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VI Expanding Ocean Frontiers Conference, 5-7 July 2021, Barcelona, Spain

37 pages, 27 figures, 2 tables, 2 appendixes, Operated since the end of 2009, the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite mission is the first orbiting radiometer that collects regular and global observations from space of two Essential Climate Variables of the Global Climate Observing System: Sea Surface Salinity (SSS) and Soil Moisture. The National Aeronautics and Space Administration (NASA) Aquarius mission, with the primary objective to provide global SSS measurements from space operated from mid-2011 to mid-2015. NASA's Soil Moisture Active-Passive (SMAP) mission, primarily dedicated to soil moisture measurements, but also monitoring SSS, has been operating since early 2015. The primary sensors onboard these three missions are passive microwave radiometers operating at 1.4 GHz (L-band). SSS is retrieved from radiometer measurements of the sea surface brightness temperature (TB). In this paper, we first provide a historical review of SSS remote sensing with passive L-band radiometry beginning with the discussions of measurement principles, technology, sensing characteristics and complementarities of the three aforementioned missions. The assessment of satellite SSS products is then presented in terms of individual mission characteristics, common algorithms, and measurement uncertainties, including the validation versus in situ data, and, the consideration of sampling differences between satellite SSS and in situ salinity measurements. We next review the major scientific achievements of the combined first 10 years of satellite SSS data, including the insights enabled by these measurements regarding the linkages of SSS with the global water cycle, climate variability, and ocean biochemistry. We also highlight the new ability provided by satellites to monitor mesoscale and synoptic-scale SSS features and to advance our understanding of SSS' role in air-sea interactions, constraining ocean models, and improving seasonal predictions. An overview of satellite SSS observation highlights during this first decade and upcoming challenges are then presented, The EU authors acknowledge support of ESA in the frame of the SMOS ESL Level 2 phase 3 contract (https://smos.argans.co.uk/), the SMOS Pilot-Mission Exploitation Platform (Pi-MEP) project (https://www.salinity-pimep.org/), and the Climate Change Initiative (CCI) project (http://cci.esa.int/salinity). French authors thank the support of the Centre National d'Etudes Spatiales (CNES) in the frame of the Centre Aval de Traitement des Données SMOS (CATDS, http://www.catds.fr) and of CNES-TOSCA SMOS-Ocean projects. US authors thank the support of the NASA Ocean Surface Salinity Team (OSST). SMAP CAP SSS is produced at Jet Propulsion Laboratory. Thomas Meissner and Frank Wentz acknowledge funding from NASA (contracts no. NNG04HZ29C, NNH15CM44C, 80HQTR18C0015, and JPL sub-contract 1602331), With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

16th Specialist Meeting on on Microwave Radiometry and Remote Sensing of the Environment, 16-20 November 2020

Earth Observation for Water Cycle Science. 16-18 November 2020

2nd SMOS Science Conference, 25-29 May 2015, Madrid), Spain.-- 1 page

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2015), Remote Sensing: Understanding the Earth for a Safer World, 26-31 July 2015, Milan, Italy

SMOS+SOS Ocean Salinity Science and Salinity Remote Sensing Workshop, 26-28 november 2014, Exeter, United Kingdom.-- 2 pages, Remote sensing imagery of the ocean surface provides a synoptic view of mesoscale signatures from different ocean scalars advected by the oceanic flow. The most probable origin of the observed structures is the turbulent character of the oceanic flow as they slowly evolve and are very persistent over time scales compatible with ocean mesoscale dynamics. At spatial scales of kilometers, turbulence is mainly 2D, and a complex geometry, full of filaments and eddies of different sizes, emerges in remote sensing images of surface chlorophyll-a concentration (Chl-a) and sea surface salinity (SSS), as well as in the better resolved sea surface temperature (SST) and sea surface height (SSH). A fusion technique has been recently proposed to exploit these common turbulent signatures between variables. This technique is theoretically based on the geometrical properties of advected tracers [Turiel et al., 2005b]. Coherent vortices in a turbulent flow strongly interact, leading to permanently stretch and fold small-scale filaments ejected from vortex cores, and generate small-scale tracer gradients between eddies. Therefore the spatial structure of a tracer inherits some properties of the underlying flow. This geometrical arrangement of the flow is intimately linked to the energy cascade. A key point in this approach is the assumption of a multifractal structure in ocean images [Lovejoy et al., 2001]. It is assumed that singularity lines of ocean variables coincide [Umbert et al., 2014]. In turn, the gradient of both variables can be related by a smooth function. As a first and simple approach, the relating function is expressed as the identity, leading to a local regression scheme. This simple approach allows reducing the error and improving the coverage of the resulting Level 4 product of one variable using another variable as a template. Moreover, information about the statistical relationship between the two fields can also be obtained. This methodology is been applied to Aquarius SSS using SSH from AVISO as template in the Gulf Stream. Resulting SSS Level 4 product contain the mesoscale structures seen by SSH and a significant reduction of the uncertainty

