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Progress in study of snow parameter inversion by passive microwave remote sensing |
| SUN Zhiwen1, YU Pengshan2, XIA Lang3, WU Shengli4, JIANG Lingmei5, GUO Lei1 |
1. Space Star Technology Co., Ltd., Beijing 100086, China;
2. Beijing Shenzhou Aerospace Software Technology Co., Ltd., Beijing 100094, China;
3. Key Laboratory of Resource Remote Sensing and Digital Agriculture, Beijing 100081, China;
4. National Satellite Meteorological Center, Beijing 100081, China;
5. State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China |
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Abstract Snow depth(SD)and snow water equivalent(SWE)are key parameters in hydrology and climate research,especially in the snowstorm monitoring. In this paper,the authors first provided a brief background of the physical basis of the SD and SWE inversion algorithm,i.e., the snow microwave radiative transfer model,and discussed the snow microwave radiation and scattering in different microwave frequencies. After that, the former snow estimation inversion algorithms were reviewed, which can be categorized into two types: linear brightness temperature gradient and prior knowledge-based from mathematical methods. The advantages and limitations of the two algorithms were summarized. The linear brightness temperature gradient method is easier and runs faster,but it only suits specific study areas. For the establishment of a prior knowledge-based model,researchers need to obtain the sample data and repeated training so as to achieve higher accuracy. However, the model requires the independence and significant mean difference of the samples. The SD and SWE inversion algorithms for Fengyun-3 microwave radiation imager (FY-3 MWRI) were described,which are composed of global business algorithm and improved regional algorithm for China. Finally, the research focuses in this aspect were predicted.
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| Keywords
LiDAR
point cloud filtering
micro-geomorphologic features
fissure identification
linear detection
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Issue Date: 08 December 2014
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