Toward an Enhanced SMOS Level-2 Ocean Salinity Product

Olmedo, Estrella; González-Gambau, Verónica; Turiel, Antonio; Guimbard, Sébastien; González‐Haro, Cristina; Martínez, Justino; Gabarró, Carolina; Portabella, Marcos; Arias, Manuel; Sabia, Roberto; Oliva, Roger; Corbella, Ignasi

doi:10.1109/jstars.2020.3034432

Cited by 5 publications

(8 citation statements)

References 24 publications

(31 reference statements)

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“…The original approach leads in the Black Sea to SMOS SSS fields with seasonal biases much larger than the expected ones in global ocean (see the biases of Figure 7 in comparison with the ones reported in (Olmedo et al, 2020(Olmedo et al, , 2021a). The cause of the seasonal biases is not completely understood although some authors associate the origin to SST-depending errors in the salinity retrieval (Le Vine et al, 2015).…”

Section: Bias Characterization Depending On the Sstmentioning

confidence: 81%

“…We use the debiased non-Bayesian retrieval (Olmedo et al, 2017) because: i) this approach properly mitigates the systematic biases coming from the residual LSC and permantent RFI sources; and ii) it improves the coverage of the good quality SSS retrievals in comparison with the standard (Bayesian) retrieval approach (Olmedo et al, 2020). The debiased non-Bayesian (DNB) approach (Olmedo et al, 2017(Olmedo et al, , 2021a has been fine-tuned for retrieving SSS in the Black Sea.…”

Section: Sss Retrieval Approachmentioning

confidence: 99%

“…We use triple collocation to estimate the uncertainty of the different EO4SIBS SMOS SSS products. For this, we apply the Correlated Triple Collocation (CTC) method, which allows analysing three data sets that resolve similar spatial scales from which two of them present correlated errors (González-Gambau et al, 2020). The triplets used in this analysis are those presented in Table 2.…”

Section: Estimated Sss Uncertaintymentioning

confidence: 99%

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New SMOS SSS maps in the framework of the Earth Observation data For Science and Innovation in the Black Sea

Olmedo

González-Gambau

Turiel

et al. 2021

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Abstract. In the framework of the European Space Agency (ESA) regional initiative called Earth Observation data For Science and Innovation in the Black Sea (EO4SIBS), a new dedicated Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) product is generated for the Black Sea for the years 2011–2020. Three SMOS SSS fields are retrieved and distributed: a level 2 product consisting of binned SSS in daily maps at 0.25° × 0.25° spatial resolution grid by considering ascending ((Olmedo et al., 2021b), https://doi.org/10.20350/digitalCSIC/13993) and descending ((Olmedo et al., 2021c), https://doi.org/10.20350/digitalCSIC/13995) satellite overpass directions separately; a level 3 product ((Olmedo et al., 2021d), https://doi.org/10.20350/digitalCSIC/13996) consisting of binned SSS in 9-day maps at 0.25° × 0.25° grid by combining as cending and descending satellite overpass directions; and a level 4 product ((Olmedo et al., 2021e), https://doi.org/10.20350/digitalCSIC/13997) consisting of daily maps at 0.05 × 0.0505° that are computed by merging the level 3 SSS product with Sea Surface Temperature (SST) maps. The generation of SMOS SSS fields in the Black Sea requires the use of enhanced data processing algorithms for improving the Brightness Temperatures in the region since this basin is typically strongly affected by Radio Frequency Interference (RFI) sources which hinders the retrieval of salinity. Here, we describe the algorithms introduced to improve the quality of the salinity retrieval in this basin. The validation of the EO4SIBS SMOS SSS products is performed by: i) comparing the EO4SIBS SMOS SSS products with near-to-surface salinity measurements provided by in situ measurements; ii) assessing the geophysical consistency of the products by comparing them with a model and other satellite salinity measurements; iii) computing maps of SSS errors by using Correlated Triple Collocation analysis. The accuracy of the EO4SIBS SMOS SSS products depend on the time period and on the product level. The accuracy in the period 2016–2020 is better than in 2011–2015 and it is as follows for the different products: i) Level 2 ascending: 1.85 / 1.50 psu (in 2011–2015 / 2016–2020); Level 2 descending: 2.95 1.95 psu; ii) Level 3: 0.7 / 0.5 psu; and iii) Level 4: 0.6 / 0.4 psu.