The Climate Symposium 2014, 13-17 October 2014, Darmstadt, Germany.-- 1 page, Remote sensing imagery of the ocean surface provides a synoptic view of mesoscale signatures from different ocean scalars advected by the oceanic flow. The most probable origin of the observed structures is the turbulent character of the oceanic flow. At spatial scales of kilometers, turbulence is regarded as a two-dimensional phenomenon, with a complex geometry. Such complexity emerges in remote sensing images as filaments and eddies of different sizes. This is seen in images of surface chlorophyll-a concentration (Chl-a) and sea surface salinity (SSS), as well as the betterresolved sea surface temperature (SST) and sea surface height (SSH). A fusion technique has been recently proposed to exploit these common turbulent signatures among the different variables. This technique is based on the hypothesis that the spatial structure of a tracer inherits some properties of the underlying flow [Turiel et al., 2005]. This yields an organized geometry of the flow as a hierarchy of fractal structures, called singularity manifolds, each of them associated with a singularity exponent. This is the so-called multifractal formalism for fully developed turbulence [Lovejoy et al., 2001]. Such geometrical arrangement of the flow is intimately linked to the energy cascade. By assuming that the singularity lines of the different ocean variables coincide,, the gradient of two variables should be related through a smooth function [Umbert et al., 2013]. In a first approach, the function is expressed as the identity, leading to a local regression scheme. When applied to SMOS SSS data, this simple approach already allows a significant noise reduction and coverage improvement of the resulting Level 4 product using OSTIA SST fields as a template [Umbert et al., 2013]. Moreover, information about the spatial relationship between the two fields can also be obtained. This methodology is now applied to daily Aqua MODIS Level-3 chlorophyll maps using MODIS SST maps as template, and to Aquarius SSS using SSH from AVISO as template. The resulting Chl-a and SSS maps contain the mesoscale structures seen in SST and SSH maps, exhibit a significant reduction of the uncertainty, and allow extrapolation to cloud-affected areas. This technique sets the grounds for reprocessing long time series of remote sensing derived parameters, by exploiting the information content of each variable to obtain spatial and temporally consistent datasets while preserving the turbulent energy cascade. Since this approach does not require the use of any background information (e.g., numerical models) can be used to improve ocean climate data records

IV Congress of Marine Sciences, Encuentro de la Oceanografía Física Española (EOF 2014), 11-13 June 2014, Las Palmas de Gran Canaria.-- 1 page, Remote sensing imagery of the ocean surface provides a synoptic view of mesoscale signatures from different ocean scalars advected by the oceanic flow. The most probable origin of the observed structures is the turbulent character of the oceanic flow as they slowly evolve and are very persistent over time scales compatible with ocean mesoscale dynamics. At spatial scales of kilometers, turbulence is regarded as a two-dimensional phenomenon, with a complex geometry. Such complexity emerges in remote sensing images as filaments and eddies of different sizes. This is seen in images of surface chlorophyll-a concentration (Chl-a) and sea surface salinity (SSS), as well as the better-resolved sea surface temperature (SST) and sea surface height (SSH). A fusion technique has been recently proposed to exploit these common turbulent signatures between variables. This technique is theoretically based on the geometrical properties of advected tracers [Turiel et al., 2005b]. Coherent vortices in a turbulent flow strongly interact, leading to permanently stretch and fold small-scale filaments ejected from vortex cores, and generate small-scale tracer gradients between eddies. Therefore the spatial structure of a tracer inherits some properties of the underlying flow. This leads to an organized geometry of the flow as a hierarchy of fractal sets, called singularity manifolds, each one of them associated to a singularity exponent; this is the so-called multifractal formalism for fully developed turbulence. This geometrical arrangement of the flow is intimately linked to the energy cascade. A key point in this approach is the assumption of a multifractal structure in ocean images [Lovejoy et al., 2001]. It is assumed that singularity lines of ocean variables coincide [Umbert et al., 2013]. In turn, the gradient of both variables can be related by a smooth function. As a first and simple approach, the relating function is expressed as the identity, leading to a local regression scheme. This simple approach allows reducing the error and improving the coverage of the resulting Level 4 product of one variable using another variable as a template. Moreover, information about the statistical relationship between the two fields can also be obtained. This methodology is been applied to daily Aqua MODIS Level-3 chlorophyll maps using MODIS SST maps as template, to SMOS SSS using OSTIA SST as template, and to Aquarius SSS using SSH from AVISO as template. Resulting SSS and Chl-a Level 4 products contain the mesoscale structures seen in SST and SSH maps, exhibit a significant reduction of the uncertainty, and allow extrapolation to cloud-affected areas

European Geosciences Union General Assembly 2014 (EGU2014), 27 april - 2 may 2014, Vienna, Austria.-- 1 page, The European Soil Moisture and Ocean Salinity (SMOS) mission, aimed at observing sea surface salinity (SSS) from space, has been launched in November 2009. The L–band frequency (1413 MHz) has been chosen as a tradeoff between a sufficient sensitivity of radiometric measurements to changes in salinity, a high sensitivity to soil moisture and spatial resolution constraints. It is also a band protected against human-made emissions. But, even at this frequency, the sensitivity of brightness temperature (TB) to SSS remains low requiring accurate correction for other sources of error. Two significant sources of error for retrieved SSS are the uncertainties on the correction for surface roughness and sea surface temperature (SST). One main geophysical source of error in the retrieval of SSS from L-band TB comes from the need for correcting the effect of the surface roughness and foam. In the SMOS processing, the wind speed (WS) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) is used to initialize the retrieval process of WS and Sea Surface Salinity (SSS). This process compensates for the lack of onboard instrument providing a measure of ocean surface WS independent of the L-band radiometer measurements. Using multi-angular polarimetric SMOS TBs, it is possible to adjust the WS from the initial value in the center of the swath (within 300km) by taking advantage of the different sensitivities of L-band H-pol and V-pol TBs to WS and SSS at various incidence angles. As a consequence, the inconsistencies between the MIRAS sensed roughness and the roughness simulated with the ECMWF WS are reduced by the retrieval scheme but they still lead to residual biases in the SMOS SSS. We have developed an alternative two-step method for retrieving WS from SMOS TB, with larger error on prior ECMWF wind speed in a first step. We show that although it improves SSS in some areas characterized by large currents, it is more sensitive to SMOS TB errors in the vicinity of coasts. The SST used in the SMOS SSS retrievals is from ECMWF Meteorological Archival and Retrieval System (MARS) archive which uses Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) SST. There are noticeable differences between the OSTIA SST and Reynolds SST product derived from satellite and in situ SST. We estimate the SMOS SSS uncertainties due to uncertainties in SST and WS, especially in the tropical Pacific Ocean where there are significant and sometimes coupled variations of SST and WS due to strong seasonal upwelling, zonal surface currents and the development of tropical instability waves

