Sea Surface Salinity

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Jacqueline Boutin - One of the best experts on this subject based on the ideXlab platform.

  • IGARSS - Satellite Sea Surface Salinity: Evaluation of Products and Impact of Retrieval Algorithms
    IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019
    Co-Authors: Emmanuel P Dinnat, David M. Le Vine, Jacqueline Boutin, Thomas Meissner
    Abstract:

    We present comparisons between satellite Sea Surface Salinity products from the SMOS, Aquarius and SMAP missions and assess some of the reasons for the observed differences. We reprocess Aquarius retrievals using the dielectric constant model and ancillary Sea Surface temperature product used for SMOS. We also quantify the impact of the recently revised atmospheric model for Aquarius end of mission product. One recurrent feature of the SSS difference between satellite retrieval and in situ observation has been its dependence on Sea Surface temperature. We discuss the performances of the latest algorithms in mitigating this bias and possible improvements in theoretical models.

  • Rainfall imprint on SMOS and SMAP Sea Surface Salinity
    2018
    Co-Authors: Alexandre Supply, Jacqueline Boutin, J.-l. Vergely, Gilles Reverdin, Audrey Hasson, Cécile Mallet, Nicolas Viltard
    Abstract:

    Two L-Band (1.4GHz) microwave radiometer missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP), currently provide Sea-Surface Salinity (SSS) measurements. At this frequency, Salinity is measured in the first centimetre below the Sea Surface, which makes it very sensitive to the presence of fresh water lenses linked to rain events. A relationship between Salinity anomaly (ΔS) and rain rate (RR) is derived in the Pacific intertropical convergence zone from SMOS and SMAP SSS measurements, and the RR from the Special Sensor Microwave Imager/Sounder (SSMIS). We look at the robustness of the relationship in various areas. It is then used to estimate RR from SMOS and SMAP SSS measurements. By applying this algorithm over the global ocean between 30°S and 30°N, we found that the rain imprint is the dominant factor affecting SMOS and SMAP variability at small temporal scale, except in river plumes (Amazon, Mississippi, etc.) and in regions with high mesoscale variability. Our study allows to identify the observed difference between Argo products and satellite Salinity that are due to the impact of rain on the satellite Salinity in the first centimetre measured.

  • Precipitation Estimates from SMOS Sea-Surface Salinity
    Quarterly Journal of the Royal Meteorological Society, 2018
    Co-Authors: Alexandre Supply, Jacqueline Boutin, J.-l. Vergely, Nicolas Martin, Gilles Reverdin, Audrey Hasson, Cécile Mallet, Nicolas Viltard
    Abstract:

    Two L-Band (1.4 GHz) microwave radiometer missions, SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive) currently provide Sea Surface Salinity (SSS) measurements. At this frequency, Salinity is measured in the first centimetre below the Sea Surface, which makes it very sensitive to the presence of fresh water lenses linked to rain events. A relationship between Salinity anomaly (ΔS) and rain rate (RR) is derived in the Pacific Inter Tropical Convergence Zone from SMOS SSS measurements and Special Sensor Microwave Imager/Sounder (SSMIS) RR. It is then used to develop an algorithm to estimate RR from SMOS SSS measurements. A heuristic function is developed to correct the SMOS-estimated negative RR due to measurements noise. Correlation between SMOS and SSMIS RR and between SMOS and Integrated MultisatellitE Retrievals for GPM (IMERG) RR are high when SMOS and SSMIS passes are less than 15mn apart (r=0.7 at 1°×1° resolution), showing a good quality of SMOS RR retrievals. When the time shift between SMOS and SSMIS passes increases, the correlation between SMOS and IMERG RR diminishes. This suggests that L-band radiometry can provide information complementary to GPM missions to improve RR products interpolated at high temporal resolution. The retrieval is successfully tested on SMAP SSS. We also check that our algorithm provides reliable estimates of RR when averaged at a monthly time scale.

