Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery
This thesis transforms satellite precipitation estimation through the integration of a multi-sensor, multi-channel approach to current precipitation estimation algorithms, and provides more accurate readings of precipitation data from space. Using satellite data to estimate precipitation from space...
Gorde:
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| Formatua: | Liburua |
| Hizkuntza: | English |
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Springer
2015
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| Sarrera elektronikoa: | https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/57789 |
| Etiketak: |
Etiketa erantsi
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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| Gaia: | This thesis transforms satellite precipitation estimation through the integration of a multi-sensor, multi-channel approach to current precipitation estimation algorithms, and provides more accurate readings of precipitation data from space.
Using satellite data to estimate precipitation from space overcomes the limitation of ground-based observations in terms of availability over remote areas and oceans as well as spatial coverage. However, the accuracy of satellite-based estimates still need to be improved.
The approach introduced in this thesis takes advantage of the recent NASA satellites in observing clouds and precipitation. In addition, machine-learning techniques are also employed to make the best use of remotely-sensed "big data." The results provide a significant improvement in detecting non-precipitating areas and reducing false identification of precipitation. |
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