Abstract
Snow cover over the Qinghai-Tibetan Plateau (QTP) plays an important role in climate, hydrological, and ecological systems. Currently, passive microwave remote sensing is the most efficient way to monitor snow depth on global and regional scales; however, it presents a serious overestimation of snow cover over the QTP and has difficulty describing patchy snow cover over the QTP because of its coarse spatial resolution. In this study, a new spatial dynamic method is developed by introducing ground emissivity and assimilating the snow cover fraction (SCF) and land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive snow depth at an enhanced spatial resolution. In this method, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) brightness temperature and MODIS LST are used to calculate ground emissivity. Additionally, the microwave emission model of layered snowpacks (MEMLS) is applied to simulate brightness temperature with varying ground emissivities to determine the key coefficients in the snow depth retrieval algorithm. The results show that the frozen ground emissivity presents large spatial heterogeneity over the QTP, which leads to the variation of coefficients in the snow depth retrieval algorithm. The overestimation of snow depth is rectified by introducing the ground emissivity factor at 18 and 36 GHz. Compared with in situ observations, the snow cover accuracy of the new method is 93.9%, which is better than the 60.2% accuracy of the existing method (old method) which does not consider ground emissivity. The bias and root-mean-square error (RMSE) of snow depth are 1.03 cm and 7.05 cm, respectively, for the new method; these values are much lower than the values of 6.02 cm and 9.75 cm, respectively, for the old method. However, the snow cover accuracy with depths between 1 and 3 cm is below 60%, and snow depths greater than 25 cm are underestimated in Himalayan mountainous areas. In the future, the snow cover identification algorithm should be improved to identify shallow snow cover over the QTP, and topography should be considered in the snow depth retrieval algorithm to improve snow depth accuracy in mountainous areas.
Highlights
Snow cover on the Qinghai-Tibetan Plateau (QTP) contributes a large portion of the water supply for the large rivers in Asia [1], reduces the incident solar radiation absorbed at the surface, inhibits heat stored in the ground from being released to the overlying atmosphere, and subsequently influences climate change [2], which will further influence ecosystems and spring phenology [3,4,5]
There are four passive microwave (PMW) snow depth/snow water equivalent (SWE) products covering the QTP: National Aeronautics and Space Administration (NASA)’s Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), standard SWE product [14], the European Space Agency’s (ESA) GlobSnow SWE product [15,16], the snow depth product from the West Data Center of China (WESTDC) [17], and the China Meteorological Administration (CMA)’s Fengyun snow depth product [18]. All of these methods were developed based on the original baseline retrieval algorithm (Chang method) by which the snow depth is assumed to be linearly dependent on the brightness temperature difference between 18 GHz and 36 GHz (TBD), and coefficients used in the method, which was developed based on the fundamental radiative transfer theory [19], changed with snow characteristics
A spatial dynamic snow depth retrieval algorithm was developed to derive snow depth with an enhanced spatial resolution for the QTP. This algorithm introduced ground emissivity to improve the snow depth accuracy, which was calculated by dividing the AMSR-E brightness temperature by the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST)
Summary
Snow cover on the Qinghai-Tibetan Plateau (QTP) contributes a large portion of the water supply for the large rivers in Asia [1], reduces the incident solar radiation absorbed at the surface, inhibits heat stored in the ground from being released to the overlying atmosphere, and subsequently influences climate change [2], which will further influence ecosystems and spring phenology [3,4,5]. There are four PMW snow depth/snow water equivalent (SWE) products covering the QTP: NASA’s Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), standard SWE product [14], the European Space Agency’s (ESA) GlobSnow SWE product [15,16], the snow depth product from the West Data Center of China (WESTDC) [17], and the CMA’s Fengyun snow depth product [18] All of these methods were developed based on the original baseline retrieval algorithm (Chang method) by which the snow depth is assumed to be linearly dependent on the brightness temperature difference between 18 GHz and 36 GHz (TBD), and coefficients used in the method, which was developed based on the fundamental radiative transfer theory [19], changed with snow characteristics.
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