  • Sea Surface Salinity: Inter-comparison of satellite products, in situ measurements, and impact of differences in retrieval algorithm
    2017
    Co-Authors: Emmanuel P Dinnat, David M. Le Vine, Jacqueline Boutin, Thomas Meissner
    Abstract:

    We present comparisons between satellite Sea Surface Salinity products from the SMOS, Aquarius and SMAP missions and assess some of the reasons for the observed differences. To do so, we reprocess Aquarius retrievals using the dielectric constant model and ancillary Sea Surface temperature product used for SMOS. We quantify their impact on the differences between SMOS and Aquarius, and validate the various Aquarius algorithms using in situ Salinity measurements. Among the significant difference in retrieved Sea Surface Salinity are the dependence to Sea Surface temperature and coastal biases. New approaches to for land contamination correction will be presented.

  • IGARSS - Sea Surface Salinity: Inter-comparison of satellite products, in situ measurements, and impact of differences in retrieval algorithm
    2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
    Co-Authors: Emmanuel P Dinnat, David M. Le Vine, Jacqueline Boutin, Thomas Meissner
    Abstract:

    We present comparisons between satellite Sea Surface Salinity products from the SMOS, Aquarius and SMAP missions and assess some of the reasons for the observed differences. To do so, we reprocess Aquarius retrievals using the dielectric constant model and ancillary Sea Surface temperature product used for SMOS. We quantify their impact on the differences between SMOS and Aquarius, and validate the various Aquarius algorithms using in situ Salinity measurements. Among the significant difference in retrieved Sea Surface Salinity are the dependence to Sea Surface temperature and coastal biases. New approaches to for land contamination correction will be presented.

Nicolas Reul - One of the best experts on this subject based on the ideXlab platform.

  • Sea Surface Salinity under rain cells: SMOS satellite and in situ drifters observations
    Journal of Geophysical Research. Oceans, 2014
    Co-Authors: Jacqueline Boutin, Xiaobin Yin, Nicolas Martin, Gilles Reverdin, Simon Morisset, Luca R. Centurioni, Nicolas Reul
    Abstract:

    We study the signature of rainfall on S1cm, the Sea Surface Salinity retrieved from the Soil Moisture and Ocean Salinity (SMOS) satellite mission first by comparing SMOS S1cm with ARGO Sea Surface Salinity measured at about 5 m depth in the Intertropical Convergence Zone (ITCZ) and in the Southern Pacific Convergence Zone; second by investigating spatial variability of SMOS S1cm related to rainfall. The resulting estimated S1cm decrease associated with rainfall occurring within less than 1 h from the Salinity measurement is close to −0.2 pss (mm h−1) −1. We estimate that rain induced roughness and atmospheric effects are responsible for no more than 20% of this value. We also study the signature of rainfall on Sea Surface Salinity measured by Surface drifters at 45 cm depth and find a decrease associated with rainfall of −0.21 (±0.14) pss (mm h−1) −1, consistent with SMOS observations. When averaged over one month, this rain associated Salinity decrease is at most −0.2 in monthly 100 × 100 km2 pixels, and at most 40% of the difference between SMOS S1cm and interpolated in situ bulk Salinity in pixels near the ITCZ. This suggests that more than half of this difference is related to the in situ products obtained from optimal interpolation and therefore influenced by smoothing and relaxation to climatology. Finally, further studies on the satellite-derived salinities should pay attention to that as well as to other sources of uncertainties in satellite measurements and not interpret fully the observed differences between in situ and satellite mapped products, as rain induced SSS variability.

  • SMOS first data analysis for Sea Surface Salinity determination
    International Journal of Remote Sensing, 2013
    Co-Authors: Jordi Font, Jacqueline Boutin, Nicolas Reul, Carolina Gabarró, Paul Spurgeon, Joaquim Ballabrera-poy, Andrei Chuprin, Jérôme Gourrion, Sébastien Guimbard, Claire Hénocq
    Abstract:

    Soil Moisture and Ocean Salinity (SMOS), launched on 2 November 2009, is the first satellite mission addressing Sea Surface Salinity (SSS) measurement from space. Its unique payload is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at the L-band frequency. This article presents a summary of SSS retrieval from SMOS observations and shows initial results obtained one year after launch. These results are encouraging, but also indicate that further improvements at various data processing levels are needed and hence are currently under investigation.

