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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.
| Autorius: | Nasrin Nasrollahi |
| Serija: | Springer Theses |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2014 |
| Knygos puslapių skaičius: | 92 |
| ISBN-10: | 3319120808 |
| ISBN-13: | 9783319120805 |
| Formatas: | 241 x 160 x 10 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery“
