Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation

Satellite‐based remotely sensed data have the potential to provide hydrologically relevant information about spatially and temporally varying physical variables. A methodology for estimating such variables from multichannel remotely sensed data is presented; the approach is based on a modified count...

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Bibliographic Details
Published in:Water resources research 1999-05, Vol.35 (5), p.1605-1618
Main Authors: Hsu, Kuo‐lin, Gupta, Hoshin V., Gao, Xiaogang, Sorooshian, Soroosh
Format: Article
Language:English
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Summary:Satellite‐based remotely sensed data have the potential to provide hydrologically relevant information about spatially and temporally varying physical variables. A methodology for estimating such variables from multichannel remotely sensed data is presented; the approach is based on a modified counterpropagation neural network (MCPN) and is both effective and efficient at building complex nonlinear input‐output function mappings from large amounts of data. An application to high‐resolution estimation of the spatial and temporal variation of surface rainfall using geostationary satellite infrared and visible imagery is presented. Test results also indicate that spatially and temporally sparse ground‐based observations can be assimilated via an adaptive implementation of the MCPN method, thereby allowing on‐line improvement of the estimates.
ISSN:0043-1397
1944-7973
DOI:10.1029/1999WR900032