  • SMOS first data analysis for Sea Surface Salinity determination
    International Journal of Remote Sensing, 2012
    Co-Authors: Jordi Font, Jacqueline Boutin, Nicolas Reul, Carolina Gabarró, Paul Spurgeon, Joaquim Ballabrera-poy, Andrei Chuprin, Jérôme Gourrion, Sébastien Guimbard, Claire Hénocq
    Abstract:

    International audienceSoil Moisture and Ocean Salinity (SMOS), launched on 2 November 2009, is the first satellite mission addressing Sea Surface Salinity (SSS) measurement from space. Its unique payload is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at the L-band frequency. This article presents a summary of SSS retrieval from SMOS observations and shows initial results obtained one year after launch. These results are encouraging, but also indicate that further improvements at various data processing levels are needed and hence are currently under investigation

  • SMOS: The Challenging Sea Surface Salinity Measurement From Space
    Proceedings of the IEEE, 2010
    Co-Authors: Jordi Font, A. Camps, Jacqueline Boutin, Nicolas Reul, Andrés Borges, Manuel Martín-neira, Yann H. Kerr, Achim Hahne, Susanne Mecklenburg
    Abstract:

    Soil Moisture and Ocean Salinity, European Space Agency, is the first satellite mission addressing the challenge of measuring Sea Surface Salinity from space. It uses an L-band microwave interferometric radiometer with aperture synthesis (MIRAS) that generates brightness temperature images, from which both geophysical variables are computed. The retrieval of Salinity requires very demanding performances of the instrument in terms of calibration and stability. This paper highlights the importance of ocean Salinity for the Earth's water cycle and climate; provides a detailed description of the MIRAS instrument, its principles of operation, calibration, and image-reconstruction techniques; and presents the algorithmic approach implemented for the retrieval of Salinity from MIRAS observations, as well as the expected accuracy of the obtained results.

  • IGARSS - SMOS Sea Surface Salinity prototype processor: Algorithm validation
    2007 IEEE International Geoscience and Remote Sensing Symposium, 2007
    Co-Authors: S. Zine, Jacqueline Boutin, Nicolas Reul, Joseph Tenerelli, Jordi Font, Carolina Gabarró, M. Talone, Philippe Waldteufel, F. Petitcolin, J.-l. Vergely
    Abstract:

    The Soil Moisture and Ocean Salinity (SMOS) mission (launch scheduled for 2008) aims at obtaining global maps of soil moisture and Sea Surface Salinity (SSS). It uses an L-band (1.4 GHz) microwave interferometric radiometer to obtain brightness temperatures (Tb) at the Earth Surface at horizontal and vertical polarizations. They will be used to retrieve both geophysical variables, following specifically designed algorithms that will be applied when the satellite field-of-view is covering land or ocean Surfaces respectively. The retrieval of Salinity is a complex process that requires the knowledge of environmental information and an accurate processing of the radiometer measurements, because of the narrow range of ocean Tb and the strong impact on the measures of geophysical parameters (such as Sea state). Here we present the baseline approach chosen to retrieve Sea Surface Salinity from SMOS data, as developed and implemented by the joint team of scientists and engineers responsible for the SMOS Salinity Level 2 Prototype Processor. We present academic tests conducted over homogeneous scenes with the prototype. In these configurations, external perturbation sources (sky radiation, sun glint, ...) are not taken into account. Roughness is the main Sea Surface signal disturbing SSS retrieval.

J.-l. Vergely - One of the best experts on this subject based on the ideXlab platform.

  • Rainfall imprint on SMOS and SMAP Sea Surface Salinity
    2018
    Co-Authors: Alexandre Supply, Jacqueline Boutin, J.-l. Vergely, Gilles Reverdin, Audrey Hasson, Cécile Mallet, Nicolas Viltard
    Abstract:

    Two L-Band (1.4GHz) microwave radiometer missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP), currently provide Sea-Surface Salinity (SSS) measurements. At this frequency, Salinity is measured in the first centimetre below the Sea Surface, which makes it very sensitive to the presence of fresh water lenses linked to rain events. A relationship between Salinity anomaly (ΔS) and rain rate (RR) is derived in the Pacific intertropical convergence zone from SMOS and SMAP SSS measurements, and the RR from the Special Sensor Microwave Imager/Sounder (SSMIS). We look at the robustness of the relationship in various areas. It is then used to estimate RR from SMOS and SMAP SSS measurements. By applying this algorithm over the global ocean between 30°S and 30°N, we found that the rain imprint is the dominant factor affecting SMOS and SMAP variability at small temporal scale, except in river plumes (Amazon, Mississippi, etc.) and in regions with high mesoscale variability. Our study allows to identify the observed difference between Argo products and satellite Salinity that are due to the impact of rain on the satellite Salinity in the first centimetre measured.