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Section: Bias Characterization Depending On the Sstmentioning

confidence: 81%

Section: Sss Retrieval Approachmentioning

confidence: 99%

Section: Estimated Sss Uncertaintymentioning

confidence: 99%

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New SMOS SSS maps in the framework of the Earth Observation data For Science and Innovation in the Black Sea

Olmedo

González-Gambau

Turiel

et al. 2021

Preprint

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“…At the SSS level, global comparisons (60 • S-60 • N) with in situ data provided by Argo floats show a significant improvement in NS SSS wit a Root Mean Square (RMS) equal to 0.81 with respect to the SSS from the current operational TBs (RMS = 1.09). Global comparisons to data from ship tracks show also a slight improvement in terms of RMS when applying NS (RMS = 1.82 and a correlation coefficient of 0.61) with respect to the SSS from the current operational TBs (RMS = 1.87 and correlation coefficient R = 0.55).It must be pointed out that the spatial coverage of SSS retrievals from the corrected NS TBs increases by 20% on average with respect to the SSS from the current operational TBs, particularly over semi-enclosed seas and strongly RFI-contaminated regions [20].…”

Section: Nodal Sampling: Reduction Of Rfi Contamination In Smos Imagesmentioning

confidence: 90%

Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land

et al. 2020

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The error characterization of satellite observations is crucial for blending observations from multiple platforms into a unique dataset and for assimilating them into numerical weather prediction models. In the last years, the triple collocation (TC) technique has been widely used to assess the quality of many geophysical variables acquired with different instruments and at different scales. This paper presents a new formulation of the triple collocation (Correlated Triple Collocation (CTC)) for the case of three datasets that resolve similar spatial scales, with two of them being error-correlated datasets. Besides, the formulation is designed to ensure fast convergence of the error estimators. This approach is of special interest in cases such that finding more than three datasets with uncorrelated errors is not possible and the amount of data is limited. First, a synthetic experiment has been carried out to assess the performance of CTC formulation. As an example of application, the error characterization of three collocated L-band brightness temperature (TB) measurements over land has been performed. Two of the datasets come from ESA (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) mission: one is the reconstructed TB from the operational L1B v620 product, and the other is the reconstructed TB from the operational L1B v620 product resulting from application of an RFI (Radio Frequency Interference) mitigation technique, the nodal sampling (NS). The third is an independent dataset, the TB acquired by a NASA (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) radiometer. Our analysis shows that the application of NS leads to TB error reduction with respect to the current version of SMOS TB in 80% of the points in the global map, with an average reduction of approximately 1 K over RFI-free regions and approximately 1.45 K over strongly RFI-contaminated areas.

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“…When we applied the original DNB retrieval to the Baltic Sea, we observed that seasonal variations were much higher than in the global ocean (Olmedo et al, 2020) and a non-expected spatial gradient appeared close to the coasts. These effects were evidenced when computing the monthly mean difference between SMOS SSS and CMEMS Baltic reanalysis salinity field (Figure 4).…”

Section: Definition Of a Smos-based Climatologymentioning

confidence: 93%

First SMOS Sea Surface Salinity dedicated products over the Baltic Sea

González-Gambau

Olmedo

Turiel

et al. 2022

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Abstract. This paper presents the first Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) dedicated products over the Baltic Sea. The SSS retrieval from L-band brightess temperature (TB) measurements over this basin is really challenging due to important technical issues, such as the land-sea and ice-sea contamination, the high contamination by Radio-Frequency Interferences (RFI) sources, the low sensitivity of L-band TB at SSS changes in cold waters and the poor characterization of dielectric constant models for the low SSS and SST ranges in the basin. For these reasons, exploratory research in the algorithms used from the level 0 up to level 4 has been required to develop these dedicated products. This work has been performed in the framework of the European Space Agency regional initiative Baltic+ Salinity Dynamics. Two Baltic+ SSS products have been generated for the period 2011–2019 and are freely distributed: the Level 3 (L3) product (daily generated 9-day maps in a 0.25° grid, https://doi.org/10.20350/digitalCSIC/13859) (González-Gambau et al., 2021a) and the Level 4 (L4) product (daily maps in a 0.05° grid, https://doi.org/10.20350/digitalCSIC/13860) (González-Gambau et al., 2021b)), that are computed by applying multifractal fusion to L3 SSS with Sea Surface Temperature (SST) maps. The accuracy of L3 SSS products is typically around 0.7–0.8 psu. The L4 product has an improved spatio-temporal resolution with respect to the L3 and the accuracy is typically around 0.4 psu. Regions with the highest errors and limited coverage are located in Arkona and Bornholm basins and Gulfs of Finland and Riga. The impact assessment of Baltic+ SSS products has shown that they can help in the understanding of salinity dynamics in the basin. They complement the temporally and spatially very sparse in situ measurements, covering data gaps in the region and they can also be useful for the validation of numerical models, particularly in areas where in situ data are very sparse.

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Toward an Enhanced SMOS Level-2 Ocean Salinity Product

Cited by 5 publications

References 24 publications

New SMOS SSS maps in the framework of the Earth Observation data For Science and Innovation in the Black Sea

New SMOS SSS maps in the framework of the Earth Observation data For Science and Innovation in the Black Sea

Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land

First SMOS Sea Surface Salinity dedicated products over the Baltic Sea

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