Remote sensing of surface ocean PH exploiting sea surface salinity satellite observations

ESA SMOS satellite [1] has been providing first-ever Sea Surface Salinity (SSS) measurements from space for over a decade now. Until recently, inherent algorithm limitations or external interferences hampered a reliable provision of satellite SSS data in semi-enclosed basin such as the Mediterranean Sea. This has been however overcome through different strategies in the retrieval scheme and data filtering approach [2, 3]. This recent capability has been in turn used to infer the spatial and temporal distribution of Total Alkalinity (TA - a crucial parameter of the marine carbonate system) in the Mediterranean, exploiting basin-specific direct relationships existing between salinity and TA.Preliminary results [4] focused on the differences existing in several parameterizations [e.g, 5] relating these two variables, and how they vary over a seasonal to interannual timescale.Currently, to verify the consistency and accuracy of the derived products, these data are being validated against a proper ensemble of in-situ, climatology and model outputs within the Mediterranean basin. An error propagation exercise is also being planned to assess how uncertainties in the satellite data would translate into the final products accuracy.The resulting preliminary estimates of Alkalinity in the Mediterranean Sea will be linked to the overall carbonate system in the broader context of Ocean Acidification assessment and marine carbon cycle.References:[1] J. Font et al., "SMOS: The Challenging Sea Surface Salinity Measurement From Space," in Proceedings of the IEEE, vol. 98, no. 5, pp. 649-665, May 2010. doi: 10.1109/JPROC.2009.2033096[2] Olmedo, E., J. Martinez, A. Turiel, J. Ballabrera-Poy, and M. Portabella,&#160; &#8220;Debiased non-Bayesian retrieval: A novel approach to SMOS Sea Surface Salinity&#8221;. Remote Sensing of Environment 193, 103-126 (2017).[3] Alvera-Azc&#225;rate, A., A. Barth, G. Parard, J.-M. Beckers, Analysis of SMOS sea surface salinity data using DINEOF, In Remote Sensing of Environment, Volume 180, 2016, Pages 137-145, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2016.02.044.[4] Sabia, R., E. Olmedo, G. Cossarini, A. Turiel, A. Alvera-Azc&#225;rate, J. Martinez, D. Fern&#225;ndez-Prieto, Satellite-driven preliminary estimates of Total Alkalinity in the Mediterranean basin, Geophysical Research Abstracts, Vol. 21, EGU2019-17605, EGU General Assembly 2019, Vienna, Austria, April 7-12, 2019.[5] Cossarini, G., Lazzari, P., and Solidoro, C.: Spatiotemporal variability of alkalinity in the Mediterranean Sea, Biogeosciences, 12, 1647-1658, https://doi.org/10.5194/bg-12-1647-2015, 2015.&#160;&#160;

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A new algorithm for microwave radiometer remote sensing of sea surface salinity without influence of wind

International Journal of Remote Sensing ◽

10.1080/01431160802227313 ◽

2008 ◽

Vol 29 (23) ◽

pp. 6789-6800

Author(s):

X. B. Yin ◽

Z. Z. Wang ◽

Y. G. Liu ◽

Y. C. Cheng

Keyword(s):

Remote Sensing ◽

Microwave Radiometer ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity

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Preparing the potential and challenge of remote sensing-based sea surface salinity estimation: the CoSMOS airborne campaign

10.1117/12.804940 ◽

2008 ◽

Cited By ~ 3

Author(s):

Nicolas Reul ◽

Joseph Tenerelli ◽

Bertrand Chapron ◽

Sebastien Guimbard ◽

Stephane-S. Picard ◽

...

Keyword(s):

Remote Sensing ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity

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Aquarius/SAC-D: An international remote sensing mission to measure Sea surface salinity

2012 IEEE Aerospace Conference ◽

10.1109/aero.2012.6187021 ◽

2012 ◽

Author(s):

A. Sen

Keyword(s):

Remote Sensing ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity

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Removing the impact of wind direction on remote sensing of sea surface salinity

Chinese Science Bulletin ◽

10.1007/s11434-006-1368-x ◽

2006 ◽

Vol 51 (11) ◽

pp. 1368-1373 ◽

Cited By ~ 3

Author(s):

Xiaobin Yin ◽

Yuguang Liu ◽

Hande Zhang

Keyword(s):

Remote Sensing ◽

Wind Direction ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

The Impact

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Technical Note: Remote sensing of sea surface salinity using the propagation of low-frequency navigation signals