  • Precipitation Estimates from SMOS Sea-Surface Salinity
    Quarterly Journal of the Royal Meteorological Society, 2018
    Co-Authors: Alexandre Supply, Jacqueline Boutin, J.-l. Vergely, Nicolas Martin, Gilles Reverdin, Audrey Hasson, Cécile Mallet, Nicolas Viltard
    Abstract:

    Two L-Band (1.4 GHz) microwave radiometer missions, SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive) currently provide Sea Surface Salinity (SSS) measurements. At this frequency, Salinity is measured in the first centimetre below the Sea Surface, which makes it very sensitive to the presence of fresh water lenses linked to rain events. A relationship between Salinity anomaly (ΔS) and rain rate (RR) is derived in the Pacific Inter Tropical Convergence Zone from SMOS SSS measurements and Special Sensor Microwave Imager/Sounder (SSMIS) RR. It is then used to develop an algorithm to estimate RR from SMOS SSS measurements. A heuristic function is developed to correct the SMOS-estimated negative RR due to measurements noise. Correlation between SMOS and SSMIS RR and between SMOS and Integrated MultisatellitE Retrievals for GPM (IMERG) RR are high when SMOS and SSMIS passes are less than 15mn apart (r=0.7 at 1°×1° resolution), showing a good quality of SMOS RR retrievals. When the time shift between SMOS and SSMIS passes increases, the correlation between SMOS and IMERG RR diminishes. This suggests that L-band radiometry can provide information complementary to GPM missions to improve RR products interpolated at high temporal resolution. The retrieval is successfully tested on SMAP SSS. We also check that our algorithm provides reliable estimates of RR when averaged at a monthly time scale.

  • What can we learn on rainfall from SMOS Sea Surface Salinity?
    2016
    Co-Authors: Alexandre Supply, Jacqueline Boutin, J.-l. Vergely, Gilles Reverdin, Audrey Hasson, Cécile Mallet, Nicolas Viltard
    Abstract:

    The Soil Moisture and Ocean Salinity (SMOS) satellite mission has been measuring Sea Surface Salinity (SSS) for over 6 years with about 5-day global ocean coverage and a spatial resolution of about 50 km. In rainy regions, at local and short time scales, the spatio-temporal variability of SSS is dominated by rainfall. The relationship between Surface freshening and rain rate (RR) has been highlighted in the Pacific intertropical convergence zone (Boutin et al., 2014). In this context, this study investigates the rainfall characteristics that may be inferred from SMOS SSS based on statistical approach. Salinity anomalies associated with rainfall events are first estimated. In order to do so, a reference Salinity (i.e. with no rain-induced signal) is computed for each pixel of the SMOS observation using the statistical distribution within 3°x3° region of SMOS SSS. In case the distribution is asymmetrical toward low values, suggesting a rain influence, a mean ‘non-rainy’ SSS corresponding to a Gaussian distribution fitted onto the highest part of the distribution (quantile>0.8) is computed. Rain rate probability associated with SSS anomalies are then inferred from a probabilistic approach. It also enables us to separate the rain intensity depending on the SSS anomaly. Finally, a RR retrieval algorithm based on SSS is developed combining this dependence with the SSS-RR relationship described in Boutin et al. (2014) and a spatial association index (spatial correlations of SSS anomalies within 100 km). SMOS-derived RRs are then collocated with various radiometers and CMORPH RR datasets. Their consistency is assessed. A particular focus will be put on RRs estimates derived during the Salinity Processes in the Upper Ocean Regional Study (SPURS-2, http://spurs2.jpl.nasa.gov) from a near real time implementation of rain retrieval from SMOS SSS. Boutin et al. (2014), Sea Surface Salinity under rain cells: SMOS satellite and in situ drifters observations, JGR: Oceans, doi:10.1002/2014JC010070