17th Ocean Sciences Meeting, 23-28 February 2014, Honolulu, Hawaii USA, Thanks to new remote sensing platforms SMOS and Aquarius we have access to synoptic maps of Sea Surface Salinity (SSS). Much effort is still under way to bring both missions to meet pre-launch requirements on the quality of SSS. In this work we explain a new technique to improve the quality of SSS maps at Level 4, by combining SMOS/Aquarius data with high quality maps of Sea Surface Temperature (SST). We use singularity analysis for the assessment of the structure of ocean flows at submesoscale and larger scales. Visual correspondence of eddies and fronts in images of different variables can be expressed as the correspondence of singularity exponents associated to each variable. Scalars having the same singularity exponents have a local functional dependence that is approximated by local linear regressions around each point. This simple algorithm to reduce noise and increase the resolution has been applied to SMOS and Aquarius data. Redundancy between ocean scalars opens the use of remote sensed sea surface salinity data for new applications, including the instant identification of ocean fronts, rain lenses, hurricane tracks, etc

European Geosciences Union General Assembly 2014 (EGU2014), 27 april - 2 may 2014, Vienna, Austria.-- 1 page, The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission was launched in November 2009, carrying onboard the MIRAS instrument, a novel fully-polarimetric L-band radiometer which estimates the surface brightness temperature (TB) by means of two-dimensional aperture synthesis interferometry. In June 2011, the National Aeronautics and Space Administration (NASA) and Argentina’s Space Agency (CONAE) launched the Aquarius/SAC-D mission carrying onboard an L-band real aperture radiometer together with an L-band scatterometer. These two missions provide global coverage of sea surface salinity (SSS) with different repetition rates, spatial resolutions and accuracies. While SMOS has a wider coverage and higher spatial resolution, Aquarius has higher radiometric accuracy. To achieve the challenging mission requirements at weekly (0.1 psu at 200 x 200 km resolution) and monthly (0.1 psu at 100 km x 100 km resolution) scales, fusion of SMOS and Aquarius SSS is required. A prerequisite for a successful data fusion is to perform a comprehensive intercalibration of the different SSS data sources. The SMOS and Aquarius instrument concepts are very different and, as such, we expect different calibration strategies as well as different impact of external noise contaminations (e.g., Sun, land-sea contamination, radio frequency interference, etc.). These differences will of course produce differences in the SMOS and Aquarius SSS retrievals. Despite these differences, both instruments measure the brightness temperature of the ocean surface at the same frequency (1.41 GHz) and polarizations (except for the Stokes 4 parameter which is not measured by Aquarius). As such, the theoretical relation between the brightness temperature and the different sea surface geophysical parameters (including SSS) is the same for both missions. In consequence, one would expect that by doing proper calibration and external noise corrections/filtering, SMOS and Aquarius SSS could be straightforwardly merged. However, this is not true since SMOS and Aquarius SSS retrieval algorithms differ and such differences lead to non-negligible differences in the derived SSS maps. This can be shown by simply analyzing the differences between the different products (i.e., different SSS retrieval algorithms) available for each mission separately. In this work, a thorough assessment of the impact of using different auxiliary data (e.g., sea surface winds: ECMWF, NCEP, Aquarius scatterometer; sea surface temperature: Reynolds, OSTIA), different forward models (galactic, dielectric constant, and roughness models), and different retrieval approaches (multiparametric Bayesian inversion, direct retrievals by forward propagation to TB corrections for TEC, galaxy, and roughness) on the final SSS maps is carried out. This analysis sets the grounds for an optimal fusion of SMOS and Aquarius SSS data

Earth Observation for Ocean-Atmosphere Interactions Science 2014, Responding to the new scientific challenges of SOLAS, 28-31 October 2014, Frascati, Italy

American Geophysical Union (AGU) Fall Meeting, 9-13 December 2013, San Francisco, Thanks to new remote sensing platforms as SMOS and Aquarius we have now access to synoptic maps of Sea Surface Salinity (SSS) at global scale. Both missions require a non-negligible amount of development in order to meet pre-launch requirements on the quality of the retrieved variables. Development efforts have been so far mainly concentrated in improving the accuracy of the acquired signals from the radiometric point of view, which is a point-wise characteristic, that is, the qualities of each point in the snapshot or swath are considered separately. However, some spatial redundancy (i.e., spatial correlation) is implicit in geophysical signals, and particularly in SSS. This redundancy is known since the beginning of the remote sensing age: eddies and fronts are visually evident in images of different variables, including Sea Surface Temperature (SST), Sea Surface Height (SSH), Ocean Color (OC), Synthetic Aperture Radars (SAR) and Brightness Temperatures (BT) at different bands. An assessment on the quality of SSS products accounting for this kind of spatial redundancy would be very interesting. So far, the structure of those correlations have been evidenced using correlation functions, but correlation functions vary from one variable to other; additionally, they are not characteristic to the points of the image but to a given large enough area. The introduction of singularity analysis for remote sensing maps of the ocean has shown that the correspondence among different scalars can be rigorously stated in terms of the correspondence of the values of their associated singularity exponents. The singularity exponents of a scalar at a given point is a unitless measure of the degree of regularity or irregularity of this function at that given point. Hence, singularity exponents can be directly compared disregarding the physical meaning of the variable from which they were derived. Using singularity analysis we can assess the quality of any scalar, as singularity exponents align in fronts following the streamlines of the flow, while noise breaks up the coherence of singularity fronts. The analysis of the output of numerical models show that up to the numerical accuracy singularity exponents of different scalars take the same values at every point. Taking the correspondence of the singularity exponents into account, it can be proved that two scalars having the same singularity exponents have a relation of functional dependence (a matricial identity involving their gradients). That functional relation can be approximated by a local linear regression under some hypothesis, which simplifies and speeds up the calculations and leads to a simple algorithm to reduce noise on a given ocean scalar using another higher- quality variable as template. This simple algorithm has been applied to SMOS data with a considerable quality gain. As a template, high-level SST maps from different sources have been used, while SMOS L2 and L3 SSS maps, and even brightness temperature maps play the role of the noisy data to be corrected. In all instances the noise level is divided by a factor of two at least. This quality gain opens the use of SMOS data for new applications, including the instant identification of ocean fronts, rain lenses, hurricane tracks, etc

