Intercomparison of In-Situ and Remote Sensing Salinity Products in the Gulf of Mexico, a River-Influenced System

The recent emergence of satellite-based sea surface salinity (SSS) measurements provides new opportunities for oceanographic research on freshwater influence in coastal environments. Several products currently exist from multiple observing platforms and processing centers, making product selection for different uses challenging. Here we evaluate four popular SSS datasets in the Gulf of Mexico (GoM) to characterize the error in each product versus in-situ observations: Two products from NASA’s Soil Moisture Active Passive (SMAP) mission, processed by Remote Sensing Systems (REMSS) (40 km and 70 km); one SMAP 60 km product from the Jet Propulsion Laboratory (JPL); and one 60 km product from ESA’s Soil Moisture Ocean Salinity (SMOS) mission. Overall, the four products are remarkably consistent on seasonal time scales, reproducing dominant salinity features. Towards the coast, 3 of the 4 products (JPL SMAP, REMSS 40 km SMAP, and SMOS) show increasing salty biases (reaching 0.7–1 pss) and Root Mean Square Error (RMSD) (reaching 1.5–2.5 pss), and a decreasing signal to noise ratio from 3 to 1. REMSS 40 km generally shows a lower RMSD than other products (~0.5 vs. ~1.1 pss) in the nearshore region. However, at some buoy locations, SMOS shows the lowest RMSD values, but has a higher bias overall (>0.2 vs. <0.1 pss). The REMSS 70km product is not consistent in terms of data availability in the nearshore region and performs poorly within 100 km of the coast, relative to other products. Additional analysis of the temporal structure of the errors over a range of scales (8/9-day to seasonal) shows significantly decreasing RMSD with increasing timescales across products.

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Modelling soil moisture at SMOS scale by use of a SVAT model over the Valencia Anchor Station

Hydrology and Earth System Sciences ◽

10.5194/hess-14-831-2010 ◽

2010 ◽

Vol 14 (5) ◽

pp. 831-846 ◽

Cited By ~ 30

Author(s):

S. Juglea ◽

Y. Kerr ◽

A. Mialon ◽

J.-P. Wigneron ◽

E. Lopez-Baeza ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Surface Soil ◽

Temporal Distribution ◽

Sea Surface Salinity ◽

Surface Soil Moisture ◽

Surface Salinity ◽

Anchor Station ◽

Svat Model

Abstract. The main goal of the SMOS (Soil Moisture and Ocean Salinity) mission is to deliver global fields of surface soil moisture and sea surface salinity using L-band (1.4 GHz) radiometry. Within the context of the Science preparation for SMOS, the Valencia Anchor Station (VAS) experimental site, in Spain, was chosen to be one of the main test sites in Europe for Calibration/Validation (Cal/Val) activities. In this framework, the paper presents an approach consisting in accurately simulating a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over the wide VAS surface (50×50 km2). Ground and meteorological measurements over the area are used as the input of a Soil-Vegetation-Atmosphere-Transfer (SVAT) model, SURFEX (Externalized Surface) - module ISBA (Interactions between Soil-Biosphere-Atmosphere) to simulate the spatial and temporal distribution of surface soil moisture. The calibration as well as the validation of the ISBA model are performed using in situ soil moisture measurements. It is shown that a good consistency is reached when point comparisons between simulated and in situ soil moisture measurements are made. Actually, an important challenge in remote sensing approaches concerns product validation. In order to obtain an representative soil moisture mapping over the Valencia Anchor Station (50×50 km2 area), a spatialization method is applied. For verification, a comparison between the simulated spatialized soil moisture and remote sensing data from the Advanced Microwave Scanning Radiometer on Earth observing System (AMSR-E) and from the European Remote Sensing Satellites (ERS-SCAT) is performed. Despite the fact that AMSR-E surface soil moisture product is not reproducing accurately the absolute values, it provides trustworthy information on surface soil moisture temporal variability. However, during the vegetation growing season the signal is perturbed. By using the polarization ratio a better agreement is obtained. ERS-SCAT soil moisture products are also used to be compared with the simulated spatialized soil moisture. However, the lack of soil moisture data from the ERS-SCAT sensor over the area (45 observations for one year) prevented capturing the soil moisture variability.

