In the framework of the MAXSS project, a multi-mission (MM) wind product under tropical cyclone conditions has been generated for the period 2010-2020, i.e., a synergistic product that combines the European Center for Medium-range Weather Forecast fifth reanalysis (ERA5) output with several scatterometer and radiometer wind data adjusted to the wind scale of hurricane hunter in situ observations. The errors of the satellite and MM wind products have been estimated with triple collocation analysis, while the different spatial representation of the datasets (i.e., representativeness error r2) is accounted for and computed through spatial variance analysis. The error analysis shows that C-band scatterometers have the lowest standard deviation errors (0.9 m/s) compared to those of the Ku-band scatterometers (1.4-2.1 m/s), while radiometers have the largest errors (2.0-2.9 m/s). Finally, the analysis reveals that the MM wind product has a lower error (1.6 m/s) compared to ERA5 (2.6 m/s) under tropical cyclone conditions.

The monsoon freshwater and wind forcing drive high Sea Surface Salinity (SSS) contrasts and variability (up to 10 pss range) in the Bay of Bengal (BoB), with important consequences for upper ocean mixing and air-sea interactions. Synoptic SSS maps did only become available with the advent of L-band radiometers in 2010, due to insufficient prior in situ data coverage. Here, we build tools aiming at reconstructing the monthly BoB SSS at ¼° resolution since 2002 from AMSR-E radiometer data. The C-band low sensitivity to SSS requires a very careful processing. Taking the X- minus C- bands signals reduce the impact of Sea Surface Temperature (SST) and wind on brightness temperatures. It was however further necessary to train the algorithm with SSS data from L-Band radiometers to remove residual surface winds, SST, and atmospheric water contents signals. We also found that a separate treatment of the ascending and descending passes was necessary, as well as a proper data screening to minimize contamination by land signals. The resulting SSS product reproduces the broad BoB climato-logical SSS, and has a 0.66 correlation, 1.08 pss rms-difference to co-located in situ surface salinity from Array for Real-time Geostrophic Oceanography (ARGO) floats. Comparisons with ocean re-analyses in two SSS interannual variability hotspots indicate poor performance in the Northern BoB, but some skill along the East coast of India. Our results provide a proof of concept for reconstructing the BoB SSS from AMSR-E data, and we discuss possible future improvements of the data processing to further reduce the impact of spurious signals.

International audience

Sea surface salinity is retrieved for more than 11 years from the Soil Moisture and Ocean Salinity (SMOS) satellite mission. This data set provides a unique monitoring of the Sea Surface Salinity (SSS) spatio-temporal variability at global scale. It is particularly useful to follow the surface ocean pathway of fresh river plumes water as illustrated here in the Bay of Bengal. A revised adjustment of the whole SMOS SSS time series (CATDS Expertise Center version 5, 2010–2020) leads to clear reduction of local biases in very variable regions and in very noisy regions. The robust std difference between SMOS CEC v5 (18-day, ~70km SSS) and Argo in situ SSS is 0.17 in regions warmer than 5°C. We will discuss how future CATDS products will be improved in view of two ongoing reprocessings, the CATDS L1/L2 v7 reprocessing and the ESA CCI+SSS L2 SMOS reprocessing.

The European Space Agency (ESA) Climate Change Initiative (CCI+) for Sea Surface Salinity (SSS) aims at generating global SSS fields from all available satellite L-band radiometer measurements over the longest possible period with a great stability (including Soil Moisture and Ocean Salinity, SMOS). Version 1 and 2 of CCI+SSS level 4 fields combine SSS form the three satellite L-Band radiometer missions and have been found to be in a very good agreement with in situ measurements (global rms difference of 0,16 pss). Nevertheless, some systematic differences still remain between CCI+SSS and in situ SSS. We study here to which extent some errors coming from the SMOS SSS processing are reduced, when making some key changes in the SMOS level 2 OS processing. Then, we discuss the contribution of each change in the preliminary results we obtain.

