On The Effect of Atmospheric Emission upon the Passive Microwave Polarimetric Response of an Azimuthally Anisotropic Sea Surface

Marzano

2008

Sensors

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.

Section: Introductionmentioning

confidence: 99%

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

Marzano

2008

Sensors

Simulating brightness temperatures at SSM/I channels in the Mediterranean area

IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477)

An accurate retrieval of atmospheric and sea surface parameters from spaceborne microwave radiometers requires to validate the radiative forward model capable to simulate the brightness temperatures for known atmospheric and surface conditions. Matching the forward model and the retrieval algorithm to the climatological condition of the area of interest is also essential to obtain better retrieval accuracy. This work focuses on the Mediterranean Sea and aims to set up a consistent and accurate forward model using different sources of geophysical quantities and a large set of SSM/I data for validation purposes

A model function approach to generate a large set of brightness temperature simulations over the Mediterranean Sea

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings

Marzano³

A reliable estimate of atmospheric and sea surface parameters from satellite microwave radiometric measurements requires the availability of a large dataset of simulated brightness temperatures to train the retrieval algorithms. The validity of the radiative forward model adopted to generate the synthetic brightness temperatures, for known atmospheric and surface conditions, represents a crucial point of any retrieval method. In this work, we propose an approach based on microwave sea emissivity and scattering model functions, derived from the two-scale sea surface model and on a radiative transfer scheme in a non-scattering atmosphere. For the latter, we investigate about the possibility to adopt a simple cloud model derived from the outputs of a simulation of a stratiform event occurred in the Mediterranean basin. The simulation has been carried out by using a detailed microphysical time-evolving cloud model