Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency’s (ESA’s) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration’s (NASA’s) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from the Argo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS. We also identify the impact of the corrections for atmospheric effects recently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.

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Correcting sea surface temperature spurious effects in salinity retrieved from spaceborne L-band Radiometer measurements

10.5194/egusphere-egu21-655 ◽

2021 ◽

Author(s):

Jacqueline Boutin ◽

Jean-Luc Vergely ◽

Emmanuel Dinnat ◽

Philippe Waldteufel ◽

Francesco D'Amico ◽

...

Keyword(s):

Dielectric Constant ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Dielectric Constants ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

L Band ◽

Band Radiometer

We derived a new parametrisation for the dielectric constant of the ocean (Boutin et al. 2020). Earlier studies have pointed out systematic differences between Sea Surface Salinity retrieved from L-band radiometric measurements and measured in situ, that depend on Sea Surface Temperature (SST). We investigate how to cope with these differences given existing physically based radiative transfer models. In order to study differences coming from seawater dielectric constant parametrization, we consider the model of Somaraju and Trumpf (2006) (ST) which is built on sound physical bases and close to a single relaxation term Debye equation. While ST model uses fewer empirically adjusted parameters than other dielectric constant models currently used in salinity retrievals, ST dielectric constants are found close to those obtained using the Meissner and Wentz (2012) (MW) model. The ST parametrization is then slightly modified in order to achieve a better fit with seawater dielectric constant inferred from SMOS data. Upgraded dielectric constant model is intermediate between KS and MW models. Systematic differences between SMOS and in situ salinity are reduced to less than +/-0.2 above 0&#176;C and within +/-0.05 between 7 and 28&#176;C. Aquarius salinity becomes closer to in situ salinity, and within +/-0.1. The order of magnitude of remaining differences is very similar to the one achieved with the Aquarius version 5 empirical adjustment of wind model SST dependency. The upgraded parametrization is recommended for use in processing the SMOS data.&#160;The rationale for this new parametrisation, results obtained with this new parametrisation in recent SMOS reprocessings and comparisons with other parametrisations will be discussed.Reference:Boutin, J.,et al. (2020), Correcting Sea Surface Temperature Spurious Effects in Salinity Retrieved From Spaceborne L-Band Radiometer Measurements, IEEE TGRSS, doi:10.1109/tgrs.2020.3030488.

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Comparison of Satellite-Derived Sea Surface Temperature and Sea Surface Salinity Gradients Using the Saildrone California/Baja and North Atlantic Gulf Stream Deployments

Remote Sensing ◽

10.3390/rs12111839 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1839 ◽

Cited By ~ 1

Author(s):

Jorge Vazquez-Cuervo ◽

Jose Gomez-Valdes ◽

Marouan Bouali

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

North Atlantic ◽

Gulf Stream ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

Temporal Sampling ◽

The North

Validation of satellite-based retrieval of ocean parameters like Sea Surface Temperature (SST) and Sea Surface Salinity (SSS) is commonly done via statistical comparison with in situ measurements. Because in situ observations derived from coastal/tropical moored buoys and Argo floats are only representatives of one specific geographical point, they cannot be used to measure spatial gradients of ocean parameters (i.e., two-dimensional vectors). In this study, we exploit the high temporal sampling of the unmanned surface vehicle (USV) Saildrone (i.e., one measurement per minute) and describe a methodology to compare the magnitude of SST and SSS gradients derived from satellite-based products with those captured by Saildrone. Using two Saildrone campaigns conducted in the California/Baja region in 2018 and in the North Atlantic Gulf Stream in 2019, we compare the magnitude of gradients derived from six different GHRSST Level 4 SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and two SSS (JPLSMAP, RSS40km) datasets. While results indicate strong consistency between Saildrone- and satellite-based observations of SST and SSS, this is not the case for derived gradients with correlations lower than 0.4 for SST and 0.1 for SSS products.