Earlier studies have pointed out systematic differences between sea surface salinity retrieved from L-band radiometric measurements and measured in situ, which depend on sea surface temperature (SST). We investigate how to cope with these differences given existing physically based radiative transfer models. In order to study differences coming from seawater dielectric constant parametrization, we consider the model of Somaraju and Trumpf (2006) (ST) which is built on sound physical bases and close to a single relaxation term Debye equation. While ST model uses fewer empirically adjusted parameters than other dielectric constant models currently used in salinity retrievals, ST dielectric constants are found close to those obtained using the Meissner and Wentz (2012) (MW) model. The ST parametrization is then slightly modified in order to achieve a better fit with seawater dielectric constant inferred from SMOS data. Upgraded dielectric constant model is intermediate between KS and MW models. Systematic differences between SMOS and in situ salinity are reduced to less than +/-0.2 above 0 °C and within +/-0.05 between 7 °C and 28 °C. Aquarius salinity becomes closer to in situ salinity, and within +/-0.1. The order of magnitude of remaining differences is very similar to the one achieved with the Aquarius version 5 empirical adjustment of wind model SST dependence. The upgraded parametrization is recommended for use in processing the SMOS data. Further assessment or improvement using new laboratory measurements should consider keeping the physics-based formulation by ST that has been shown here to be very efficient.

Surface roughness and foam are two main components of ocean surface microwave thermal emission. Surface roughness provides scattering element and modifies local incidence angle. Air in foam alters the dielectric property of the surface layer. Bubbles in foam also alter the curvature of foam- water interface and modify emission and scattering properties of the water surface itself. Whitecap coverage Wc is the most accessible oceanographic information to represent surface foam. For emission analysis, it is necessary to establish a function relating to Wc and the effective air fraction Fa interacting with electromagnetic (EM) waves. An empirical relation is established through analyzing several microwave radiometer data sets in high winds covering a wide range of frequency, incidence angle, and vertical and horizontal polarizations. A physical interpretation of the proposed Fa (Wc) relationship is discussed. The relationship is used to quantify several important characteristics of surface foam relevant to microwave emission, including effective air fraction and skin depth as functions of wind speed, microwave frequency, and incidence angle.

An important contribution of satellite Sea Surface Salinity (SSS) is the spatio-temporal monitoring of rivers fresh water plumes at mesoscale. In this paper, we detail a new correction for systematic errors in the Soil Moisture and Ocean Salinity (SMOS) measurements that is implemented in the Centre Aval de Traitement des Donnees SMOS (CATDS). With this new mitigation, the SMOS and Soil Moisture Active Passive (SMAP) SSS monitor very consistent features in most areas close to continents. The rms-difference between bi-weekly SMOS and SMAP SSS over 20 months and in selected coastal regions is about 0.3pss (once outliers are filtered out), rather consistent with the rms-difference between satellite and in situ SSS (on the order of 0.2pss). The coefficient of determination (r2) between SMOS and SMAP SSS is above than 0.8 in very fresh areas (river plumes). Over the open ocean, the rms difference between SMOS and ship SSS is 0.2pss.

After almost 9 years in orbit L band satellite radiometry has demonstrated its impacts and values for a wide range of science and applications. In some cases it has demonstrated its uniqueness for assessing key environmental variables and in many others its high impact.

Co-located over extreme events, C-band copolarized and cross-polarized normalized radar cross sections (NRCS) and L-band ocean surface roughness brightness temperature (TB,rough) are directly compared to analyze the similarities and differences between these two parameters at medium resolution (about 25 km). NRCS in VH-polarization and VV-polarization (σ0,VH, σ0,VV) were acquired by Sentinel-1 C-band synthetic aperture radar. TB,roughis estimated from brightness temperatures (TB) measured by the L-band radiometer on-board the Soil Moisture Active Passive mission. When the rain rate is less than 20 mm/h, a striking linear relationship is found between active C-Band cross-polarized NRCS and passive L-Band TB,rough: σ0,VH(θSAR) ∝ tan(θSAR) × TB,rough(θSMAP= 40°), without any apparent saturation for TB,roughranging from 3.5 to 17 K. Compared to both high TB,roughand σ0,VH, copolarized σ0,VVmeasurements saturate. As interpreted, this can correspond to a regime change of the air-sea interactions during extreme events. In heavy rain conditions, C-band co-polarized NRCS decreases for extreme situations. In these cases, the covariation between C-band cross-polarized NRCS and L-band TB,roughis less evident. An accurate and unambiguous assessment of the impact of rain will deserve further investigations.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been in orbit for over 7 years, with its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) functioning well. This 7 year period has provided a wealth of information which has enabled us to understand and consolidate the performance of the payload in great detail. More importantly, we know now the things that work well, those that need improvement, and how the instrument could be enhanced if we were to build it again. This paper presents the lessons learnt from SMOS after 7 years in orbit.

