Share Email Print
cover

Proceedings Paper

Quasi-real time estimation of distributed precipitation using EOS/MODIS remote sensing datasets
Author(s): Qiuwen Zhang; Cheng Wang; Fumio Shinohara; Tatsuo Yamaoka
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

The relationships between the atmosphere products of EOS/MODIS and precipitation are analyzed. Some key meteorological factors tightly related to precipitation are then selected. With the key meteorological factors extracted from EOS/MODIS remote sensing datasets and the corresponding observed precipitation being the input and output layer respectively, a Back Propagation(BP) Artificial Neural Network(ANN) is learned and trained. As the test and application, the distributed precipitations in Qingjiang river basin located at central China are estimated with the established model. It is concluded that the precipitations estimated by the BP ANN based on EOS/MODIS are nearly equal to the observed ones at the rainfall stations distributed in the river basin. It is revealed that the integration of EOS/MODIS and ANN provides a new effective way to estimate the distributed precipitation in river basin near real time.

Paper Details

Date Published: 10 November 2007
PDF: 6 pages
Proc. SPIE 6795, Second International Conference on Space Information Technology, 67957J (10 November 2007); doi: 10.1117/12.775458
Show Author Affiliations
Qiuwen Zhang, Huazhong Univ. of Science and Technology (China)
Cheng Wang, Huazhong Univ. of Science and Technology (China)
Fumio Shinohara, Information and Science Techno-System Co., Ltd. (Japan)
Tatsuo Yamaoka, Information and Science Techno-System Co., Ltd. (Japan)


Published in SPIE Proceedings Vol. 6795:
Second International Conference on Space Information Technology

© SPIE. Terms of Use
Back to Top