Main content area

AMSR2 snow depth downscaling algorithm based on a multifactor approach over the Tibetan Plateau, China

Wang, Yunlong, Huang, Xiaodong, Wang, Jianshun, Zhou, Minqiang, Liang, Tiangang
Remote sensing of environment 2019 v.231 pp. 111268
algorithms, climate, data collection, grasslands, hydrologic cycle, land cover, landscapes, microwave radiometers, models, monitoring, remote sensing, snow, snowpack, spatial variation, surface roughness, temperature, temporal variation, uncertainty, China, Japan
Accurate dynamic measurements of snow depth (SD) on the Tibetan Plateau (TP) are of great importance for understanding local climate systems and surface hydrological cycles. Currently, passive microwave remote sensing is the most effective approach used to monitor the spatiotemporal variations in SD. However, the large uncertainties and low spatial resolution of existing SD products lead to less satisfactory passive microwave remote sensing in regions with complex terrain conditions, strong seasonal transitions and great spatiotemporal heterogeneity, such as the TP. In this study, geographic location, terrain, snow cover days and brightness temperature were evaluated. Then, critical factors were used to develop an Advanced Microwave Scanning Radiometer 2 (AMSR2) SD downscaling model. The results indicated the following: (1) SD is greatly influenced by geographic location, snow covered days, terrain parameters and brightness temperature difference. The surface roughness exhibited the best correlations with ground observations, which reflects 44% of the variation in SD; (2) the novel multifactor power SD downscaling model, which combines AMSR2 brightness temperature data and other auxiliary data, showed higher accuracy and stability and was closely correlated with the SD ground observations, and it reflected 80% of the SD variation; (3) compared with the AMSR2 ascending and descending orbital SD products released by the Japan Aerospace Exploration Agency (JAXA) and the SD datasets in China from the Environmental and Ecological Science Data Center for West China (WESTDC), the proposed downscaled SD datasets for the TP were greatly improved, and the root-mean-square error (RMSE) and average absolute error (MAE) were greatly reduced (2.00 cm and 0.25 cm, respectively); (4) the downscaled SD datasets for the TP showed good accuracy (RMSE = 0.58 cm) in areas with a SD of <3 cm; and (5) the worst monitoring accuracy of the downscaled SD product was for grasslands, with a RMSE of 2.07 cm. The best accuracy of the downscaling SD product was found for bare land cover conditions, with an RMSE of only 0.41 cm.