Measurements of the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission-Water 1 (GCOM-W1) satellite at 6.925 and 7.3 GHz and both linear polarizations over tropical cyclones (TCs) during 2012-2014 are used to derive a new geophysical function relating the brightness temperature to the sea surface wind speed (SWS) in extreme conditions. Similar sensitivity to the SWS at close C-band frequencies allowed correcting for the atmospheric contributions to the microwave radiance and estimating the brightness temperature (TB) at the surface under TCs, combining theoretical modeling and measured TBanalyses. Estimated oceanic TB's were regressed against the wind speeds from the Best Track Archive to derive a new geophysical model function for the wind speed excess emissivity at AMSR2 C-band microwave frequencies.

The L-band passive microwave data from the Soil Moisture Active Passive (SMAP) observatory are investigated for remote sensing of ocean surface winds during severe storms. The surface winds of Joaquin derived from the real-time analysis of the Center for Advanced Data Assimilation and Predictability Techniques at Penn State support the linear extrapolation of the Aquarius and SMAP geophysical model functions (GMFs) to hurricane force winds. We apply the SMAP and Aquarius GMFs to the retrieval of ocean surface wind vectors from the SMAP radiometer data to take advantage of SMAP's two-look geometry. The SMAP radiometer winds are compared with the winds from other satellites and numerical weather models for validation. The root-mean-square difference (RMSD) with WindSat or Special Sensor Microwave Imager/Sounder is 1.7 m/s below 20-m/s wind speeds. The RMSD with the European Center for Medium-Range Weather Forecasts direction is 18° for wind speeds between 12 and 30 m/s. We find that the correlation is sufficiently high between the maximum wind speeds retrieved by SMAP with a 60-km resolution and the best track peak winds estimated by the National Hurricane Center and the Joint Typhoon Warning Center to allow them to be estimated by SMAP with a correlation coefficient of 0.8 and an underestimation by 8%-18% on average, which is likely due to the effects of spatial averaging. There is also a good agreement with the airborne Stepped-Frequency Radiometer wind speeds with an RMSD of 4.6 m/s for wind speeds in the range of 20-40 m/s.

The overall process commonly referred to as Ocean Acidification (OA) is nowadays gathering increasing attention for its profound impact at scientific and socio-economic level. To date, the majority of the scientific studies into the potential impacts of OA have focused on models and in situ datasets. Satellite remote sensing technology have yet to be fully exploited and could play a significant role by providing synoptic and frequent measurements for investigating OA processes on global scales. Within this context, the purpose of the ESA “Pathfinders-OA” project is to quantitatively and routinely estimate surface ocean pH by means of satellite observations in several ocean regions. Satellite Ocean Colour, Sea Surface Temperature and Sea Surface Salinity data (with an emphasis on the latter) will be exploited. A proper merging of these different datasets will allow to compute at least two independent proxies among the seawater carbonate system parameters and therefore obtain the best educated guess of the surface ocean pH. Preliminary results of the anomaly and variability of the ocean pH maps are presented.

We validate Soil Moisture and Ocean Salinity (SMOS) sea surface salinity (SSS) retrieved during August 2010 from the European Space Agency SMOS processing. Biases appear close to land and ice and between ascending and descending orbits; they are linked to image reconstruction issues and instrument calibration and remain under study. We validate the SMOS SSS in conditions where these biases appear to be small. We compare SMOS and ARGO SSS over four regions far from land and ice using only ascending orbits. Four modelings of the impact of the wind on the sea surface emissivity have been tested. Results suggest that the L-band brightness temperature is not linearly related to the wind speed at high winds as expected in the presence of emissive foam, but that the foam effect is less than previously modeled. Given the large noise on individual SMOS measurements, a precision suitable for oceanographic studies can only be achieved after averaging SMOS SSS. Over selected regions and after mean bias removal, the precision on SSS retrieved from ascending orbits and averaged over 100 km$ \times$100 km and 10 days is between 0.3 and 0.5 pss far from land and sea ice borders. These results have been obtained with forward models not fitted to satellite L-band measurements, and image reconstruction and instrument calibration are expected to improve. Hence, we anticipate that deducing, from SMOS measurements, SSS maps at 200 km$\times$200 km, 10 days resolution with an accuracy of 0.2 pss at a global scale is not out of reach.