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Modelling soil moisture at SMOS scale by use of a SVAT model over the Valencia Anchor Station

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-649-2010 ◽

2010 ◽

Vol 7 (1) ◽

pp. 649-686 ◽

Cited By ~ 2

Author(s):

S. Juglea ◽

Y. Kerr ◽

A. Mialon ◽

J.-P. Wigneron ◽

E. Lopez-Baeza ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Surface Soil ◽

Remote Sensing Data ◽

Sea Surface Salinity ◽

Surface Soil Moisture ◽

Surface Salinity ◽

Anchor Station ◽

Svat Model

Abstract. The main goal of the SMOS (Soil Moisture and Ocean Salinity) mission is to deliver global fields of surface soil moisture and sea surface salinity using L-band (1.4 GHz) radiometry. Within the context of the preparation for this mission over land, the Valencia Anchor Station experimental site, in Spain, was chosen to be one of the main test sites in Europe for the SMOS Calibration/Validation (Cal/Val) activities. Ground and meteorological measurements over the area are used as the input of a Soil-Vegetation-Atmosphere-Transfer (SVAT) model, SURFEX (Externalized Surface)-module ISBA (Interactions between Soil-Biosphere-Atmosphere) so as to simulate the surface soil moisture. The calibration as well as the validation of the ISBA model was made using in situ soil moisture measurements. It is shown that a good consistency was reached when point comparisons between simulated and in situ soil moisture measurements were made. In order to obtain an accurate soil moisture mapping over the Valencia Anchor Station (50×50 km2 area), a spatialization method has been applied. To validate the approach, a comparison with remote sensing data from the Advanced Microwave Scanning Radiometer on Earth observing System (AMSR-E) and from the European Remote Sensing Satellites (ERS-Scat) was performed. Despite the fact that AMSR-E surface soil moisture product is not reproducing accurately the absolute values, it provides trustworthy information on surface soil moisture temporal variability. However, during the vegetation growing season the signal is perturbed. By using the polarization ratio a better agreement is obtained. ERS-Scat soil moisture products were also used to be compared with the simulated spatialized soil moisture. The seasonal variations were well reproduced. However, the lack of soil moisture data over the area (45 observations for one year) was a limit into completely understanding the soil moisture variability.

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

Remote Sensing ◽

10.3390/rs11070750 ◽

2019 ◽

Vol 11 (7) ◽

pp. 750 ◽

Cited By ~ 8

Author(s):

Emmanuel Dinnat ◽

David Le Vine ◽

Jacqueline Boutin ◽

Thomas Meissner ◽

Gary Lagerloef

Keyword(s):

Dielectric Constant ◽

Soil Moisture ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

In Situ Observations ◽

The Impact

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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Global validation of the ESA CCI+SSS products

10.5194/egusphere-egu2020-11683 ◽

2020 ◽

Author(s):

Adrien Martin ◽

Sébastien Guimbard ◽

Jacqueline Boutin ◽

Nicolas Reul ◽

Rafael Catany

Keyword(s):

Soil Moisture ◽

European Space Agency ◽

Ground Truth ◽

Sea Surface Salinity ◽

Global Ocean ◽

Surface Salinity ◽

Spatial Gradients ◽

Good Agreement

The European Space Agency (ESA) Climate Change Initiative for Sea Surface Salinity (CCI+SSS) project aims at generating long-term, improved, calibrated global SSS fields from space.&#160;The project started in mid-2018 and in its first year has produced a 9-year dataset (2010-2018) from the three available L-band radiometer satellites (SMOS: Soil Moisture and Ocean Salinity; Aquarius; SMAP: Soil Moisture Active Passive) and validated it against in situ references (Argo and ISAS: In Situ Analysis System). The dataset is available at https://catalogue.ceda.ac.uk/uuid/9ef0ebf847564c2eabe62cac4899ec41.The comparisons with in situ ground truth indicate much better performances than the ones obtained with a single satellite data product, with global precision against in situ references of 0.16 pss and 0.10 pss in areas with low variability.&#160;There is a very good agreement between the CCI dataset and references, including long-term stability, with differences within +-0.05 pss for global ocean within [40&#176;S-20&#176;N]. At higher latitude, we observe seasonal oscillation of the CCI SSS difference against references.&#160;The CCI SSS products uncertainty have been validated against references and show good agreement as long as the spatial representativeness is considered in presence of strong spatial gradients in salinity.