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Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast

Remote Sensing ◽

10.3390/rs11171964 ◽

2019 ◽

Vol 11 (17) ◽

pp. 1964 ◽

Cited By ~ 11

Author(s):

Jorge Vazquez-Cuervo ◽

Jose Gomez-Valdes ◽

Marouan Bouali ◽

Luis Miranda ◽

Tom Van der Stocken ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Current System ◽

California Current ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

California Current System ◽

The Impact ◽

Buoy Measurements

Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products by comparing with buoy measurements, do not allow for evaluating the impact of mesoscale-to-submesoscale variability. We present the validation of remotely sensed SST and SSS data against the unmanned surface vehicle (USV)—called Saildrone—measurements from the 60 day 2018 Baja California campaign. More specifically, biases and root mean square differences (RMSDs) were calculated between USV-derived SST and SSS values, and six satellite-derived SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and three SSS (JPLSMAP, RSS40, RSS70) products. Biases between the USV SST and OSTIA/CMC/DMI were approximately zero, while MUR showed a bias of 0.3 °C. The OSTIA showed the smallest RMSD of 0.39 °C, while DMI had the largest RMSD of 0.5 °C. An RMSD of 0.4 °C between Saildrone SST and the satellite-derived products could be explained by the diurnal and sub-daily variability in USV SST, which currently cannot be resolved by remote sensing measurements. SSS showed fresh biases of 0.1 PSU for JPLSMAP and 0.2 PSU and 0.3 PSU for RMSS40 and RSS70 respectively. SST and SSS showed peaks in coherence at 100 km, most likely associated with the variability of the California Current System.

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Towards an improved temporal stability of CCI+SSS time series

10.5194/egusphere-egu21-1231 ◽

2021 ◽

Author(s):

Xavier Perrot ◽

Jacqueline Boutin ◽

Jean Luc Vergely ◽

Frédéric Rouffi ◽

Adrien Martin ◽

...

Keyword(s):

Climate Change ◽

Time Series ◽

Dielectric Constant ◽

Soil Moisture ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

L Band ◽

Band Radiometer

This study is performed in the frame of the European Space Agency (ESA) Climate Change Initiative (CCI+) for Sea Surface Salinity (SSS), which aims at generating global SSS fields from all available satellite L-band radiometer measurements over the longest possible period with a great stability. By combining SSS from the Soil Moisture and Ocean Salinity, SMOS, Aquarius and the Soil Moisture Active Passive, SMAP missions, CCI+SSS fields (Boutin et al. 2020) are the only one to provide a 10 year time series of satellite salinity with such quality: global rms difference of weekly 25x25km2 CCI+SSS with respect to in situ Argo SSS of 0.17 pss, correlation coefficient of 0.97 (see https://pimep.ifremer.fr/diffusion/analyses/mdb-database/GO/cci-l4-esa-merged-oi-v2.31-7dr/argo/report/pimep-mdb-report_GO_cci-l4-esa-merged-oi-v2.31-7dr_argo_20201215.pdf). Nevertheless, we found that some systematic biases remained. In this presentation, we will show how they will be reduced in the next CCI+SSS version.The key satellite mission ensuring the longest time period, since 2010, at global scale, is SMOS. We implemented a re-processing of the whole SMOS dataset by changing some key points. Firstly we replace the Klein and Swift (1977) dielectric constant parametrization by the new Boutin et al. (2020) one. Secondly we change the reference dataset used to perform a vicarious calibration over the south east Pacific Ocean (the so-called Ocean Target Transformation), by using Argo interpolated fields (ISAS, Gaillard et al. 2016) contemporaneous to the satellite measurements instead of the World Ocean Atlas climatology. And thirdly the auxiliary data (wind, SST, atmospheric parameters) used as priors in the retrieval scheme, which come in the original SMOS processing from the ECMWF forecast model were replaced by ERA5 reanalysis.Our results are showing a quantitative improvement in the stability of the SMOS CCI+SSS with respect to in situ measurements for all the period as well as a decrease of the spread of the difference between SMOS and in situ salinity measurements.Bibliography:J. Boutin et al. (2020), Correcting Sea Surface Temperature Spurious Effects in Salinity Retrieved From Spaceborne L-Band Radiometer Measurements, IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3030488.F. Gaillard et al. (2016), In Situ&#8211;Based Reanalysis of the Global Ocean Temperature and Salinity with ISAS: Variability of the Heat Content and Steric Height, Journal of Climate, vol. 29, no. 4, pp. 1305-1323, doi: 10.1175/JCLI-D-15-0028.1.L. Klein and C. Swift (1977), An improved model for the dielectric constant of sea water at microwave frequencies, IEEE Transactions on Antennas and Propagation, vol. 25, no. 1, pp. 104-111, doi: 10.1109/JOE.1977.1145319.Data reference:J. Boutin et al. (2020): ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product, v2.31, for 2010 to 2019. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/eacb7580e1b54afeaabb0fd2b0a53828