European Space Agency (ESA) Living Planet Symposium, 9-13 September 2013, Edinburgh

SMOS & Aquarius Science Workshop, 15-17 April 2013, Brest, France, The Soil Moisture and Ocean Salinity (SMOS) satellite was launched on November 2, 2009 in the framework of the European Space Agency's (ESA's) Earth Explorer opportunity missions. Over the oceans, Sea Surface Salinity (SSS) is retrieved on a global basis with a spatio-temporal sampling appropriate for Ocean dynamics and Earth water cycle studies (Font 2010). The single payload onboard SMOS is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), a novel fully-polarimetric L-band radiometer which estimates the brightness temperature by means of two-dimensional aperture synthesis interferometry. It consists of a Y-shaped set of 72 receivers (McMullan, 2008). More than 3 years after launch, the salinity product accuracy has still not reached the mission objective, even in the RFI-free open ocean domain. Main reasons are: 1) the challenging but intrinsically low sensitivity of L-band brightness temperature to sea surface, 2) the imperfection of the forward model used in the inversion procedure, 3) the spatio-temporal biases still present in the reconstructed brightness temperature. The present work is a contribution to adressing above-mentioned points 2 and 3. Several forward model deficiencies have been identified which propagate down to the retrieved salinity. If different studies have recently pointed out roughness dependent SSS errors and proposed updated formulations for the increment of emissivity due to surface roughness (Guimbard et al., 2012; Yin et al., 2012), the agreement in their results suggests that a robust improvement has been achieved. Nevertheless, another critical component of the forward model is the celestial signal scattered by the rough sea surface (Tenerelli 2008). The complexity of the biscattering problem and the large number of parameters involved makes highly difficult the procedure to improve its description empirically from real data. In spite of this, a recent work by J.Tenerelli has produced very promising results. The amplitude of the modeled signal near the specular direction is improved and better mimics the changes due to surface roughness variations. Nevertheless, there is still some discrepancy between parameters obtained when using different datasets, especially when using ascending or descending passes, and between different geometrical observation conditions i.e. incidence angle. Such inconsistency in the model parameters suggest an imperfection of the model physics. As mentioned in the introduction, latitudinal and seasonal biases are also affecting SMOS reconstructed TB ocean images (Tenerelli et al. 2010, Oliva et al. 2012) and retrieved salinity fields (Reul et al. 2012). Results suggest a correlation of the error with the sun illumination of the instrument through thermal effects, but attempts to cancel the corresponding biases at the calibration level are still not conclusive. In this work, it is assumed that such biases are essentially uniform across the field of view., A key point in this discussion is that celestial reflection model errors and thermal instrumental biases both vary at latitudinal and seasonal scales. In the current approach, forward model updates are contaminated by the imperfect instrumental biases estimates and vice versa. The present work is an attempt to uncouple these two important steps. First, for a specific data subset where the celestial reflection signal is expected to be time-invariant, the temporal biases are estimated, an empirical correction applicable to the brightness temperatures is derived and a corrected data subset obtained. Second, the corrected dataset is used to obtain celestial reflection residuals. Their inconsistency with the current galactic model, primarily in terms of incidence angle dependence is analyzed to derive a modification of the model. Finally, after evaluating its performance, the updated model is evaluated for a much larger dataset and the instrumental biases are now evaluated both at the temporal and orbital scales. For a given latitudinal band, i.e. orbital position, and a limited set of locations in the FOV, a specific geometrical configuration is identified for which the celestial contamination does not significantly vary along the year. A data selection strategy developed for the antenna frame systematic errors study (Gourrion et al., 2012) is refined to characterize the instrumental temporal biases in that particular latitudinal range. Assuming that the thermally-induced instrumental biases are homogenous across the FOV, celestial reflection residuals are derived from a wide range of FOV locations but the same orbital location. Their analysis points out an imperfection in the shape of the bistatic scattering coefficients used in the computation of the celestial signal as scattered by the rough sea surface. Both theoretically-based and empirical ad-hoc modifications are tested to propose a modification of the bistatic scattering function