A renewal of interest for the radiometric L-band Sea Surface Salinity (SSS) remote sensing appeared in the 1990s and led to the Soil Moisture and Ocean Salinity (SMOS) satellite launched in November 2009 and to the Aquarius mission (launched in June 2011). However, due to low signal to noise ratio, retrieving SSS from L-band radiometry is very challenging. In order to validate and improve L-band radiative transfer model and salinity retrieval method used in SMOS data processing, the Cooperative Airborne Radiometer for Ocean and Land Studies (CAROLS) was developed. We analyze here a coastal flight (20 May 2009), in the Gulf of Biscay, characterized by strong SSS gradients (28 to 35 pss-78). Extensive in-situ measurements were gathered along the plane track. Brightness temperature$(T_{b})$integrated over 800 ms correlates well with simulated$T_{b}$(correlation coefficients between 0.80 and 0.96; standard deviations of the difference of 0.2 K). Over the whole flight, the standard deviation of the difference between CAROLS and in-situ SSS is about 0.3 pss-78 more accurate than SSS fields derived from coastal numerical model or objective analysis. In the northern part of the flight, CAROLS and in-situ SSS agree. In the southern part, the best agreement is found when using only V-polarization measured at 30$^{\circ}$incidence angle or when using a multiparameter retrieval assuming large error on$T_{b}$(suggesting the presence of biases on H-polarization). When compared to high-resolution model SSS, the CAROLS SSS underlines the high SSS temporal variability in river plume and on continental shelf border, and the importance of using realistic river run-offs for modeling coastal SSS.

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission was launched on the 2nd of November 2009. The first six months after launch, the so-called commissioning phase, were dedicated to test the functionalities of the spacecraft, the instrument, and the ground segment including the data processors. This phase was successfully completed in May 2010, and SMOS has since been in the routine operations phase and providing data products to the science community for over a year. The performance of the instrument has been within specifications. A parallel processing chain has been providing brightness temperatures in near-real time to operational centers, e.g., the European Centre for Medium-Range Weather Forecasts. Data quality has been within specifications; however, radio-frequency interference (RFI) has been detected over large parts of Europe, China, Southern Asia, and the Middle East. Detecting and flagging contaminated observations remains a challenge as well as contacting national authorities to localize and eliminate RFI sources emitting in the protected band. The generation of Level 2 soil moisture and ocean salinity data is an ongoing activity with continuously improved processors. This article will summarize the mission status after one year of operations and present selected first results.

Multi-angular images of the brightness temperature (TB) of the Earth at 1.4 GHz are reconstructed from the Soil Moisture and Ocean Salinity (SMOS) satellite sensor data since end 2009. Sea surface salinity (SSS) products remote sensing from space is being attempted using these data over the world oceans. The quality of the first version of the European Space Agency operational Level 2 (L2) SSS swath products is assessed in this paper, using satellite/in situ SSS data match-ups that were collected over the second half of 2010. This database reveals that 95% of the SMOS L2 products show a global error standard deviation on the order of ~ 1.3 practical salinity scale. Simple spatiotemporal aggregation of the L2 products to generate monthly SSS maps at 1° ×1° spatial resolution reduces the error down to about 0.6 globally and 0.4 in the tropics for 90% of the data. Several major problems are, however, detected in the products. Systematically, SMOS SSS data are biased within a ~ 1500 km wide belt along the world coasts and sea ice edges, with a contamination intensity and spread varying from ascending to descending passes. Numerous world ocean areas are permanently or intermittently contaminated by radio-frequency interferences, particularly in the northern high latitudes and following Asia coastlines. Moreover, temporal drifts in the retrieved SSS fields are found with varying signatures in ascending and descending passes. In descending passes, a time-dependent strong latitudinal bias is found, with maximum amplitude reached at the end of the year. Errors in the forward modeling of the wind-induced emissivity and of the sea surface scattered galactic sources are as well identified, biasing the sss retrievals at high and low winds and when the galactic equator sources are reflected toward the sensor.