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Using Saildrones to Validate Satellite-Derived Sea Surface Salinity and Sea Surface Temperature along the California/Baja Coast

10.20944/preprints201905.0309.v1 ◽

2019 ◽

Author(s):

Jorge Vazquez-Cuervo ◽

Jose Gomez-Valdes ◽

Marouan Bouali ◽

Luis Miranda ◽

Tom Van der Stocken ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Current System ◽

Local Maximum ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

California Current System ◽

Daily Variability ◽

The Impact

Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products, using comparisons with buoy measurements, do not allow for evaluating the impact of mesoscale to submesoscale variability. Here we present the validation of remotely-sensed SST and SSS data against the unmanned surface vehicle (USV) – Saildrone – measurements from the Spring 2018 Baja deployment. More specifically, biases and root mean square differences (RMSD) were calculated between USV-derived SST and SSS values, and six satellite-derived SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and three SSS (JPLSMAP, RSS40, RSS70) products. Biases between the USV SST and OSTIA/CMC/DMI were approximately zero while MUR showed a bias of 0.2C. OSTIA showed the smallest RMSD of 0.36C while DMI had the largest RMSD of 0.5C. An RMSD of 0.4C between Saildrone SST and the satellite-derived products could be explained by the daily variability in USV SST which currently cannot be resolved by remote sensing measurements. For SSS, values from the JPLSMAP product showed saltier biases of 0.2 PSU, while RSS40 and RSS70 showed fresh biases of 0.3 PSU. An RMSD of 0.4 PSU could not be explained solely by the daily variability of the USV-derived SSS. Coherences were significant at the longer wavelengths, with a local maximum at 100 km that is most likely associated with the mesoscale turbulence in the California Current System.

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A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-11-00099.1 ◽

2012 ◽

Vol 29 (6) ◽

pp. 867-879 ◽

Cited By ~ 26

Author(s):

Bruno Buongiorno Nardelli

Keyword(s):

High Resolution ◽

Root Mean Square Error ◽

Surface Temperature ◽

Sea Surface ◽

Sea Surface Salinity ◽

Mean Square ◽

Surface Salinity ◽

Signal Ratio ◽

In Situ Observations

Abstract A novel technique for the high-resolution interpolation of in situ sea surface salinity (SSS) observations is developed and tested. The method is based on an optimal interpolation (OI) algorithm that includes satellite sea surface temperature (SST) in the covariance estimation. The covariance function parameters (i.e., spatial, temporal, and thermal decorrelation scales) and the noise-to-signal ratio are determined empirically, by minimizing the root-mean-square error and mean error with respect to fully independent validation datasets. Both in situ observations and simulated data extracted from a numerical model output are used to run these tests. Different filters are applied to sea surface temperature data in order to remove the large-scale variability associated with air–sea interaction, because a high correlation between SST and SSS is expected only at small scales. In the tests performed on in situ observations, the lowest errors are obtained by selecting covariance decorrelation scales of 400 km, 6 days, and 2.75°C, respectively, a noise-to-signal ratio of 0.01 and filtering the scales longer than 1000 km in the SST time series. This results in a root-mean-square error of ~0.11 g kg−1 and a mean error of ~0.01 g kg−1, that is, reducing the errors by ~25% and ~60%, respectively, with respect to the first guess.

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Reconstructing sea surface temperature, sea surface salinity and partial pressure of carbon dioxide in atmosphere in the Okinawa Trough during the Holocene and their paleoclimatic implications

Chinese Science Bulletin ◽

10.1007/bf02900945 ◽

2003 ◽

Vol 48 (S1) ◽

pp. 88-92 ◽

Cited By ~ 2

Author(s):

Xianwei Meng ◽

Yanguang Liu ◽

Zhenxia Liu ◽

Dewen Du ◽

Qiyu Huang ◽

...

Keyword(s):

Carbon Dioxide ◽

Surface Temperature ◽

Partial Pressure ◽

Sea Surface Temperature ◽

Okinawa Trough ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

The Holocene ◽

The Okinawa Trough

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Copernicus Imaging Microwave Radiometer (CIMR) Benefits for the Copernicus Level 4 Sea-Surface Salinity Processing Chain

Remote Sensing ◽

10.3390/rs11151818 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1818 ◽

Cited By ~ 2

Author(s):

Daniele Ciani ◽

Rosalia Santoleri ◽

Gian Luigi Liberti ◽

Catherine Prigent ◽

Craig Donlon ◽

...