SMOS & Aquarius Science Workshop, 15-17 April 2013, Brest, France, In this study, a simplified parallel version of the operational Soil Moisture and Ocean Salinity (SMOS) Level 2 data processor (L2OS) is used to assess the optimal configuration of both the SMOS Bayesian-based cost function and the corresponding Levenberg-Marquardt based multi-parametric minimization scheme for sea surface salinity (SSS) and sea surface wind speed (U10) retrievals. Within such a framework, realistically simulated brightness temperature measurements (TBs) and a post-launch derived semi-empirical forward model (FM) are used. As already anticipated in pre-launch studies, the results carried out in this work demonstrate that the SMOS cost function needs to be constrained by a priori information on the sea-surface related geophysical parameters, due to the low TB sensitivity with respect to both SSS and U10 changes. Furthermore, the present study, in which a variety of marine scenarios are simulated, confirms that the operationally used SMOS L2OS cost function configuration is optimal for SSS retrieval purposes. As a novelty, this study shows that the optimal SMOS cost function configuration for U10 retrievals is different from the one used for SSS retrieval purposes. This is mainly due to the TB sensitivity to SSS changes being different to the TB sensitivity to U10 changes. Current work focuses on the verification of the simulation results with SMOS real data, using collocated in situ observations and numerical weather prediction model output. The most relevant results of this study will be presented at the workshop

SMOS & Aquarius Science Workshop, 15-17 April 2013, Brest, France, The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission has provided nearly continuous global record of fully polarimetric brightness temperatures at L-band (1.4135 GHz) since November 2009. The single payload of the SMOS satellite, MIRAS, is a two-dimensional aperture synthesis radiometer that measures the cross-correlations between the signals from many L-band antennas distributed in a Y-shape array. These cross-correlations are transformed by ground processing into brightness temperature images that extend over a swath several hundred kilometers across. Over the ocean, these brightness temperature images are used, together with a forward model of the L-band scene brightness, to derive maps of surface salinity over the global oceans, with full earth coverage approximately every five days. Over the global oceans the surface salinity varies between about 32 and 38 on the practical salinity scale, with the strongest variations in the vicinity of river outflows and heavy rainfall. The sensitivity of the brightness temperature at L-band to a change in salinity depends somewhat upon polarization and sea surface temperature but, in tropical latitudes, is about +1 K in the first Stokes parameter per unit decrease of salinity on the pratical salinity scale. Thus, the dynamic range of L-band brightness temperatures over the open ocean is only several kelvin. As one goal of the mission is to produce global maps of salinity with an accuracy of 0.1 after averaging over 10-30 days, strict requirements must be placed upon the accuracy and stability of the brightness temperatures. Efforts to reach this goal continue, but challenges related to interannual, seasonal, and orbital stability of the retrieved salinity remain. These challenges stem from difficulties in the instrument calibration, image reconstruction, and modeling of the scene brightness over the ocean. On the one hand, the instrument calibration and image reconstruction are plagued by the sun which impacts the accuracy of the brightness temperatures indirectly, through variations in the thermal characteristics of the instrument, and directly, through its impact on the visibilities. On the other hand, the scene modeling is plagued by emission from the rough ocean surface, emission from foam, and galactic radiation scattered towards the instrument by the wind-roughened ocean surface. Moreover, the sun-synchronous orbit of the SMOS satellite is such that both the solar (direct and indirect) and galactic impacts exhibit orbital and seasonal cycles that, if not properly accounted for, will contribute to bias in the salinity. A key factor complicating progress is the fact that the aforementioned problems can produce similar bias evolutions, and so disentangling the various sources of bias is difficult. Using open-ocean model solutions for the brightness temperature images as well as the antenna temperatures (which provide the mean brightness temperature level for the images), this paper will examine the spatial and temporal structures observed in the biases over the nearly four years of continuous data. An attempt will be made to exploit the recent oscillatory character of the sun L-band brightness in order to separate the impacts of the sun and scattered galactic radiation. In parallel, improvements in the modeling of the scattering of galactic radiation will be presented, and a comparison will be made with the impact on the brightness temperatures and salinity maps from the Aquarius mission. Finally, recognizing that adequate calibration and forward scene modeling may not be achieved in the near future, the paper will examine practical alternatives to bias correction, with an emphasis on finding an approach that minimizes impact on the range of applications of the SMOS salinity maps

SMOS & Aquarius Science Workshop, 15-17 April 2013, Brest, France, The retrieval of Sea Surface Salinity (SSS) with SMOS and Aquarius missions is very challenging, due to the limited sensibility of brightness temperatures in L-band to SSS. However, salinity is one of the worst known Essential Climate Variables and its acquisition is crucial for the full understanding of the water cycle and utterly of Earth's climate system. Errors in SMOS and Aquarius acquisitions are of diverse origin: incorrect geophysical modeling, systematic antenna patterns, galactic noise, land-sea contamination, RFI sources, seasonal biases... In spite of all those problems the quality of SSS retrieval has increased steadily since the beginning of the missions. Having two independent missions working with two different technologies has been beneficial in improving the quality of the maps of SSS. For instance, intercomparison of SSS maps from the two missions is very useful for the characterization of errors and biases. The question is hence posed of how to produce SSS maps of the best quality by an appropriate combination of both missions. A simple point-wise average of SMOS and Aquarius maps leads to a small reduction of error, of order 2 on the variance (assuming that errors in SMOS and Aquarius are small). Point-wise average, however, is not capable to appropriately tackle with biases. In fact, point-wise averages only makes sense if errors are supposed to be spatially independent, and that the signals have no particular spatial structure. However, none of those two hypotheses are correct in general. As SSS maps are strongly spatially correlated, the correlation matrix can be used to reduce the noise level. This is the basis for the Optimal Interpolation (OI) method. The main drawback associate to OI is the necessity of having good prior estimates of the background term and of the spatial correlation matrices what, in turn, implies having large prior datasets of SSS maps - what we are lacking of, in fact. Taking profit of having two independent estimates of SSS, as SMOS and Aquarius, can only be profited if we include the mutual correlation matrix - also very demanding in data. Alternative ways to improve the quality of SSS maps are use singularity-based fusion methods, but in that case we need a third variable of high quality (not necessarily of the same type: high resolution SST is usually employed to this end). Having two independent variables such as the ones provided by SMOS and Aquarius allows for a different approach. If errors in SMOS and Aquarius were spatially decorrelated the optimal estimate of SSS would be the point-wise average of both variables, while the error on each of them will be given by half the difference of both SSS's, that we will call point-wise error estimate. However, the point-wise error estimate has significant two-point spatial correlations which decays like a power law with distance, what means that errors are correlated over significant distances. In this presentation, we will show a method for obtain a better estimate of SSS by jointly minimizing error correlation on SMOS and on Aquarius which improves point-wise averages and gives more spatially consistent maps. We will discuss the role of biases in the minimization and how this can be employed to enhance the intercomparison

