Using a Linear Unmixing Method to Improve Passive Microwave Snow Depth Retrievals
| Autor: | Shirui Hao, Gongxue Wang, Zhizhong Chen, Lingmei Jiang, Xiaojing Liu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Atmospheric Science
Brightness 010504 meteorology & atmospheric sciences Microwave radiometer 0211 other engineering and technologies 02 engineering and technology Snow 01 natural sciences Physics::Geophysics Weather station Microwave imaging Environmental science Satellite Astrophysics::Earth and Planetary Astrophysics Computers in Earth Sciences Image resolution Physics::Atmospheric and Oceanic Physics Microwave 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11:4414-4429 |
| ISSN: | 2151-1535 1939-1404 |
| DOI: | 10.1109/jstars.2018.2870752 |
| Popis: | Satellite microwave radiometer measurements have been used to extract snow depth information for approximately three to four decades. However, the coarse spatial resolution of satellite microwave radiobrightness observations affects the snow depth retrieval accuracy and hinders the application of snow depth data in studies on water, energy, and carbon cycles. Therefore, snow depth measurements require better spatial resolution to improve the retrieval accuracy and enhance the resolution of the data. In this paper, an improved linear unmixing method is proposed to improve the accuracy of existing methods and downscale brightness temperatures from satellite observations to improve snow depth estimates. Tests conducted on a dataset consisting of both simulated and satellite data show the effectiveness of the proposed method. The unmixing method is then applied to FengYun-3B satellite/microwave radiation imager measurements for snow depth retrievals, and the resulting snow depths are compared with weather station observations from January to February 2011. The results show that the snow depth estimated from downscaled brightness temperatures performs better than the original data and presents higher correlations and lower root mean square errors. |
| Databáze: | OpenAIRE |
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