Quasi-real time estimation of distributed precipitation using EOS/MODIS remote sensing datasets

Qiuwen Zhang; Cheng Wang; Fumio Shinohara; Tatsuo Yamaoka

doi:10.1117/12.775458

10 November 2007 Quasi-real time estimation of distributed precipitation using EOS/MODIS remote sensing datasets

Qiuwen Zhang, Cheng Wang, Fumio Shinohara, Tatsuo Yamaoka

Proceedings Volume 6795, Second International Conference on Space Information Technology; 67957J (2007) https://doi.org/10.1117/12.775458
Event: Second International Conference on Spatial Information Technology, 2007, Wuhan, China

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.

Citation Download Citation

Qiuwen Zhang, Cheng Wang, Fumio Shinohara, and Tatsuo Yamaoka "Quasi-real time estimation of distributed precipitation using EOS/MODIS remote sensing datasets", Proc. SPIE 6795, Second International Conference on Space Information Technology, 67957J (10 November 2007); https://doi.org/10.1117/12.775458

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