Estimation of rainfall based on MODIS using neural networks

Rainfall is not only an essential parameter in hydrology and in the research of water resources, but also an important consideration for the issue of flood control, disaster mitigation, runoff forecast, irrigation, etc. However, the conventional monitoring approaches of rainfall involve many disadvantages, such as limited observing range, high cost and only-one-point rainfall observation. Consequently, how to get the rainfall of any part of the valley attracts more and more attention. In this study, the main meteorological parameters which influencing the rainfall can be extracted from the MODIS satellite cloud imagery, and these meteorological parameters are combined with the actual observed rainfall data which is obtained from ground-based rainfall site correspondingly. The remote sensing retrieval model is established respectively based on the BP neural network and GA-BP neural network, and a better effect of error precision estimation is obtained. It’s also proved that the high resolution of MODIS cloud products can be used to estimate rainfall rate.