European Space Agency (ESA) Living Planet Symposium, 9-13 September 2013, Edinburgh, Due to their relatively rapid motion and low dynamic viscosity, geophysical fluids such as the atmosphere and the oceans are turbulent, although the particular regime of turbulence depends on the scale at which the fluid is studied. In the case of oceans, at scales of meters and below turbulence is mainly 3D with strong vertical displacements across the mixed layer, while at scales of kilometers and above turbulence is mainly 2D - except at some specific areas such as upwelling zones- and shows up as a complicated pattern of filaments, meanders and eddies. The fingerprint of those structures can be recognized in remote sensing images of different types, such as chlorophyll concentration (CC), sea surface temperature (SST), sea surface salinity (SSS) or sea surface height (SSH). The observed spatial redundancy among the different scalars suggests that a synergistic approach could be used to study and analyze ocean variables. In particular, synergy could be used to improve the signal-to-noise ratio of corrupted variables by using information from other variables or to infer missing values. However, it is very complicated to give a mathematical formalism leading to practical algorithms. In the last years a new formalism has arisen to systematize the concept of scalar synergy: that of Microcanonical Multifractal Formalism (MMF). According to MMF, any scalar variable submitted to the action of a turbulent flow develops a structure of singular fronts, reminiscent of the streamlines of the flow, that can be extracted from any ocean scalar as a field of dimensionless variables, the singularity exponents. The validity of MMF has been verified on remote sensing images of different types, included ocean leaving radiances, SST, CC or SSH. As singularity exponents have no dimensions, singularity exponents coming from variables of different type can be directly compared, and in fact must coincide for the reasons exposed above. Not only that: an algorithm for merging the information of variables of different type has been devised using this common information. With this algorithm, a noisy variable can be improved by merging it with a higher-quality variable of a different type. In this work we show the application of this algorithm for a different goal: the extrapolation of chlorophyll maps to missing areas by using SST as a template. This extrapolation provides significant results in open ocean without assuming any parametrization and warrants that the extrapolated field is consistent with the observed ocean structures

1 page, figures, 1. SPURS: Salinity Processes in the Upper ocean Regional Study. An international program in 2012-2013 to understand the processes responsible for the formation and maintenance of the salinity maximum associated to the North Atlantic subtropical gyre. http://spurs.jpl.nasa.gov/SPURS

European Space Agency (ESA) Living Planet Symposium, 9-13 September 2013, Edinburgh

VII Riunione Annuale CeTeM-AIT sul telerilevamento a Microonde: sviluppi scientifici ed implicazioni tecnologiche, 4-5 dicembre 2012, Bari, The Soil Moisture and Ocean Salinity (SMOS) mission is one of the European Space Agency (ESA) Earth Explorer Opportunity Missions, which was proposed in 1998 within the ESA Living Planet Program. It was launched in November 2009 with the purpose to provide global maps of both soil moisture (SM) and sea surface salinity (SSS) with both spatial and temporal resolutions adequate for climate and ocean general circulation studies. The payload embarked on SMOS includes the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), which is an L-band fullpolarized 2-Dimensional (2-D) interferometric radiometer able to use the aperture synthesis to obtain high spatial resolution over a large swath. It provides 2-D images of the ocean-surface brightness temperature (TB) for each overpass with a multi-angular imaging capability to observe the same point on Earth from a wide range of incidence angles, which is crucial for a successful retrieval of SSS. It must be pointed out that TB measurements acquired at Lband over the ocean are mainly modulated by three geophysical variables, i.e. the SSS, the sea surface temperature (SST), and the sea surface roughness (SSR). As a matter of fact, the operational SMOS MIRAS TB measurements can be properly used in a multi-parametric inversion scheme to retrieve not only the SSS, but at the same time SST and sea surface roughness related parameters such as the sea surface wind speed (WS). The retrieval of both SSS and other interesting geophysical parameters (e.g. SST, WS, friction velocity or other roughness descriptors, etc.) is accomplished in the Data Product Generation System (DPGS) by means of the SMOS Level 2 Salinity Prototype Processor (L2PP), which provides consistent retrievals of the above-mentioned parameters by efficiently processing geo-located TBs provided at the SMOS Level 1C (L1C) after an image reconstruction step. The retrieval is accomplished by properly inverting a Bayesian-based cost function through the Levenberg-Marquardt (LM) multi-parametric minimization procedure. It has been demonstrated that a suitable definition of the Bayesian-based cost function is very important to obtain good performances for the retrieval of SSS, SST and WS from SMOS TB measurements. In this paper, an assessment study on the optimal configuration of both the SMOS maximum-likelihood Bayesian-based cost function and the corresponding LM-based multi-parametric inversion scheme is accomplished over realistically simulated TB measurements to develop a parallel simplified version of the SMOS DPGS inversion scheme for the effective retrieval of both SSS and WS. Within such a framework, a review of the cost function weights as well as the minimization strategy is carried out to fully exploit both the SSS and the WS content of the SMOS data. Current work focuses on the verification of the simulation results with SMOS real data, collocated with in-situ observations and numerical weather prediction model output. The most relevant results of this study will be presented at the conference

