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Sensors 2008, 8(12), 7850-7865;

Coupling a Neural Network-Based forward Model and a Bayesian Inversion Approach to Retrieve Wind Field from Spaceborne Polarimetric Radiometers

Department of Electronic Engineering, Sapienza University of Rome, via Eudossiana 18, 00184 Rome, Italy
Author to whom correspondence should be addressed.
Received: 2 June 2008 / Revised: 29 October 2008 / Accepted: 21 November 2008 / Published: 3 December 2008
(This article belongs to the Special Issue Ocean Remote Sensing)
Full-Text   |   PDF [518 KB, uploaded 21 June 2014]


A simulation study to assess the potentiality of sea surface wind vector estimation based on the approximation of the forward model through Neural Networks and on the Bayesian theory of parameter estimation is presented. A polarimetric microwave radiometer has been considered and its observations have been simulated by means of the two scale model. To perform the simulations, the atmospheric and surface parameters have been derived from ECMWF analysis fields. To retrieve wind speed, Minimum Variance (MV) and Maximum Posterior Probability (MAP) criteria have been used while, for wind direction, a Maximum Likelihood (ML) criterion has been exploited. To minimize the cost function of MAP and ML, conventional Gradient Descent method, as well as Simulated Annealing optimization technique, have been employed. Results have shown that the standard deviation of the wind speed retrieval error is approximately 1.1 m/s for the best estimator. As for the wind direction, the standard deviation of the estimation error is less than 13° for wind speeds larger than 6 m/s. For lower wind velocities, the wind direction signal is too weak to ensure reliable retrievals. A method to deal with the non-uniqueness of the wind direction solution has been also developed. A test on a case study has yielded encouraging results. View Full-Text
Keywords: Microwave radiometry; polarimetry; sea surface winds Microwave radiometry; polarimetry; sea surface winds
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Pulvirenti, L.; Pierdicca, N.; Marzano, F.S. Coupling a Neural Network-Based forward Model and a Bayesian Inversion Approach to Retrieve Wind Field from Spaceborne Polarimetric Radiometers. Sensors 2008, 8, 7850-7865.

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