Review of the CALIMAS Team Contributions to European Space Agency's Soil Moisture and Ocean Salinity Mission Calibration and Validation

Camps, Adriano ... et al.-- 38 pages, 22 figuresThis work summarizes the activities carried out by the SMOS (Soil Moisture and Ocean Salinity) Barcelona Expert Center (SMOS-BEC) team in conjunction with the CIALE/Universidad de Salamanca team, within the framework of the European Space Agency (ESA) CALIMAS project in preparation for the SMOS mission and during its first year of operation. Under these activities several studies were performed, ranging from Level 1 (calibration and image reconstruction) to Level 4 (land pixel disaggregation techniques, by means of data fusion with higher resolution data from optical/infrared sensors). Validation of SMOS salinity products by means of surface drifters developed ad-hoc, and soil moisture products over the REMEDHUS site (Zamora, Spain) are also presented. Results of other preparatory activities carried out to improve the performance of eventual SMOS follow-on missions are presented, including GNSS-R to infer the sea state correction needed for improved ocean salinity retrievals and land surface parameters. Results from CALIMAS show a satisfactory performance of the MIRAS instrument, the accuracy and efficiency of the algorithms implemented in the ground data processors, and explore the limits of spatial resolution of soil moisture products using data fusion, as well as the feasibility of GNSS-R techniques for sea state determination and soil moisture monitoringThis work has been performed under research grants TEC2005-06863-C02-01/TCM, ESP2005-06823-C05, ESP2007-65667-C04, AYA2008-05906-C02-01/ESP and AYA2010-22062-C05 from the Spanish Ministry of Science and Innovation, and a EURYI 2004 award from the European Science FoundationPeer Reviewe

Satellite and in situ sampling mismatches: Consequences for the estimation of satellite sea surface salinity uncertainties

Validation of satellite sea surface salinity (SSS) products is typically based on comparisons with in-situ measurements at a few meters’ depth, which are mostly done at a single location and time. The difference in term of spatio-temporal resolution between the in-situ near-surface salinity and the two-dimensional satellite SSS results in a sampling mismatch uncertainty. The Climate Change Initiative (CCI) project has merged SSS from three satellite missions. Using an optimal interpolation, weekly and monthly SSS and their uncertainties are estimated at a 50 km spatial resolution over the global ocean. Over the 2016–2018 period, the mean uncertainty on weekly CCI SSS is 0.13, whereas the standard deviation of weekly CCI minus in-situ Argo salinities is 0.24. Using SSS from a high-resolution model reanalysis, we estimate the expected uncertainty due to the CCI versus Argo sampling mismatch. Most of the largest spatial variability of the satellite minus Argo salinity is observed in regions with large estimated sampling mismatch. A quantitative validation is performed by considering the statistical distribution of the CCI minus Argo salinity normalized by the sampling and retrieval uncertainties. This quantity should follow a Gaussian distribution with a standard deviation of 1, if all uncertainty contributions are properly taken into account. We find that (1) the observed differences between Argo and CCI data in dynamical regions (river plumes, fronts) are mainly due to the sampling mismatch; (2) overall, the uncertainties are well estimated in CCI version 3, much improved compared to CCI version 2. There are a few dynamical regions where discrepancies remain and where the satellite SSS, their associated uncertainties and the sampling mismatch estimates should be further validated

Towards an Improved Characterization of Instrumental Biases and Forward Model Errors in SMOS Observations over the Ocean

SMOS & Aquarius Science Workshop, 15-17 April 2013, Brest, FranceThe 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 functionPeer Reviewe

Continuing Challenges in Salinity Retrieval for the SMOS Mission

SMOS & Aquarius Science Workshop, 15-17 April 2013, Brest, FranceThe 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 mapsPeer Reviewe

Towards an optimal fusion of SMOS and Aquarius SSS data

European Geosciences Union General Assembly 2014 (EGU2014), 27 april - 2 may 2014, Vienna, Austria.-- 1 pageThe 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 dataPeer Reviewe

Interprétation et modélisation de mesures à distance de la surface marine dans le domaine micro onde

Cette thèse présente une étude générale sur l'utilisation et l'interprétation de mesures à distance de la surface océaniques dans le domaine micro-onde dans le but de caractériser les effets de la rugosité de surface sur l émissivité. Une revue synthétique des théories électromagnétiques de diffusion/émission par/de la surface marine est proposée. Les liens théoriques entre les mesures à distance actives et passives sont rappelés et discutés. Basé sur l'analyse des modèles électromagnétiques et de différents jeux de données actives et passives, un modèle semi-empirique de la variation d'émissivité en fonction de la rugosité de la surface a été développé. Celui-ci caractérise de manière empirique les changements d émissivité en fonction du coefficient de réflexion de Fresnel et de deux paramètres statistiques de la surface. Sur la base de cette paramétrisation, une méthodologie est proposée pour quantifier les impacts de la rugosité de la surface océanique sur la température de brillance observée dans les nouvelles données du satellite SMOS.This dissertation presents a general investigation on the use and interpretation of remote sensing measurements of the sea surface at microwave frequencies and specifically aims at better characterizing sea surface roughness effects on emissivity. A review of the state of the art of the scattering and emission theories of the sea surface at microwave frequencies is first proposed. Theorical links between active and passive remote sensing measurements are recalled and discused. Based on electromagnetic models and several active/passive data set analysis, a consistent semi-empirical model of the mutl-incidence angle emissivity change associated with the surface roughness variation is developed. The latter characterizes emissivity changes in terms of Fresnel Reflection coefficient and two rough sea surface statistical parameters. Based on this parameterization, a methodology is proposed to quantify the impacts of ocean surface roughness on the brightness temperature observed in the new mutli-angular data from SMOS.VERSAILLES-BU Sciences et IUT (786462101) / SudocSudocFranceF

SMOS SSS uncertainties associated with errors on auxiliary parameters

European Geosciences Union General Assembly 2014 (EGU2014), 27 april - 2 may 2014, Vienna, Austria.-- 1 pageThe 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 wavesPeer Reviewe

Towards an Improved Diagnostic of Instrumental Biases and Forward Model Errors in SMOS Observations over the Ocean

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

Towards error estimation maps for satellite sea surface salinity retrievals

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