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

Kuo Lin Hsu, Hoshin V. Gupta, Xiaogang Gao, Soroosh Sorooshian

Research output: Contribution to journalArticle

142 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1605-1618
Number of pages14
JournalWater Resources Research
Volume35
Issue number5
DOIs
StatePublished - Jan 1 1999

ASJC Scopus subject areas

  • Water Science and Technology

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