  • IGARSS - SMOS Sea Surface Salinity prototype processor: Algorithm validation
    2007 IEEE International Geoscience and Remote Sensing Symposium, 2007
    Co-Authors: S. Zine, Jacqueline Boutin, Nicolas Reul, Joseph Tenerelli, Jordi Font, Carolina Gabarró, M. Talone, Philippe Waldteufel, F. Petitcolin, J.-l. Vergely
    Abstract:

    The Soil Moisture and Ocean Salinity (SMOS) mission (launch scheduled for 2008) aims at obtaining global maps of soil moisture and Sea Surface Salinity (SSS). It uses an L-band (1.4 GHz) microwave interferometric radiometer to obtain brightness temperatures (Tb) at the Earth Surface at horizontal and vertical polarizations. They will be used to retrieve both geophysical variables, following specifically designed algorithms that will be applied when the satellite field-of-view is covering land or ocean Surfaces respectively. The retrieval of Salinity is a complex process that requires the knowledge of environmental information and an accurate processing of the radiometer measurements, because of the narrow range of ocean Tb and the strong impact on the measures of geophysical parameters (such as Sea state). Here we present the baseline approach chosen to retrieve Sea Surface Salinity from SMOS data, as developed and implemented by the joint team of scientists and engineers responsible for the SMOS Salinity Level 2 Prototype Processor. We present academic tests conducted over homogeneous scenes with the prototype. In these configurations, external perturbation sources (sky radiation, sun glint, ...) are not taken into account. Roughness is the main Sea Surface signal disturbing SSS retrieval.

  • Overview of the SMOS Sea Surface Salinity Prototype Processor
    2007
    Co-Authors: S. Zine, Jacqueline Boutin, Nicolas Reul, Joseph Tenerelli, Jordi Font, Carolina Gabarró, M. Talone, Philippe Waldteufel, F. Petitcolin, J.-l. Vergely
    Abstract:

    The Soil Moisture and Ocean Salinity (SMOS) mission (launch scheduled for 2008) aims at obtaining global maps of soil moisture and Sea Surface Salinity (SSS). It uses an L-band (1.4 GHz) microwave interferometric radiometer to obtain brightness temperatures (Tb) at the Earth Surface at horizontal and vertical polarizations. They will be used to retrieve both geophysical variables, following specifically designed algorithms that will be applied when the satellite field-of-view is covering land or ocean Surfaces respectively. The retrieval of Salinity is a complex process that requires the knowledge of environmental information and an accurate processing of the radiometer measurements, because of the narrow range of ocean Tb and the strong impact on the measures of geophysical parameters (such as Sea state). Here we present the baseline approach chosen to retrieve Sea Surface Salinity from SMOS data, as developed and implemented by the joint team of scientists and engineers responsible for the SMOS Salinity Level 2 Prototype Processor. We present academic tests conducted over homogeneous scenes with the prototype. In these configurations, external perturbation sources (sky radiation, sun glint, ...) are not taken into account. Roughness is the main Sea Surface signal disturbing SSS retrieval. In dual pol, wind speed biases are better corrected at the center of the swath than at the edge.

Jordi Font - One of the best experts on this subject based on the ideXlab platform.

  • SMOS first data analysis for Sea Surface Salinity determination
    International Journal of Remote Sensing, 2013
    Co-Authors: Jordi Font, Jacqueline Boutin, Nicolas Reul, Carolina Gabarró, Paul Spurgeon, Joaquim Ballabrera-poy, Andrei Chuprin, Jérôme Gourrion, Sébastien Guimbard, Claire Hénocq
    Abstract:

    Soil Moisture and Ocean Salinity (SMOS), launched on 2 November 2009, is the first satellite mission addressing Sea Surface Salinity (SSS) measurement from space. Its unique payload is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at the L-band frequency. This article presents a summary of SSS retrieval from SMOS observations and shows initial results obtained one year after launch. These results are encouraging, but also indicate that further improvements at various data processing levels are needed and hence are currently under investigation.