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CATDS CEC-LOCEAN debiased version 4 Sea Surface Salinity

10.5194/egusphere-egu2020-7545 ◽

2020 ◽

Author(s):

Clovis Thouvenin-Masson ◽

Jacqueline Boutin ◽

Jean-Luc Vergely ◽

Dimitry Khvorostyanov ◽

Stéphane Tarot

Keyword(s):

Remote Sensing ◽

Wind Speed ◽

Sea Surface ◽

Sea Surface Salinity ◽

Global Ocean ◽

Surface Salinity ◽

The Mean ◽

Analysis System ◽

Areas Of Interest

The Centre Aval de Traitement des Donn&#233;es SMOS (CATDS), developped by the CNES in collaboration with the CESBIO and IFREMER, produces and continuously improves SMOS sea surface salinity (SSS) products.The aim of this poster is to present the last version of CATDS L3 products developed by the LOCEAN CATDS Expertise Center (CEC-LOCEAN debiased v4, https://www.catds.fr/Products/Available-products-from-CEC-OS/CEC-Locean-L3-Debiased-v4), and to highlight its main improvements with respect to previous version 3.The L3 products are available for 9-day and 18-day Gaussian averaging. Both versions 3 and 4 contain a bias correction based on internal consistency of SMOS SSS retrieved in various locations across swath, and on seasonal variability of salinity. The main evolutions of version 4 consist in refining the absolute correction methodology, limiting wind speed to 16m/s, add a refined filtering for sea ice and radio frequency contamination based on SMOS retrieved pseudo dielectric constant, the so-called ACARD (Waldteufel et al. 2004) and an improved sea surface temperature (SST) correction in cold waters based on Dinnat et al. (2019) observed dependency.Improvements with respect to version 3 are assessed through systematic validation that consists in two main stages: (1) Comparison with respect to in-situ measurements (repetitive ship transects across Atlantic and Arctic regions, and Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) moorings); (2) Comparison with the In-Situ Analysis System (ISAS) monthly fields (Kolodziejczyk, 2017), in terms of both mean spatial maps and time series of key statistics parameters. The key statistics parameters are computed both over the global ocean and for individual areas of interest. Thus, both the mean spatial patterns and temporal variability in various regions are evaluated.Comparisons between the two last versions exposed in this poster are based on relevant examples from this systematic validation: main improvements are observed in high latitudes (over 45&#176; latitude).In the Southern Ocean modification of wind speed filtering and SST correction lead to a decrease in the mean difference between SMOS&#160; and ISAS SSS south of 45S from 0.16+/-0.07 to 0.02+/-0.05pss. Std of the differences and r2 are also improved over global ocean. Statistics obtained with this new version are close to the ones obtained with SMAP RemSS v4 SSS.&#160;Dinnat, E.P.; Le Vine, D.M.; Boutin, J.; Meissner, T.; Lagerloef, G. Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters. Remote Sens. 2019, 11, 750.Kolodziejczyk Nicolas, Prigent-Mazella Annaig, Gaillard Fabienne (2017). ISAS-15 temperature and salinity gridded fields. SEANOE. https://doi.org/10.17882/52367Waldteufel, P., J. L. Vergely, and C. Cot, A modified cardioid model for Processing multiangular radiometric observations, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.5, pp.1059-1063, 2004. DOI : 10.1109/TGRS.2003.821698.

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Seasonal and Interannual Variability of Sea Surface Salinity Near Major River Mouths of the World Ocean Inferred from Gridded Satellite and In-Situ Salinity Products

Remote Sensing ◽

10.3390/rs13040728 ◽

2021 ◽

Vol 13 (4) ◽

pp. 728 ◽

Cited By ~ 1

Author(s):

Severine Fournier ◽

Tong Lee

Keyword(s):

Soil Moisture ◽

World Ocean ◽

Sea Surface ◽

Sea Surface Salinity ◽

Spatial And Temporal Variability ◽

Surface Salinity ◽

River Plumes ◽

Major River ◽

River Mouths

Large rivers are key components of the land-ocean branch of the global water and biogeochemical cycles. River discharges can have important influences on physical, biological, optical, and chemical processes in coastal oceans. It is, therefore, of importance to routinely monitor the time-varying dispersal patterns of river plumes. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the NASA Soil Moisture Active Passive (SMAP) satellites provide Sea Surface Salinity (SSS) observations capable of characterizing the spatial and temporal variability of major river plumes. The main objective of this study is to examine the consistency of SSS products, from these two missions, and two in-situ gridded salinity products in depicting SSS variations on seasonal to interannual time scales within a few hundred kilometers of major river mouths. We show that SSS from SMOS and SMAP satellites have good consistency in depicting seasonal and interannual SSS variations near major river mouths. The two gridded in-situ products underestimate these variations substantially. This underestimation, most notably associated with the low SSS season following the high-discharge season, is attributable to the limited in-situ sampling of the river plumes when they are the most active. This work underscores the importance of using satellite SSS to study river plumes, as well as to evaluate and constrain models.

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