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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.
Qiuwen Zhang,Cheng Wang,Fumio Shinohara, andTatsuo 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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Qiuwen Zhang, Cheng Wang, Fumio Shinohara, 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