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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...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
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| Formato: | Electrónico eBook |
| Idioma: | Inglés |
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Cham :
Springer International Publishing : Imprint: Springer,
2015.
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| Edición: | 1st ed. 2015. |
| Colección: | Springer Theses, Recognizing Outstanding Ph.D. Research,
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| Acceso en línea: | Texto Completo |
| Sumario: | 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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| Descripción Física: | XXI, 68 p. 41 illus., 38 illus. in color. online resource. |
| ISBN: | 9783319120812 |
| ISSN: | 2190-5061 |