively accurate snow cover estimation is attainable from medium resolution satellite imagery.