Analysis between AMSR-E swath brightness temperature and ground snow depth data in winter time over Tibet Plateau, China

Snow extent and snow depth (SD) are critical parameters in metro-hydrological models and are sensitive to the global climate change. Over the western China, due to the influence from shallow snow, changing seasonal permafrost and the sparse observation stations, the passive microwave remote sensing algorithm show its applicability when using the gradient brightness temperature (Tb) algorithm of 36.5Ghz-18.7Ghz. In this work, we employ one whole-winter Tb extracted from Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) L2A swath dataset and the ground measurements of snow depth (SD) to analyse the snow microwave emission and gradient algorithm ability. The time series analysis shows that the Tb differences (36.5-18.7) and (36.5-10.7) are sensitive to relatively deep snow (>20cm), while the Tb differences (89.0-18.7) are sensitive to the occurrence of the new snow, with a promising correlation with shallow snow (<;15cm) and quickly decreasing (melting) snow depth, which suggest that a high frequency Tb difference could potentially be a good snow monitoring signal for the shallow snow cover over western China.