SMOS (Soil Moisture and Ocean Salinity), launched in November 2, 2009 is the first satellite mission addressing the salinity measurement from space through the use of MIRAS (Microwave Imaging Radiometer with Aperture Synthesis), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at L-band. This paper presents a summary of the sea surface salinity retrieval approach implemented in SMOS, as well as first results obtained after completing the mission commissioning phase in May 2010. A large number of papers have been published about salinity remote sensing and its implementation in the SMOS mission. An extensive list of references is provided here, many authored by the SMOS ocean salinity team, with emphasis on the different physical processes that have been considered in the SMOS salinity retrieval algorithm.

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

It is now well understood that data on soil moisture and sea surface salinity (SSS) are required to improve meteorological and climate predictions. These two quantities are not yet available globally or with adequate temporal or spatial sampling. It is recognized that a spaceborne L-band radiometer with a suitable antenna is the most promising way of fulfilling this gap. With these scientific objectives and technical solution at the heart of a proposed mission concept the European Space Agency (ESA) selected the Soil Moisture and Ocean Salinity (SMOS) mission as its second Earth Explorer Opportunity Mission. The development of the SMOS mission was led by ESA in collaboration with the Centre National d'Etudes Spatiales (CNES) in France and the Centro para el Desarrollo Tecnologico Industrial (CDTI) in Spain. SMOS carries a single payload, an L-Band 2-D interferometric radiometer operating in the 1400-1427-MHz protected band . The instrument receives the radiation emitted from Earth's surface, which can then be related to the moisture content in the first few centimeters of soil over land, and to salinity in the surface waters of the oceans. SMOS will achieve an unprecedented maximum spatial resolution of 50 km at L-band over land (43 km on average over the field of view), providing multiangular dual polarized (or fully polarized) brightness temperatures over the globe. SMOS has a revisit time of less than 3 days so as to retrieve soil moisture and ocean salinity data, meeting the mission's science objectives. The caveat in relation to its sampling requirements is that SMOS will have a somewhat reduced sensitivity when compared to conventional radiometers. The SMOS satellite was launched successfully on November 2, 2009.

The CAROLS L-band radiometer, which is built and designed as a copy of DTU EMIRAD II instrument will be used in conjunction with other airborne instruments (in particular the C-Band scatterometer STORM) in coordination with in situ field campaigns for futur SMOS CAL/VAL activities. A validation campaign with four flights was made over the South West of France and the Bay of Biscay (Atlantic Ocean) in September 2007. Different instrumented sites were over ocean and land surfaces were coverecd. Moreover, in order to qualify the radiometric data, different types of aircraft maneuvers were performed over ocean: circle flights, wing and nose wags. We present in this paper the first analysis of the data quality using these ocean measurements. We show a very good sensitivity of both channels.

The CAROLS, L band radiometer, is built and designed as a copy of EMIRAD II radiometer of DTU team. It is a Correlation radiometer with direct sampling and fully polarimetric (i.e 4 Stockes). It will be used in conjunction with other airborne instruments (in particular the C-Band scatterometer (STORM) and IEEC GPS system, Infrared CIMEL radiometer, one visible camera), in coordination with in situ field campaigns for SMOS CAL/VAL. The instruments are implemented on board the French research airplane ATR42. A validation campaign with four flights was made over south west of France, Hourtin Lake and Bay of Biscay (Atlantic Ocean) in September 2007. In order to qualify the radiometer data, different types of aircraft movements were realized: circle flights, wing and nose wags. Simultaneously to flights, different ground measurements were made over continental surfaces and ocean. First results show a good quality of data over ocean surfaces. For continental surfaces, important Radio-Frequency Interferences (RFI) were observed over a large part of the studied region.

We examine how the rough sea surface scattering of L-band celestial sky radiation might affect the measurements of the future European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. For this purpose, we combined data from several surveys to build a comprehensive all-sky L-band celestial sky brightness temperature map for the SMOS mission that includes the continuum radiation and the hydrogen line emission rescaled for the SMOS bandwidth. We also constructed a separate map of strong and very localized sources that may exhibit L-band brightness temperatures exceeding 1000 K. Scattering by the roughened ocean surface of radiation from even the strongest localized sources is found to reduce the contributions from these localized strong sources to negligible levels, and rough surface scattering solutions may be obtained with a map much coarser than the original continuum maps. In rough ocean surface conditions, the contribution of the scattered celestial noise to the reconstructed brightness temperatures is not significantly modified by the synthetic antenna weighting function, which makes integration over the synthetic beam unnecessary. The contamination of the reconstructed brightness temperatures by celestial noise exhibits a strong annual cycle with the largest contamination occurring in the descending swaths in September and October, when the specular projection of the field of view is aligned with the Galactic equator. Ocean surface roughness may alter the contamination by over 0.1 K in 30% of the SMOS measurements. Given this potentially large impact of surface roughness, an operational method is proposed to account for it in the SMOS level 2 sea surface salinity algorithm.

