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Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation
Author(s) -
Hsu Kuolin,
Gupta Hoshin V.,
Gao Xiaogang,
Sorooshian Soroosh
Publication year - 1999
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/1999wr900032
Subject(s) - geostationary orbit , remote sensing , computer science , artificial neural network , satellite , satellite imagery , estimation , environmental science , artificial intelligence , geology , engineering , management , aerospace engineering , economics
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.