PROJECT TITLE :
Nodal sampling: a new image reconstruction Algorithm for smos. - 2016
Soil moisture and ocean salinity (SMOS) brightness temperature (TB) pictures and calibrated visibilities are related by the so-known as G-matrix. Because of the unfinished sampling at some spatial frequencies, sharp transitions in the TB scenes generate a Gibbs-like contamination ringing and unfold sidelobes. In this SMOS image reconstruction strategy, a Blackman window is applied to the Fourier elements of the TBs to diminish the amplitude of artifacts like ripples, as well as alternative Gibbs-like effects. In this paper, a completely unique image reconstruction algorithm centered on the reduction of Gibbs-like contamination in TB images is proposed. It's based on sampling the TB images at the nodal points, that's, at those points at which the oscillating interference causes the minimum distortion to the geophysical signal. Results show a important reduction of ripples and sidelobes in strongly radio-frequency interference contaminated images. This system has been totally validated using snapshots over the ocean, by comparing TBs reconstructed in the quality means or using the nodal sampling (NS) with modeled TBs. Tests have revealed that the standard deviation of the distinction between the measurement and the model is reduced around one K over clean and stable zones when using NS technique with respect to the SMOS image reconstruction baseline. The reduction is approximately 0.seven K when considering the global ocean. This represents a crucial improvement in TB quality, that will translate in an enhancement of the retrieved geophysical parameters, particularly the sea surface salinity.
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