Ocean Science Discussions ◽

10.5194/osd-11-2971-2014 ◽

2014 ◽

Vol 11 (6) ◽

pp. 2971-2978

Author(s):

I. Astin ◽

Y. Feng

Keyword(s):

Remote Sensing ◽

Low Frequency ◽

Potential Method ◽

Propagation Delay ◽

Technical Note ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

Delay Variations ◽

Navigation Signals

Abstract. This paper introduces a potential method for the remote sensing of sea surface salinity (SSS) using measured propagation delay of low-frequency Loran-C signals transmitted over an all-seawater path between the Sylt station in Germany and an integrated Loran-C/GPS receiver located in Harwich, UK. The overall delay variations in Loran-C surface waves along the path may be explained by changes in sea surface properties (especially the temperature and salinity), as well as atmospheric dynamics that determine the refractive index of the atmosphere. After removing the atmospheric and sea surface temperature (SST) effects, the residual delay revealed a temporal variation similar to that of SSS data obtained by the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite.

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Sea surface salinity subfootprint variability from a global high-resolution model

10.31223/x5kp77 ◽

2021 ◽

Author(s):

Frederick Bingham ◽

Susannah Brodnitz ◽

Severine Fournier ◽

Karly Ulfsax ◽

Akiko Hayashi ◽

...

Keyword(s):

High Resolution ◽

Satellite Observations ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

Representation Error ◽

Resolution Model ◽

One Year ◽

The One ◽

Model Year

Subfootprint variability (SFV) is variability at a spatial scale smaller than the footprint of a sat-ellite, and cannot be resolved by satellite observations. It is important to quantify and understand as it contributes to the error budget for satellite data. The purpose of this study is to estimate the SFV for sea surface salinity (SSS) satellite observations. This is done using a high-resolution (1/48°) numerical model, the MITgcm, from which one year of output has recently become availa-ble. SFV, defined as the weighted standard deviation of SSS within the satellite footprint, was computed from the model for a 2°X2° grid of points for the one model year. We present maps of SFV for 40 and 100 km footprint size, display histograms of its distribution for a range of foot-print sizes and quantify its seasonality. At 100 km (40 km) footprint size, SFV has a mode of 0.06 (0.04). It is found to vary strongly by location and season. It has larger values in western bound-ary and eastern equatorial regions, and a few other areas. SFV has strong variability throughout the year, with generally largest values in the fall season. We also quantify representation error, the degree of mismatch between random samples within a footprint and the footprint average. Our estimates of SFV and representation error can be used in understanding errors in satellite obser-vation of SSS.

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Intercomparison of Salinity Products in the Beaufort Gyre and Arctic Ocean

Remote Sensing ◽

10.3390/rs14010071 ◽

2021 ◽

Vol 14 (1) ◽

pp. 71

Author(s):

Sarah B. Hall ◽

Bulusu Subrahmanyam ◽

James H. Morison

Keyword(s):

Soil Moisture ◽

Arctic Ocean ◽

Satellite Observations ◽

Sea Surface ◽

Sea Surface Salinity ◽

The Arctic ◽

Surface Salinity ◽

Beaufort Gyre ◽

The Arctic Ocean

Salinity is the primary determinant of the Arctic Ocean’s density structure. Freshwater accumulation and distribution in the Arctic Ocean have varied significantly in recent decades and certainly in the Beaufort Gyre (BG). In this study, we analyze salinity variations in the BG region between 2012 and 2017. We use in situ salinity observations from the Seasonal Ice Zone Reconnaissance Surveys (SIZRS), CTD casts from the Beaufort Gyre Exploration Project (BGP), and the EN4 data to validate and compare with satellite observations from Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Aquarius Optimally Interpolated Sea Surface Salinity (OISSS), and Arctic Ocean models: ECCO, MIZMAS, HYCOM, ORAS5, and GLORYS12. Overall, satellite observations are restricted to ice-free regions in the BG area, and models tend to overestimate sea surface salinity (SSS). Freshwater Content (FWC), an important component of the BG, is computed for EN4 and most models. ORAS5 provides the strongest positive SSS correlation coefficient (0.612) and lowest bias to in situ observations compared to the other products. ORAS5 subsurface salinity and FWC compare well with the EN4 data. Discrepancies between models and SIZRS data are highest in GLORYS12 and ECCO. These comparisons identify dissimilarities between salinity products and extend challenges to observations applicable to other areas of the Arctic Ocean.

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