Nodal Sampling: A New Image Reconstruction Algorithm for SMOS

Abstract

1SMOS brightness temperature images and calibrated visibilities are related by the so-called G-matrix. Due to 2 the incomplete sampling at some spatial frequencies, sharp transitions in the brightness temperature scenes generate 3 a Gibbs-like contamination ringing and spread sidelobes. In the current SMOS image reconstruction strategy, a Blackman window is applied to the Fourier components of the brightness temperatures to diminish the amplitude of 5 artifacts such as ripples, and other Gibbs-like effects. In this work, a novel image reconstruction algorithm focused 6 on the reduction of Gibbs-like contamination in brightness temperature images is proposed. It is based on sampling 7 the brightness temperature images at the nodal points, that is, at those points at which the oscillating interference 14 This represents a crucial improvement in brightness temperature quality, which will translate in an enhancement of 15 the retrieved geophysical parameters, especially the sea surface salinity. 16 Index Terms 17

Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions

“…In other words, the generation of SMOS SSS maps with smaller correlation radii and the same level of noise as in the case of the global SSS maps requires SMOS SSS retrievals less noisy. In this sense, improvements at T B level as the ones introduced in [48] and assessed at salinity level in [49,50] are providing promising results in terms of noise reduction in the SSS retrievals. The application of this technique will probably help to retrieve more accurate SSS in those regions and therefore to generate SMOS SSS maps with smaller correlation radii (more appropriate to capture the dynamics of this region).…”

In Situ and Satellite Sea Surface Salinity in the Algerian Basin Observed through ABACUS Glider Measurements and BEC SMOS Regional Products

“…Therefore, since the large scale salinity patterns are well described, future releases of these products should now be focused on using different interpolation/fusion schemes to improve the effective spatial and temporal resolutions of the SMOS SSS products. Since systematic negative biases still appear in the eastern Mediterranean Sea, a better mitigation of RFI contamination will then be required in these regions, for example by improving the quality of the brightness temperature with methodologies [54,55] that have already been proven to improve salinity retrieval in coastal areas [56]. Furthermore, all the ABACUS high resolution in situ data used in this study come from three glider surveys carried out in the AB during fall season (i.e., September to December).…”

Enhancing SMOS brightness temperatures over the ocean using the nodal sampling image reconstruction technique

“…In particular, the search condition for nodal points has been relaxed by allowing some nodal points to cross the boundaries defined by the original pixel. Once a first guess of the nodal points has been estimated by finding the local minima of the Laplacian in the oversampled image, an iterative refinement of the selection is performed in order to reduce the Laplacian in the original grid (see González-Gambau et al, 2015 for more details). The new algorithm (hereafter referred to as NSv2) proceeds as follows (see scheme in (1) The original TB image is spatially oversampled using an oversampling factor β = 9.…”

“…The nodal sampling algorithm was recently developed by (González-Gambau, Turiel, Martínez, Olmedo, & Corbella, 2014;González-Gambau et al, 2015). This method is based on sampling TB images at the nodal points, i.e., those points at which the oscillating interference causes the minimum distortion of the geophysical signal.…”