Improvements on Calibration and Image Reconstruction of SMOS for Salinity Retrievals in Coastal Regions

González-Gambau, Verónica; Olmedo, Estrella; Martínez, Justino; Turiel, Antonio; Duran, Israel

doi:10.1109/jstars.2017.2685690

Cited by 18 publications

(22 citation statements)

References 34 publications

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“…Future improvements of this SMOS SSS product are aimed at reducing the error of the salinity retrievals at high latitudes. Enhanced image reconstruction techniques as the one introduced in [48] have been assessed in [49,50] reaching promising results. The decrease of the error in the salinity retrieval would allow generating SMOS SSS maps with smaller correlation radii.…”

Section: Discussionmentioning

confidence: 99%

“…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).…”

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confidence: 91%

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

et al. 2018

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This paper aims to present and assess the quality of seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans ( 50 ∘ N– 90 ∘ N). The SMOS SSS maps presented in this work are an improved version of the preliminary three-year dataset generated and freely distributed by the Barcelona Expert Center. In this new version, a time-dependent bias correction has been applied to mitigate the seasonal bias that affected the previous SSS maps. An extensive database of in situ data (Argo floats and thermosalinograph measurements) has been used for assessing the accuracy of this product. The standard deviation of the difference between the new SMOS SSS maps and Argo SSS ranges from 0.25 and 0.35. The major features of the inter-annual SSS variations observed by the thermosalinographs are also captured by the SMOS SSS maps. However, the validation in some regions of the Arctic Ocean has not been feasible because of the lack of in situ data. In those regions, qualitative comparisons with SSS provided by models and the remotely sensed SSS provided by Aquarius and SMAP have been performed. Despite the differences between SMOS and SMAP, both datasets show consistent SSS variations with respect to the model and the river discharge in situ data, but present a larger dynamic range than that of the model. This result suggests that, in those regions, the use of the remotely sensed SSS may help to improve the models.

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Section: Discussionmentioning

confidence: 99%

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confidence: 91%

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

et al. 2018

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“…The Eastern Mediterranean region is strongly affected by RFI and the SMOS SSS retrieval there is even more challenging. Recent calibration and image reconstruction techniques [34,47,48] are providing promising results in these more degraded regions. .…”

Section: Comparison With Argo Floatsmentioning

confidence: 99%

Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis

et al. 2018

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A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data Interpolating Empirical Orthogonal Functions) and multifractal fusion has been used to obtain Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) fields over the North Atlantic Ocean and the Mediterranean Sea. The debiased non-Bayesian retrieval mitigates the systematic errors produced by the contamination of the land over the sea. In addition, this retrieval improves the coverage by means of multiyear statistical filtering criteria. This methodology allows obtaining SMOS SSS fields in the Mediterranean Sea. However, the resulting SSS suffers from a seasonal (and other time-dependent) bias. This time-dependent bias has been characterized by means of specific Empirical Orthogonal Functions (EOFs). Finally, high resolution Sea Surface Temperature (OSTIA SST) maps have been used for improving the spatial and temporal resolution of the SMOS SSS maps. The presented methodology practically reduces the error of the SMOS SSS in the Mediterranean Sea by half. As a result, the SSS dynamics described by the new SMOS maps in the Algerian Basin and the Balearic Front agrees with the one described by in situ SSS, and the mesoscale structures described by SMOS in the Alboran Sea and in the Gulf of Lion coincide with the ones described by the high resolution remotely-sensed SST images (AVHRR).

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“…The image reconstruction methodology outlined in the previous sections is fully implemented in the MIRAS Testing Software [19]. This tool is being systematically used for scientific studies as a flexible alternative to the nominal SMOS Level 1 data products and with successful results, as reported for example in González-Gambau et al [21].…”

Section: Resultsmentioning

confidence: 99%

Wide Field of View Microwave Interferometric Radiometer Imaging

et al. 2019

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In microwave interferometric radiometers with a large field of view, as for example the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) onboard the Soil Moisture and Ocean Salinity (SMOS) satellite, one of the major causes of reconstruction error is the contribution to the visibility of the brightness temperature outside the fundamental period, defined on the basis of reciprocal grids. A mitigation method consisting of estimating this contribution through the application of a brightness temperature model outside the fundamental period is proposed. The main advantage is that it does not require any a posteriori addition of artificial scenes to the reconstructed image. Additionally, a method to avoid the sophisticated matrix regularization and inversion techniques usually applied in microwave interferometry is presented. Image reconstruction algorithms are implemented on a minimum grid size in order to maximize their numerical efficiency. An improved method to apply an apodization window to the reconstructed image for reducing Gibbs oscillations is also proposed. All procedures are generally described considering the single polarization case and successively implemented applying the MIRAS layout in both its single polarization and full polarimetric modes. Results show similar performance of the proposed algorithm with respect to the nominal one applied by SMOS. All algorithms are implemented in the MIRAS Testing Software and have been successfully used for scientific studies by other teams.

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Improvements on Calibration and Image Reconstruction of SMOS for Salinity Retrievals in Coastal Regions

Cited by 18 publications

References 34 publications

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

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

Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis

Wide Field of View Microwave Interferometric Radiometer Imaging

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