  • SMOS first data analysis for Sea Surface Salinity determination
    International Journal of Remote Sensing, 2012
    Co-Authors: Jordi Font, Jacqueline Boutin, Nicolas Reul, Carolina Gabarró, Paul Spurgeon, Joaquim Ballabrera-poy, Andrei Chuprin, Jérôme Gourrion, Sébastien Guimbard, Claire Hénocq
    Abstract:

    International audienceSoil Moisture and Ocean Salinity (SMOS), launched on 2 November 2009, is the first satellite mission addressing Sea Surface Salinity (SSS) measurement from space. Its unique payload is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at the L-band frequency. This article presents a summary of SSS retrieval from SMOS observations and shows initial results obtained one year after launch. These results are encouraging, but also indicate that further improvements at various data processing levels are needed and hence are currently under investigation

  • SMOS: The Challenging Sea Surface Salinity Measurement From Space
    Proceedings of the IEEE, 2010
    Co-Authors: Jordi Font, A. Camps, Jacqueline Boutin, Nicolas Reul, Andrés Borges, Manuel Martín-neira, Yann H. Kerr, Achim Hahne, Susanne Mecklenburg
    Abstract:

    Soil Moisture and Ocean Salinity, European Space Agency, is the first satellite mission addressing the challenge of measuring Sea Surface Salinity from space. It uses an L-band microwave interferometric radiometer with aperture synthesis (MIRAS) that generates brightness temperature images, from which both geophysical variables are computed. The retrieval of Salinity requires very demanding performances of the instrument in terms of calibration and stability. This paper highlights the importance of ocean Salinity for the Earth's water cycle and climate; provides a detailed description of the MIRAS instrument, its principles of operation, calibration, and image-reconstruction techniques; and presents the algorithmic approach implemented for the retrieval of Salinity from MIRAS observations, as well as the expected accuracy of the obtained results.

  • IGARSS - SMOS Sea Surface Salinity prototype processor: Algorithm validation
    2007 IEEE International Geoscience and Remote Sensing Symposium, 2007
    Co-Authors: S. Zine, Jacqueline Boutin, Nicolas Reul, Joseph Tenerelli, Jordi Font, Carolina Gabarró, M. Talone, Philippe Waldteufel, F. Petitcolin, J.-l. Vergely
    Abstract:

    The Soil Moisture and Ocean Salinity (SMOS) mission (launch scheduled for 2008) aims at obtaining global maps of soil moisture and Sea Surface Salinity (SSS). It uses an L-band (1.4 GHz) microwave interferometric radiometer to obtain brightness temperatures (Tb) at the Earth Surface at horizontal and vertical polarizations. They will be used to retrieve both geophysical variables, following specifically designed algorithms that will be applied when the satellite field-of-view is covering land or ocean Surfaces respectively. The retrieval of Salinity is a complex process that requires the knowledge of environmental information and an accurate processing of the radiometer measurements, because of the narrow range of ocean Tb and the strong impact on the measures of geophysical parameters (such as Sea state). Here we present the baseline approach chosen to retrieve Sea Surface Salinity from SMOS data, as developed and implemented by the joint team of scientists and engineers responsible for the SMOS Salinity Level 2 Prototype Processor. We present academic tests conducted over homogeneous scenes with the prototype. In these configurations, external perturbation sources (sky radiation, sun glint, ...) are not taken into account. Roughness is the main Sea Surface signal disturbing SSS retrieval.

  • Overview of the SMOS Sea Surface Salinity Prototype Processor
    2007
    Co-Authors: S. Zine, Jacqueline Boutin, Nicolas Reul, Joseph Tenerelli, Jordi Font, Carolina Gabarró, M. Talone, Philippe Waldteufel, F. Petitcolin, J.-l. Vergely
    Abstract:

    The Soil Moisture and Ocean Salinity (SMOS) mission (launch scheduled for 2008) aims at obtaining global maps of soil moisture and Sea Surface Salinity (SSS). It uses an L-band (1.4 GHz) microwave interferometric radiometer to obtain brightness temperatures (Tb) at the Earth Surface at horizontal and vertical polarizations. They will be used to retrieve both geophysical variables, following specifically designed algorithms that will be applied when the satellite field-of-view is covering land or ocean Surfaces respectively. The retrieval of Salinity is a complex process that requires the knowledge of environmental information and an accurate processing of the radiometer measurements, because of the narrow range of ocean Tb and the strong impact on the measures of geophysical parameters (such as Sea state). Here we present the baseline approach chosen to retrieve Sea Surface Salinity from SMOS data, as developed and implemented by the joint team of scientists and engineers responsible for the SMOS Salinity Level 2 Prototype Processor. We present academic tests conducted over homogeneous scenes with the prototype. In these configurations, external perturbation sources (sky radiation, sun glint, ...) are not taken into account. Roughness is the main Sea Surface signal disturbing SSS retrieval. In dual pol, wind speed biases are better corrected at the center of the swath than at the edge.