The ldquogalactic glitterrdquo phenomenon at L-band, i.e., the scattering of celestial sky radiation by the rough ocean surface, is examined here as a potential source of error for sea surface salinity (SSS) remote sensing. We begin by considering the transformations that must be applied to downwelling celestial noise in order to compute the eventual impact on the antenna temperature. Then, outside the context of any particular measurement system, we use approximate scattering models along with a model for the equilibrium wind wave spectrum to examine how the scattered signal at the surface might depend on the geophysical conditions and scattering geometry. It is found that, when the specular point lies far away from the galactic plane, where the incident celestial brightness is uniform, sea surface roughness has a negligible impact on the glitter. At such a point, variations in both the orientation of the incidence plane and the wind direction relative to the scattering azimuth have negligible impact. By contrast, when the specular point lies in the vicinity of a localized maximum of brightness, scattering by the roughened ocean surface may reduce the glitter by more than 30%, as compared to a perfectly flat surface, and the glitter amplitude may vary by up to 0.7 K with variations in wind direction and by up to 0.5 K with variations in incidence plane orientation. It is shown that accounting for the roughness impact on celestial noise contamination is of particular concern for the remote sensing of SSS.

The L-band interferometric radiometer onboard the Soil Moisture and Ocean Salinity mission will measure polarized brightness temperatures (Tb). The measurements are affected by strong radiometric noise. However, during a satellite overpass, numerous measurements are acquired at various incidence angles at the same location on the Earth's surface. The sea surface salinity (SSS) retrieval algorithm implemented in the Level 2 Salinity Prototype Processor (L2SPP) is based on an iterative inversion method that minimizes the differences between Tb measured at different incidence angles and Tb simulated by a full forward model. The iterative method is initialized with a first-guess surface salinity that is iteratively modified until an optimal fit between the forward model and the measurements is obtained. The forward model takes into account atmospheric emission and absorption, ionospheric effects (Faraday rotation), scattering of celestial radiation by the rough ocean surface, and rough sea surface emission as approximated by one of three models. Potential degradation of the retrieval results is indicated through a flagging strategy. We present results of tests of the L2SPP involving horizontally uniform scenes with no disturbing factors (such as sun glint or land proximity) other than wind-induced surface roughness. Regardless of the roughness model used, the error on the retrieved SSS depends on the location within the swath and ranges from 0.5 psu at the center of the swath to 1.7 psu at the edge, at 35 psu and 15degC. Dual-polarization (DP) mode provides a better correction for wind-speed (WS) biases than pseudofirst Stokes mode (ST1). For a WS bias of -1 mmiddots-1, the corresponding SSS bias at the center of the swath is equal to -0.3 psu in DP mode and to -0.5 psu in ST1 mode. The inversion methodology implicitly assumes that WS errors follow a Gaussian distribution, even though these errors should follow more closely a Rayleigh distribution. For this reason, the use of wind components, which typically exhibit Gaussian error distributions, may be preferred in the retrieval. However, the use of noisy wind components creates WS and SSS biases at low WSs (0.1 psu at 3 mmiddots-1). At a sea surface temperature (SST) of 15degC, the retrieved SSS is weakly sensitive to the SST biases, with the SSS bias always lower than 0.3 psu for SST biases ranging from -0.5degC to -2degC. In DP mode, biases in the vertical total electron content (TEC) of the atmosphere result in SSS biases smaller than 0.2 psu. The pseudofirst Stokes mode is insensitive to TEC. Failure to fully account for sea surface roughness scattering effects in the computation of sky radiation contribution leads to a maximum SSS bias of 0.2 psu in the selected configuration, i.e., a descending orbit over the Northern Pacific in February. To achieve SSS biases that are smaller than 0.2 psu, special care must be taken to correct for biases at low WS and to ensure that the bias on the mean WS (averaged over 200 km times 200 km and ten days) remains smaller than 0.5 mmiddots-1.