AGU Fall Meeting 3–7 December 2012, San Francisco, California, In the context of the ESA SMOS and Aquarius/SAC-D missions, sea surface thermal emission in L-band is measured from space since almost 3 and 1 years respectively. Using a new instrumental concept for the first one, a Microwave Imaging Radiometer using two dimensional aperture synthesis, sea surface brightness temperatures can be extracted under a large range of incidence and azimuthal angles, and different spatial resolutions. Although using a more classical real aperture radiometer, Aquarius derives its originality from measuring quasi simultaneously active and passive signals for three different incidence and azimuthal angles. A lot of works have been done the last ten years to develop physically based scattering models in order to accurately predict the sea surface roughness impact on brightness temperatures. Recent results have shown that non negligible discrepancies exist between these models and this new data set of L-band measurements. This presentation will provide a review of the last empirical adjustments of radar normalized cross sections and sea surface brightness temperatures modulations by different sea surface roughness characteristics. A discussion regarding possible improvements in electromagnetic scattering modeling and inherent sea surface spectral description will be proposed

Capability for sea surface salinity observation was an important gap in ocean remote sensing in the last few decades of the 20th century. New technological developments during the 1990s at the European Space Agency led to the proposal of SMOS (Soil Moisture and Ocean Salinity), an Earth explorer opportunity mission based on the use of a microwave interferometric radiometer, MIRAS (Microwave Imaging Radiometer with Aperture Synthesis). SMOS, the first satellite ever addressing the observation of ocean salinity from space, was successfully launched in November 2009. The determination of salinity from the MIRAS radiometric measurements at 1.4 GHz is a complex procedure that requires high performance from the instrument and accurate modelling of several physical processes that impact on the microwave emission of the ocean’s surface. This paper introduces SMOS in the ocean remote sensing context, and summarizes the MIRAS principles of operation and the SMOS salinity retrieval approach. It describes the Spanish SMOS high-level data processing centre (CP34) and the SMOS Barcelona Expert Centre on Radiometric Calibration and Ocean Salinity (SMOS-BEC), and presents a preliminary validation of global sea surface salinity maps operationally produced by CP34., La imposibilidad de observar la salinidad superficial del mar desde el espacio era uno de los problemas importantes para la teledetección oceánica a finales del siglo XX. Nuevos desarrollos tecnológicos durante los años 90 en la Agencia Espacial Europea llevaron a formular la propuesta de SMOS (Soil Moisture and Ocean Salinity), una misión de oportunidad de observación de la Tierra basada en un radiómetro interferométrico de microondas, MIRAS (Microwave Imaging Radiometer with Aperture Synthesis). SMOS, el primer satélite que intenta observar la salinidad desde el espacio, fue lanzado en noviembre de 2009. La determinación de salinidad a partir de las mediciones de MIRAS a 1,4 GHz es un procedimiento complejo que requiere un excelente funcionamiento del instrumento y una modelización muy precisa de los procesos físicos que intervienen en la emisión de la superficie del océano en el dominio de las microondas. Este artículo presenta SMOS en el contexto de la teledetección oceánica, y resume los principios de operación de MIRAS y el enfoque utilizado en SMOS para obtener la salinidad. Describe el Centro español de Proceso de datos SMOS de alto nivel (CP34) y el Centro Experto SMOS en Barcelona para Calibración Radiométrica y Salinidad Oceánica (SMOS-BEC), así como presenta una primera validación de los mapas globales de salinidad superficial producidos operacionalmente por el CP34.

IEEE International Geoscience and Remote Sensing Symposium (IGARSS): Remote Sensing for a Dynamic Earth, 22-27 July 2012, Munich, Germany

IEEE International Geoscience and Remote Sensing Symposium (IGARSS): Remote Sensing for a Dynamic Earth, 22-27 July 2012, Munich, Germany

7ma Reunión de Ciencia SAC-D/Aquarius, del 11 al 13 de Abril de 2012, Buenos Aires

Font, Jordi ... et. al.-- SMOS-MODE Annual Workshop, 28-30 March 2012, Frascati, Italy

12th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad), 5-9 March 2012, Villa Mondragone

