Satellite mission datasets increase in size as their life span grows and the resolutions of the instruments increase. Accurately projecting antenna-based satellite measurements on a geographical grid while maintaining reasonable computational time and costs can be challenging. It is thus necessary to include big data algorithms and dedicated management techniques in the processing of such datasets.In this regard, the optimization of the number of interpolations and projections is one of the key aspects, as the native errors of the measurements propagate at each processing step. Besides each interpolation and projection implies a non-negligible increase in total computational time.This work is based on the Sea Surface Salinity (SSS) processor of the Soil Moisture and Ocean Salinity (SMOS) mission, but it could be easily extended to any other satellite mission where individual values for each measurement are retrieved. We propose a redefinition of the complete processor chain so it can work with the measurements within the instrument coordinate system. This allows us to avoid projection-related errors during the generation of the final product.Additionally, we introduce a novel algorithm to project those measurements taking into account the actual spatial extent of the acquisitions instead of taking them as points, so measures are averaged weighted by the area they cover on the Earth-based grid. This method is optimized to transform 2D areas into discrete measurements, increasing its computational efficiency and favoring parallelization. Our algorithm has demonstrated its potential when incorporated into the SMOS SSS processor at the Barcelona Expert Center (BEC), allowing us to keep a final resolution very close to the one attained at the antenna coordinate system.

The spatial spectra of three Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) datasets, produced by the Barcelona Expert Center (BEC), were assessed in this study along zonal and meridional directions. The datasets are the Level 3 (L3) SM gridded at 25 km, the Level 4 (L4) SM at 1 km and an experimental L4 SM at ~300 m. Since the L4 products are obtained by a downscaling algorithm that uses Normalized Difference Vegetation Index (NDVI), NDVI data from MODIS (1 km) and Sentinel-3 (~300 m) were also analyzed.Both L4 SM products provide useful spatial information of small-scale structures, with estimated effective spatial resolutions of ~2.5 km (for the L4 at 1 km) and ~500 m (for the L4 at ~300 m). The NDVI data used for the downscaling have a significant impact not only on the spatial patterns of the resulting SM product, but also on its spectrum.

Sea surface temperature (SST) is a key oceanic variable controlling energy fluxes, as well as several atmospheric parameters such as wind speed, air temperature, humidity and cloudiness. During the last decades, mesoscale has received much attention and the new frontier for the coming years is the understanding of sub-mesoscale dynamics and its impact on climate. In order to address this challenge, there is a need of developing high-resolution observing systems, remote sensing sensors in conjunction with in-situ observations. Some traditional climate-oriented SST observational datasets generally do not include satellite observations and are typically based on in-situ observations, prominent examples being NOAA Extended Reconstructed SST (ERSST) and Hadley Centre SST version 3 (HadSST3). Other datasets combine both, in-situ and satellites observations, like the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST). The main objective of this work is to characterize sea surface temperature (SST) climatology and variability in the tropical Atlantic region. For that purpose, we thoroughly compare two standard, climate-oriented datasets, HadISST (1° resolution) and ERSSTv5 (2° resolution), with the GHRSST product developed by the European Space Agency (ESA) Climate Change Initiative (CCI) (0.05° resolution). Our results show that, at grid-point level, the three datasets behave similarly on a large scale, but they show consistent differences in all seasons, with CCI distinctly displaying more expansive and larger variability in the equatorial Atlantic and also in the subtropical North Atlantic. The differences in climatology are less apparent. In particular, over the ATL3 region, CCI is systematically colder than ERSST and HadISST, and displays higher variability.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission [1] has been in orbit for over 11 years, and its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) in two dimensions keeps being fully operational. This II-year long lifetime of SMOS, so far, has enabled the calibration and Level-1 processor team to improve the calibration procedures and the image reconstruction resulting in a new version of the Level-1 data processor, v724. To present the main performance features of this new version and the improvement in the calibration procedures constitute the main objective and content of this presentation.

The novel Correlated Triple Collocation (CTC) analysis allows to assess three different data sources of similar spatial resolutions, but with two of them being correlated. In this study, the CTC was applied to estimate the unbiased random errors of the global soil moisture (SM) data provided by two L-band satellite missions —the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP)— and one numerical model—the ERA5-Land. The three existing SMOS SM products distributed by different research institutions were also analyzed. Preliminary results revealed that errors of SMOS and SMAP SM are correlated, with correlations of ∼0.5-0.6. Thus, only ERA5-Land can be considered as independent. The lowest error was obtained for SMAP (0.025 m3m−3), followed by ERA5-Land (0.036 m3m−3). Among the SMOS SM, SMOS-IC had the lowest error (0.046 m3m−3), SMOS-BEC showed an intermediate value (0.048 m3m−3), and SMOS-CATDS had the highest error (0.055 m3m−3).

ESA's Soil Moisture and Ocean Salinity (SMOS) mission [1] has been in orbit for over 9 years, and its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) in two dimensions is working well. The data products are generated using version v620 of the Level-1 operational processor, a version which entered into operation in Spring 2015. During last year a comprehensive data set was processed using a new processor version v720 and the assessment of the results is expected to be completed by mid 2019. In parallel to this evaluation of v720, the following version v730 of the Level-1 processor of SMOS has been already produced. This latter version is intended for investigating the capability to reduce Radio Frequency Interferences (RFI) by applying image processing techniques. This paper describes the major features and status of the two mentioned versions of the SMOS Level-1 processor, and importantly, aims at updating the remote sensing community on those aspects of the SMOS mission.

