书目名称 | Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery |
编辑 | Nasrin Nasrollahi |
视频video | |
概述 | Nominated by the University of California, Irvine, USA, as an outstanding Ph.D. thesis.Presents data sets that reduce false rain signals in satellite precipitation measurements.Provides advances in th |
丛书名称 | Springer Theses |
图书封面 |  |
描述 | .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.. |
出版日期 | Book 2015 |
关键词 | CloudSat precipitation data; CloudSat texts; MODIS satellite observations; award-winning thesis; current |
版次 | 1 |
doi | https://doi.org/10.1007/978-3-319-12081-2 |
isbn_softcover | 978-3-319-36332-5 |
isbn_ebook | 978-3-319-12081-2Series ISSN 2190-5053 Series E-ISSN 2190-5061 |
issn_series | 2190-5053 |
copyright | Springer International Publishing Switzerland 2015 |