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Microwave Radiometer ◽

Ocean Surface ◽

Sea Surface ◽

Sea Surface Salinity ◽

Optimal Interpolation ◽

Surface Salinity ◽

Processing Chain

We present a study on the potential of the Copernicus Imaging Microwave Radiometer (CIMR) mission for the global monitoring of Sea-Surface Salinity (SSS) using Level-4 (gap-free) analysis processing. Space-based SSS are currently provided by the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. However, there are no planned missions to guarantee continuity in the remote SSS measurements for the near future. The CIMR mission is in a preparatory phase with an expected launch in 2026. CIMR is focused on the provision of global coverage, high resolution sea-surface temperature (SST), SSS and sea-ice concentration observations. In this paper, we evaluate the mission impact within the Copernicus Marine Environment Monitoring Service (CMEMS) SSS processing chain. The CMEMS SSS operational products are based on a combination of in situ and satellite (SMOS) SSS and high-resolution SST information through a multivariate optimal interpolation. We demonstrate the potential of CIMR within the CMEMS SSS operational production after the SMOS era. For this purpose, we implemented an Observing System Simulation Experiment (OSSE) based on the CMEMS MERCATOR global operational model. The MERCATOR SSSs were used to generate synthetic in situ and CIMR SSS and, at the same time, they provided a reference gap-free SSS field. Using the optimal interpolation algorithm, we demonstrated that the combined use of in situ and CIMR observations improves the global SSS retrieval compared to a processing where only in situ observations are ingested. The improvements are observed in the 60% and 70% of the global ocean surface for the reconstruction of the SSS and of the SSS spatial gradients, respectively. Moreover, the study highlights the CIMR-based salinity patterns are more accurate both in the open ocean and in coastal areas. We conclude that CIMR can guarantee continuity for accurate monitoring of the ocean surface salinity from space.

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Meridional Distribution of Surface CO2 along 67°E of the Indian Ocean

10.5194/egusphere-egu2020-20849 ◽

2020 ◽

Author(s):

Dong-Jin Kang ◽

Sang-Hwa Choi ◽

Daeyeon Kim ◽

Gyeong-Mok Lee

Keyword(s):

Indian Ocean ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Seawater ◽

Surface Salinity ◽

The North ◽

The Indian Ocean ◽

North And South

Surface seawater carbon dioxide was observed from 3 &#176;S to 27 &#176;S along 67 &#176;E of the Indian Ocean in April 2018 and 2019. Partial pressure of CO2(pCO2) in the surface seawater and the atmosphere were observed every two minutes using an underway CO2 measurement system (General Oceanics Model 8050) installed on R/V Isabu. Surface water temperature and salinity were measured as well. The pCO2 was measured using Li-7000 NDIR. Standard gases were measured every 8 hours in five classes with concentrations of 0 &#181;atm, 202 &#181;atm, 350 &#181;atm, 447 &#181;atm, and 359.87 &#181;atm. The fCO2 of atmosphere remained nearly constant at 387 &#177; 2 &#181;atm, but the surface seawater fCO2 peaked at about 3 &#176;S and tended to decrease toward the north and south. The distribution of fCO2 in surface seawater according to latitude tends to be very similar to that of sea surface temperature. In order to investigate the factors that control the distribution of fCO2 in surface seawater, we analyzed the sea surface temperature, sea surface salinity, and other factors. The effects of salinity are insignificant, and the surface fCO2 distribution is mainly controlled by sea surface temperature and other factors that can be represented mainly by biological activity and mixing.

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Validation and correction of sea surface salinity retrieval from SMAP

10.5194/egusphere-egu2020-4389 ◽

2020 ◽

Author(s):

Sisi Qin

Keyword(s):

Soil Moisture ◽

In Situ Measurements ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

Tropical Regions ◽

Ocean Salinity ◽

Forecast Models ◽

Level 3

In this study, Sea Surface Salinity (SSS) Level 3 (L3) daily product derived from Soil Moisture Active Passive (SMAP) during the year 2016, was validated and compared with SSS daily products derived from Soil Moisture and Ocean Salinity (SMOS) and in-situ measurements. Generally, the Root Mean Square Error (RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the Sea Surface Temperature (SST).Then, aregression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.

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