Subpixel snow mapping of the Qinghai–Tibet Plateau using MODIS data

In this work, an algorithm for automatic subpixel snow mapping was developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data of the Qinghai–Tibet Plateau. The algorithm consists of two parts: cloud removal and snow mapping. An approach to remove cloud was presented and it was demonstrated to remove thick and thin clouds well from MODIS images. Multiple-endmember spectral mixture analysis was used in the subpixel snow mapping of the Qinghai–Tibet Plateau, and multiple indices (including the normalized difference vegetation index and normalized difference snow index) were introduced to automatically select the endmembers. Additionally, the combined use of typical and neighboring endmembers was introduced into the unmixing of mixed pixels. Finally, highly accurate snow-cover data of the Qinghai–Tibet Plateau obtained with this algorithm were stored in a spatio-temporal database. The results of the subpixel snow mapping were validated with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data recorded at the same time as corresponding MODIS data. According to the validation results, the correlation coefficients of the MODIS results versus the ASTER data exceed 0.9, and the root-mean-square errors are less than 0.2.

► MESMA was used to conduct subpixel snow mapping of the Qinghai–Tibet Plateau using MODIS data. ► Using multiple indices, an approach to selecting automatically endmembers was developed for subpixel snow mapping. ► The combined use of the typical and neighboring endmembers was introduced to unmix pixels. ► An approach to removing thick and thin clouds simultaneously was developed for MODIS images.