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...
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2015
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oai:scholar.dlu.edu.vn:DLU123456789-577892023-11-11T05:52:04Z Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery Nasrollahi, Nasrin Remote sensing Precipitation Infrared detectors Geography 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. 2015-08-26T09:06:00Z 2015-08-26T09:06:00Z 2015 Book 978-3-319-12081-2 https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/57789 en application/pdf Springer |
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Thư viện Trường Đại học Đà Lạt |
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| language |
English |
| topic |
Remote sensing Precipitation Infrared detectors Geography |
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Remote sensing Precipitation Infrared detectors Geography Nasrollahi, Nasrin Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery |
| description |
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. |
| format |
Book |
| author |
Nasrollahi, Nasrin |
| author_facet |
Nasrollahi, Nasrin |
| author_sort |
Nasrollahi, Nasrin |
| title |
Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery |
| title_short |
Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery |
| title_full |
Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery |
| title_fullStr |
Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery |
| title_full_unstemmed |
Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery |
| title_sort |
improving infrared-based precipitation retrieval algorithms using multi-spectral satellite imagery |
| publisher |
Springer |
| publishDate |
2015 |
| url |
https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/57789 |
| _version_ |
1819782843642937344 |