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

Since the sun is an extremely strong radiation source at L-band, accounting for sun glint over the ocean, i.e., solar radiation reflected by the sea surface toward downward-looking radiometers, raises a significant challenge for the remote sensing of sea surface salinity. This paper describes a dedicated physical model for sun glint at L-band frequencies and provides quantitative and qualitative estimates of the sun glint contamination impinging the antenna of the Microwave Imaging Radiometer with Aperture Synthesis interferometer onboard the future European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. The sun brightness temperature expected during the mission period is first estimated from past solar flux data with an expected range of to about . Numerical simulations of the predicted illumination of the SMOS antenna by solar radiation scattered by the rough sea surface are then performed at key dates of the seasonal cycle using different asymptotic scattering models and several representative surface conditions. Although the center of the sun's glitter pattern will never be located within the useful part of SMOS' synthesized field of view, the expected contamination due to roughness scattering will range between 0 K and about 500 K, depending on the target position, the season period, the roughness state at the target, and the level of solar activity at the time of measurements. In particular, we find the sun glint contamination to be more intense when SMOS will probe ocean surfaces in the Southern Hemisphere, reaching maxima in descending passes with highest values expected at dates around winter solstices.

The European Space Agency SMOS (Soil Moisture and Ocean Salinity) mission aims at obtaining global maps of soil moisture and sea surface salinity from space for large scale and climatic studies. It uses an L-band (1400-1427 MHz) microwave interferometric radiometer by aperture synthesis (MIRAS) to measure brightness temperature at the Earth surface at horizontal and vertical polarizations (Th and Tv). These two parameters will be used together to retrieve the geophysical variables. The retrieval of salinity is a complex process that requires the knowledge of other environmental information and an accurate processing of the radiometer measurements, due to the narrow range of ocean brightness temperatures and the strong impact in the measured values of different geophysical parameters (as sea state) other than salinity. Here we present the baseline approach chosen by ESA to retrieve sea surface salinity from MIRAS data, as it has been developed and implemented by the joint team of scientists and engineers responsible for the SMOS ocean salinity level 2 prototype processor.

Aiming to provide sea surface salinity (SSS) maps with a spatiotemporal averaged accuracy of 0.1 psu (practical salinity units), the Soil Moisture and Ocean Salinity (SMOS) community is increasingly focusing on the determination of a robust inversion scheme to enable SSS retrieval from L-band brightness temperature data. In the framework of the Synergetic Aspects and Auxiliary Data Concepts for Sea Surface Salinity Measurements from Space project, efforts have been oriented toward a quantitative analysis of SSS retrieval using different auxiliary data sets. This paper aims to contribute to the assessment of the SMOS salinity retrieval error budget in view of the upcoming SMOS mission ground segment development. Aiming to do that, different models and auxiliary data to simulate and invert the brightness temperature data have been used. An estimation of the different auxiliary parameters' influence has been performed to quantitatively predict at what extent it is reasonable to expect to retrieve salinity once the brightness temperatures are directly measured by the sensor. Statistical distributions of the spatiotemporal averaged errors are provided

A study has been undertaken which objective is to contribute to the investigation of the validity of microwave surface scattering models used in remote sensing applications, particularly when applied to realistic representations of natural surfaces. These investigations are based on recent implementations of rigorous methods (MoM and FDTD) and cover a wide range of configurations of observation (mono- and bi-static). Both land (bare soils) and sea surfaces are being investigated.

Of the satellite radiometer sensors, there has been only one instrument that provides any heritage at L-band: the Skylab S-194 instrument that operated in the 1970s. From an analysis of S-194 brightness temperature (Tb) sensitivity to SSS, SST and wind speed, Lerner & Hollinger (1976) concluded that the wind speed dependence of L-band brightness temperature at nadir is about 0.16 K/knot. This is almost four times higher than what is predicted by recently developed sea surface emissivity models at L-band and twice the experimental value reported during the Bering Sea Experiments. To investigate the possible reasons for such discrepancies, two data sets acquired by the S-194 Skylab instrument from 1973-1974 missions are used in the present paper in conjunction with products from climate model reanalysis projects as ancillary data. The re-analyses shows that it is very likely that Lerner & Hollinger overpredicted the quasi-linear wind speed dependence of L-band sea surface emissivity at nadir by a factor of about 2, main discrepancies being due to different wind speed data used in the analysis. Still, we found that emissivity models for the foam free sea surface based on the small perturbation method underestimate the roughness impact at nadir by a factor of 2. Including the foam impact cannot explain all the differences.

