A method for correcting regional bias in SMOS global salinity products

Tong, Xiaolin; Wang, Zhenzhan; Li, Qingxia

doi:10.1007/s00343-015-4196-5

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Various studies (e.g., Banks et al, ; Köhler et al, ; Tong et al, ) showed that satellite‐retrieved SSS is corrupted by systematic errors coming from ice and land contamination (Font et al, ; Gabarro et al, ; Oliva et al, ), radio frequency interference (RFI; Oliva et al, ), seasonal biases, or latitudinal biases mainly due to thermal drift of the instrument (e.g., Boutin et al, ; Gourrion et al, ; Reul et al, ). Those biases are difficult to characterize and several institutions provide satellite‐retrieved SSS products based on different correction methods (e.g., Kolodziejczyk et al, ).…”

Section: Methodsmentioning

confidence: 99%

Mechanisms of Mixed‐Layer Salinity Seasonal Variability in the Indian Ocean

Kohler¹,

Serra²,

Bryan

et al. 2018

JGR Oceans

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Based on a joint analysis of an ensemble mean of satellite sea surface salinity retrievals and the output of a high‐resolution numerical ocean circulation simulation, physical processes are identified that control seasonal variations of mixed‐layer salinity (MLS) in the Indian Ocean, a basin where salinity changes dominate changes in density. In the northern and near‐equatorial Indian Ocean, annual salinity changes are mainly driven by respective changes of the horizontal advection. South of the equatorial region, between 45°E and 90°E, where evaporation minus precipitation has a strong seasonal cycle, surface freshwater fluxes control the seasonal MLS changes. The influence of entrainment on the salinity variance is enhanced in mid‐ocean upwelling regions but remains small. The model and observational results reveal that vertical diffusion plays a major role in precipitation and river runoff dominated regions balancing the surface freshwater flux. Vertical diffusion is important as well in regions where the advection of low salinity leads to strong gradients across the mixed‐layer base. There, vertical diffusion explains a large percentage of annual MLS variance. The simulation further reveals that (1) high‐frequency small‐scale eddy processes primarily determine the salinity tendency in coastal regions (in particular in the Bay of Bengal) and (2) shear horizontal advection, brought about by changes in the vertical structure of the mixed layer, acts against mean horizontal advection in the equatorial salinity frontal regions. Observing those latter features with the existing observational components remains a future challenge.

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Section: Methodsmentioning

confidence: 99%

Mechanisms of Mixed‐Layer Salinity Seasonal Variability in the Indian Ocean

Kohler¹,

Serra²,

Bryan

et al. 2018

JGR Oceans

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How well do we know ocean salinity and its changes?

Stammer

Martins

Kohler

et al. 2021

Progress in Oceanography

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Ocean color as a proxy to predict sea surface salinity in the Banda Sea

Wouthuyzen

Kusmanto

Fadli

et al. 2020

IOP Conf. Ser.: Earth Environ. Sci.

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Salinity is an important ocean parameter that greatly influences physical, chemical, and biological ocean properties and processes. Salinity combines with sea temperature and chlorophyll-a (Chl-a) that mostly sourced from remote sensing-based measurements can reveal ocean quality and supports fisheries. However, the satellite-derived Sea Surface Salinity (SSS) dataset (∼ 9 years) is not as temporally adequate as SST and Chl-a datasets (∼3 decades) and thus, preventing a comprehensively spatio-temporal analysis of this water quality aspect. Since (SSS) can be approximated using satellite-derived ocean color products having the similar temporal length of datasets to the available SST and Chl-a datasets, predicted SSS can be produced from these ocean color products to fill the gap of the existing SSS dataset. This study aims to estimate the SSS from ocean color products of Aqua-MODIS satellite with a spatial and temporal resolution of 4 km and 8-daily by developing an empirical model. The ocean color data used were remote sensing reflectance (Rrs) of blue, green and red wavelengths (412, 433, 469, 488, 531, 547, 555, 645, 667 and 678 nm). The absorption coefficients due to detritus material non-algae, Gelbstof and CDOM (ADG) at 443 nm and the absorption coefficient due to phytoplankton (APH) at 443 nm data were also used. The Banda Sea was chosen due to its large-scale upwelling system (∼300 km × 300 km) that providing an important ocean process related to fishery and the availability of in-situ salinity measurements (i.e. CTD casts from series of Research Vessel (R/V) Baruna Jaya III, VII and VIII cruises and Argo floats), which a part of these datasets will be used to validate predicted SSS. Results showed that of all ocean color parameters tested, ADG at 443 nm was strongly correlated with in-situ SSS through the polynomial order 5 regression equation with a high R2 of 0.94 and a low RMES value of 0.101 PSU. Although this empirical model has high accuracy, but based on RMSE analysis results from various locations within and outside the Banda Sea that influenced by the Pacific and the Indian ocean water masses indicates that this model actually good to predict in-situ SSS only for a narrow range SSS of 33.4-34.5 PSU. Nevertheless, this model has a limitation, it is still can be used for predicting and mapping the SSS for Banda Sea as well as for most of the Indonesian waters. The long-term meteorological SSS map (2003-2017) derived by this model together with the SST and Chl-a maps can show clearly the upwelling phenomena of the Banda Sea, which occurred during the southeast monsoon (June-July-August, JJA). This study proves that ocean color data from Aqua-MODIS satellite can be applied to estimate and to map the SSS for most of the Indonesian waters, but validations for this model is still needed

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A method for correcting regional bias in SMOS global salinity products

Cited by 3 publications

References 27 publications

Mechanisms of Mixed‐Layer Salinity Seasonal Variability in the Indian Ocean

Mechanisms of Mixed‐Layer Salinity Seasonal Variability in the Indian Ocean

How well do we know ocean salinity and its changes?

Ocean color as a proxy to predict sea surface salinity in the Banda Sea

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