European Geosciences Union General Assembly 22-27 April 2012, Vienna, Austria.-- 1 page, The Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument onboard the Soil Moisture and Ocean Salinity (SMOS) mission was launched on November 2nd, 2009 with the aim of providing, over the oceans, synoptic sea surface salinity (SSS) measurements with spatial and temporal coverage adequate for large-scale oceanographic studies. For each single satellite overpass, SSS is retrieved after collecting, at fixed ground locations, a series of brightness temperature from successive scenes corresponding to various geometrical and polarization conditions. SSS is inversed through minimization of the difference between reconstructed and modeled brightness temperatures. To meet the challenging mission requirements, retrieved SSS needs to accomplish an accuracy of 0.1 psu after averaging in a 10- or 30-day period and 2ºx2º or 1ºx1º spatial boxes, respectively. It is expected that, at such scales, the high radiometric noise can be reduced to a level such that remaining errors and inconsistencies in the retrieved salinity fields can essentially be related to (1) systematic brightness temperature errors in the antenna reference frame, (2) systematic errors in the Geophysical Model Function – GMF, used to model the observations and retrieve salinity – for specific environmental conditions and/or particular auxiliary parameter values and (3) errors in the auxiliary datasets used as input to the GMF. The present communication primarily aims at adressing above point 1 and possibly point 2 for the whole polarimetric information i.e. issued from both co-polar and cross-polar measurements. Several factors may potentially produce systematic errors in the antenna reference frame: the unavoidable fact that all antenna are not perfectly identical, the imperfect characterization of the instrument response e.g. antenna patterns, account for receiver temperatures in the reconstruction, calibration using flat sky scenes, implementation of ripple reduction algorithms at sharp boundaries such as the Sky-Earth boundary. Data acquired over the Ocean rather than over Land are prefered to characterize such errors because the variability of the emissivity sensed over the oceanic domain is an order of magnitude smaller than over land. Nevertheless, characterizing such errors over the Ocean is not a trivial task. Even if the natural variability is small, it is larger than the errors to be characterized and the characterization strategy must account for it otherwise the estimated patterns will unfortunately vary significantly with the selected dataset. The communication will present results on a systematic error characterization methodology allowing stable error pattern estimates. Particular focus will be given to the critical data selection strategy and the analysis of the X- and Y-pol patterns obtained over a wide range of SMOS subdatasets. Impact of some image reconstruction options will be evaluated. It will be shown how the methodology is also an interesting tool to diagnose specific error sources. Criticality of accurate description of Faraday rotation effects will be evidenced and latest results about the possibility to infer such information from full Stokes vector will be presented

The 44th International Liege Colloquium on Ocean Dynamics. Remote sensing of colour, temperature and salinity – new challenges and opportunities, 7-11 May 2012, Liège, University Campus, Since November 2nd, 2009 and June 10, 2011, two spatial missions give us the ability to measure sea surface salinity (SSS) from space. The Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument onboard the Soil Moisture and Ocean Salinity (SMOS) mission [Font et al. 2004] and a 3 feed horn radiometer onboard the Aquarius mission [Le Vine et al. 2007]. These two missions provide global coverage SSS products with different repetition rates, spatial resolutions and accuracies. The complexity of SMOS measurements, the amount of external contaminations at L-Band (sun, galaxy, ionosphere, radio frequency interferences...), the different SSS retrieval algorithms and auxiliary data sources used by SMOS and Aquarius, will certainly give non negligible differences in term of final SSS product. In order to be able to interpret these observed differences, different strategies can be investigated including spatio-temporal averaging technics. This kind of approach is investigated here on the sea surface brightness temperature rather than on the SSS in order to be able to have a consistent SSS retrieval algorithm and sea surface related auxiliary parameters between SMOS and Aquarius. A subset of SMOS sea surface brightness temperature in the same incidence angle configuration as Aquarius is first considered. An Aquarius and SMOS level 2 sea surface brightness temperature dataset for three incidence angles (i=1,2,3 =28.7◦, 37.8◦, 45.6◦) , spatially averaged in a regular grid of 1◦ ×1◦ and temporally averaged over a month, is built for the last 4 months of 2011. A new retrieval algorithm is developed and apply to this new product to get the SSS. This comparison study gives the opportunity to highlight possible instrumental biases and focus on possible issues regarding galactic and ionospheric signal corrections. Results with new auxiliary data sources like sea surface temperature (SST) or wind speed retrieved by other microwave satellites, which should be more accurate than NCEP or ECMWF model predictions, will be presented. The scatterometer on board Aquarius that give viable additional informations of the sea surface roughness contribution at L-band and the link between active/passive measurements will be investigated. As an example, a comparison of the sea surface roughness induced brightness temperature as seen by SMOS and Aquarius for different polarization states will be presented

Font, Jordi ... et al.-- 1st SMOS Science Workshop, 27-29 September 2011, Arles, France.-- 4 pages, 2 figures, The SMOS (Soil Moisture and Ocean Salinity) objectives for sea surface salinity (SSS) are to provide global coverage with repetition rate and accuracy adequate for oceanographic, climatological and hydrological studies and increase the present knowledge on: Large-scale ocean circulation, Water cycle exchange rates quantitative estimation, Occurrence of natural catastrophic events, Management of water resources, Role of the ocean in the climate system. To this end the mission requirements were set to determine SSS values with an accuracy of 0.1, in boxes of 100-200 km and temporal averages of 10-30 days [1]. Even the SMOS frequency, 1413 MHz within the microwave L-band, is close to the maximum sensitivity of brightness temperature (TB) to salinity variations, this sensitivity is much smaller than the one for soil moisture. The total range of ocean SSS spans a TB range of 5K, while for soil moisture is 100 K. This implies that the SSS retrieval by SMOS requires a higher performance of the MIRAS interferometric radiometer, the single payload on board [2]. The ESA SMOS Ocean Salinity Level 2 Processor (L2OS) has been designed from 2004 by the team that co-authors this manuscript, and is being now improved to increase its performance and solve the deficiencies observed since it entered into operation during the SMOS Commissioning Phase. It relies on a minimisation of the comparison between the TB at different incidence angles measured by SMOS when overflying a single ocean spot, and a modeling of the sea surface L-band emission that takes into account the actual environmental conditions and all the processes that impact or modify this emission [3]. In this paper we present the status of the L2 ocean products as they were operationally generated during the SMOS Science Workshop in September 2011, the improvements expected from the new processors (version 500) in operation by mid October 2011 and to be used also for the 2011 general reprocessing, and the issues on SMOS SSS retrieval still being investigated at that moment

European Geosciences Union (EGU) General Assembly, 3-8 April 2011, Vienna, Austria

1st SMOS Science Workshop, 27-29 September 2011, Arles, France

1st SMOS Science Workshop, 27-29 September 2011, Arles, France