Aiming to provide sea surface salinity (SSS) maps with a spatio-temporal averaged accuracy of 0.1 psu, the SMOS community is increasingly focusing on the determination of a robust inversion scheme to enable SSS retrieval from L-band brightness temperature data. In the framework of the "Synergetic Aspects and Auxiliary Data Concepts for Sea Surface Salinity Measurements from Space" project, efforts have been oriented towards a quantitative analysis of SSS retrieval once different auxiliary data are plugged into the minimization procedure, providing statistical distributions of the spatio-temporal averaged errors.

Sea surface salinity can be measured by microwave radiometry at L-band (1400-1427 MHz). This frequency is a compromise between sensitivity to the salinity, small atmospheric perturbation, and reasonable pixel resolution. The description of the ocean emission depends on two main factors: (1) the sea water permittivity, which is a function of salinity, temperature, and frequency, and (2) the sea surface state, which depends on the wind-induced wave spectrum, swell, and rain-induced roughness spectrum, and by the foam coverage and its emissivity. This study presents a simplified two-layer emission model for foam-covered water and the results of a controlled experiment to measure the foam emissivity as a function of salinity, foam thickness, incidence angle, and polarization. Experimental results are presented, and then compared to the two-layer foam emission model with the measured foam parameters used as input model parameters. At 37 psu salt water the foam-induced emissivity increase is /spl sim/0.007 per millimeter of foam thickness (extrapolated to nadir), increasing with increasing incidence angles at vertical polarization, and decreasing with increasing incidence angles at horizontal polarization.

Since the Sun is a very bright radiation source at L-band, reception of direct and Earth-reflected solar radiations by downward-looking radiometers raises a significant challenge for the remote sensing of ocean surface salinity. For a given spaceborne mission concept, the impact of the Sun radiations depends on the sensor antenna properties, the location of the Sun relative to both the spacecraft and the reflecting surface, as well as on the surface scattering properties of the observed Earth scene. Effects on interferometric data, (i.e. visibilities), provided by Synthetic Aperture Imaging Radiometers (SAIR) need to be accounted for; the affected area, determined through both geometrical and geophysical considerations, have to be either masked or flagged, or submitted to specific correction procedures, for correct sea surface salinity retrieval. Focusing on the future ESA Soil Moisture and Ocean Salinity (SMOS) space mission, we provide quantitative and qualitative estimates for measurements contamination by Sun as expected for the period 2007-2010, and we derive methods that can serve to develop a consistent correction strategy

The small slope approximation (SSA) theory is applied to the prediction of the foam-free sea surface brightness temperatures at L-band and nadir incidence angle. If surface geometry is assumed to be Gaussian, the wind induced polarized brightness temperature signals are integral functions of the product of the directional curvature spectrum harmonics and electromagnetic weighting functions. At nadir incidence angle, these electromagnetic weighting functions are equals in magnitude for horizontal and vertical polarization and exhibit peaky distribution as function of wavenumber. Using these properties together with azimuthal characteristics of the directional curvature spectrum at L-band, the combination of vertically and horizontally polarized brightness temperature signals at normal incidence is shown to form a simple linear equation of the dielectric constant, the SST and the wind direction. A simple analytical inversion algorithm is proposed to retrieve the Sea Surface Salinity without a priori knowledge of the wind speed using the previously established dependence.

The authors conducted experiments in the large wind-wave tank at IRPHE to examine the influence of longer waves propagating at oblique angles to the wind direction on microwave backscattering. Measurements were made of wind, wave directional spectra, and cross sections from a 13.5-GHz scatterometer at 30/spl deg/ from nadir incidence angle. the authors characterize the scatterometric azimuthal scans with respect to the magnitude and the direction of the maximum backscattered power. Scans of solely wind waves are used to assess the effects of oblique waves, for which the scans yield trends that are related to the oblique-wave direction relative to the wind and the oblique-wave steepness. In particular, cross-section maxima are enhanced by oblique waves and the direction of maxima rotates from the wind axis toward the oblique-wave direction. These scatterometer data, in addition to other oblique-wave studies, led the authors to conclude that wind vectors produced from standard retrieval algorithms for scatterometers should be used with prudence in regions with significant veering winds, such as near meteorological fronts.