Avances en Estimación de Tasa de Precipitación desde Plataformas Satelitales usando Redes Neuronales Artificiales (Advances on Rain Rate Retrieval from Satellite Platforms using Artificial Neural Networks)

Erith Alexander Muñoz (erith7@gmail.com)1, Francesco Di Paola (francesco.dipaola@imaa.cnr.it)2, Mario Lanfri (lamfri@conae.gov.ar)3

1Food and Agriculture Organization
2National Research Council
3Comisión Nacional de Actividades Espaciales

This paper appears in: Revista IEEE América Latina

Publication Date: Oct. 2015
Volume: 13,   Issue: 10 
ISSN: 1548-0992

In the last two decades, great advances have been related with the development of rain rate retrieval algorithms using artificial neural networks, in order to exploit satellite data capabilities. The enhancement of computing processing capacity available from modern computers has impulsed a long number of researches aimed to generate more accurate and faster algorithms. This work deals with how the implementation of new trends in artificial neural networks and the spectral resolution improvement of spaceborne sensors have influenced in the design of retrieval algorithms to estimate rain rate from satellites using artificial neural networks. Recent results have shown an important increasing in accuracy and technical feasibility of implementation, however, the feasibility to use artificial neural networks to estimate rain rate in real time, using remote sensing techniques, is a research issue yet.

Index Terms:
Remote Sensing,Rain Rate Retrieval,Artificial Neural Network   

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