Emmanuel P Dinnat - One of the best experts on this subject based on the ideXlab platform.

  • Revisiting the Global Patterns of Seasonal Cycle in Sea Surface Salinity
    2020
    Co-Authors: Frederick M. Bingham, Oleg Melnichenko, Emmanuel P Dinnat, Tong Lee, Severine Fournier, Wenqing Tang
    Abstract:

    Seasonal cycle is the largest source of variability for Sea Surface Salinity (SSS) and has a significant influence on the upper-ocean stratification and water-mass formation. The advent of the Argo...

  • IGARSS - Sea Surface Salinity Retrievals from Aquarius Using Neural Networks
    IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019
    Co-Authors: Yan Soldo, David M. Le Vine, Emmanuel P Dinnat
    Abstract:

    Even though the Sea Surface Salinity (SSS) retrieved from Aquarius are generally very close to in-situ measurements, the level of similarity varies with the region and with the circumstances of the observations (wind speed, Sea Surface temperature, etc.). SSS is currently retrieved from the brightness temperatures measured by Aquarius and applying the current theoretical model for the propagation and emission of the natural thermal radiation. In this contribution we consider an alternative retrieval approach based on a Neural Network (NN) with the goal of improving the subsets of Aquarius SSS data that are in poorer agreement with in-situ measurements. The subset considered here are the SSS retrieved at latitudes higher than 30˚. The output of the NN approach are compared against in-situ measurements using four statistical metrics (correlation coefficient, bias, RMSD and 5% trimmed range). The output of the NN and the nominal Aquarius SSS are compared against SSS values from in-situ measurements and from ocean models. From these comparisons it appears that the output of the NN matches the in-situ measurements better than the nominal Aquarius SSS.

  • Editorial for the Special Issue ``Sea Surface Salinity Remote Sensing''
    Remote Sensing, 2019
    Co-Authors: Emmanuel P Dinnat, Xiaobin Yin
    Abstract:

    This Special Issue gathers papers reporting reSearch on various aspects of remote sensing of Sea Surface Salinity (SSS) and the use of satellites SSS in oceanography. It includes contributions presenting improvements in empirical or theoretical radiative transfer models; mitigation techniques of external interference such as radio frequency interferences (RFI) and land contamination; comparisons and validation of remote sensing products with in situ observations; retrieval techniques for improved coastal SSS monitoring, high latitude SSS monitoring and assessment of ocean interactions with the cryosphere; and data fusion techniques combining SSS with Sea Surface temperature (SST). New instrument technology for the future of SSS remote sensing is also presented.

  • IGARSS - A Theoretical Algorithm for the Retrieval of Sea Surface Salinity from Smap Observations
    IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019
    Co-Authors: Emmanuel P Dinnat, David M. Le Vine, Yan Soldo, Paolo De Matthaeis
    Abstract:

    We present a physics-based algorithm for retrieving Sea Surface Salinity from L-band radiometric observations from the NASA SMAP instrument. The model is used to assess the radiometer calibration and its long-term stability and produce Salinity products that are evaluated against in situ measurements from the Argo network of drifting floats.

  • IGARSS - Satellite Sea Surface Salinity: Evaluation of Products and Impact of Retrieval Algorithms
    IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019
    Co-Authors: Emmanuel P Dinnat, David M. Le Vine, Jacqueline Boutin, Thomas Meissner
    Abstract:

    We present comparisons between satellite Sea Surface Salinity products from the SMOS, Aquarius and SMAP missions and assess some of the reasons for the observed differences. We reprocess Aquarius retrievals using the dielectric constant model and ancillary Sea Surface temperature product used for SMOS. We also quantify the impact of the recently revised atmospheric model for Aquarius end of mission product. One recurrent feature of the SSS difference between satellite retrieval and in situ observation has been its dependence on Sea Surface temperature. We discuss the performances of the latest algorithms in mitigating this bias and possible improvements in theoretical models.

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