Arctic freshwater fluxes make this region key to regulate ocean currents and global climate. Hence, the Arctic Ocean sea surface salinity (SSS) knowledge is crucial to describe some of the processes that govern climate change.Recently, Barcelona Expert Center (BEC) deployed their version 2 of SSS Arctic data retrieved from Soil Moisture and Ocean Salinity mission (SMOS) mission. Nevertheless, in the context of the ESA Arctic+ initiative, BEC has planned to introduce improvements in all the processing levels. Some of the planned improvements include: (i) optimizing the projection grid of level 1, (ii) studying the performance of different dielectric models in the Artic region and (iii) producing an additional level 4 product. The SSS Arctic products from Soil Moisture Active Passive (SMAP) mission could be used to produce a level 4 by merging them with this new version of SSS level 3 produced from SMOS.Big data techniques are applied to produce debiased SSS maps. These techniques will be refined by introducing a new grouping method for the statistical study of the data.The aim of this work is to obtain a more accurate version of the Arctic salinity maps starting at 2011. The new SMOS SSS maps are expected to better capture the Arctic river plumes and thus they will help to better understand the freshwater inflow/outflow in the Arctic Ocean.

A new technique to mitigate Radio Frequency Interference (RFI) contamination for SMOS images was recently proposed. The technique consists in extrapolating the measured brightness temperature (BT) frequencies in the u/v coverage map outside the star coverage of SMOS. This technique has been implemented, and the quality improvements have been assessed with means of a triple collocation technique using SMOS and SMAP data.

The quality of the Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) maps has been noticeably improved in the last two years, in particular those produced at the Barcelona Expert Center (BEC). However, the BEC SSS maps are still affected by a latitudinal and seasonal bias. In this work, we comprehensively characterize the residual latitudinal and seasonal biases, which are used to correct de retrieved SSS, leading to a new generation of higher-quality SSS maps. The shape and regularity of this bias suggests that the effect, which produces this error, is not a poor characterization of the galaxy, some residual Total Electron Content (TEC) effect, or a poor characterization of the systematic Sea Surface Temperature (SST) effects on the SSS retrieval. It appears to be related to a geometrical effect associated to the relative position between the SMOS antenna, the Sun and the Earth.

Global Soil Moisture and Ocean Salinity (SMOS) Level 3 (L3) soil moisture (SM) products are being routinely distributed by the Barcelona Expert Centre (BEC). The quality and accuracy of these SM products have been demonstrated not only by direct validation, but also by its adoption in a wide range of applications. Recently, changes in SMOS Level 2 (L2) SM have led to the reprocessing of the BEC SMOS L3 SM. As in previous versions, a filtering and a weighted binning based on the uncertainty of the SM retrievals by means of the Data Quality Index (DQX) was applied for the L3 production. However, the DQX was modified in the latest L2 release (v650), which could possibly have an influence in the performance of the derived products.This study assesses the impact of the current DQX-based BEC L3 SM quality filtering and binning approach and the possibility of using an alternative strategy based on the chi-squared (χ2) parameter, which is defined as the cost function of the retrieval. The study is performed over continental USA using in situ SM from the U.S. Climate Reference Network (USCRN) as a benchmark. In both approaches, similar results were obtained in terms of correlation and unbiased root mean square difference (ubRMSD). Nevertheless, the χ2-based L3 SM is in general slightly wetter and has a lower dry bias than the DQX-based L3 SM. Further assessments will be performed to stablish the optimal filtering/binning of BEC SMOS L3 SM products.

Radio Frequency Interferences (RFI) are still an important source of contamination in SMOS data. The application of nodal sampling (NS) to brightness temperature images helps to mitigate the degradation that RFIs produce in geophysical retrievals. Nodal sampling has been extensive and successfully tested over open ocean and in the proximity to coastal regions. In this work, we assess the performances of NS over strongly RFI-contaminated ocean regions, particularly over semi-enclosed seas. These regions are especially challenging because of the strong contamination caused by the nearby RFI sources over land and the residual land-sea contamination.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission [1] has been in orbit for over 8 years, and its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) in two dimensions is working well. The data for this whole period has been and is being processed with the operational version of the current Level-l processor (version v620). Also a representative part of the same data set has been processed with a working version of a new processor (v720) which is now in preparation so that homogenous records of brightness temperatures have been made available. These rich and long data records have allowed learning important lessons from the in-flight experience, and shall eventually lead into the consolidation of the new Level-l processor version (v720) with its corresponding auxiliary calibration and configuration files. Once the improvements are confirmed the new processor version shall be recommended for the operational chain.

After more than eight years of Soil Moisture and Ocean Salinity (SMOS) aquisitions, an empirical characterization of the biases and the computation of an effective brightness temperature uncertainty is possible. In this work we show that both parameters strongly depend on the geographical location of the acquisition. Metrics based on the differences between expected and theoretical values of the bias and uncertainty are developed and used for a quantitative assessment of the locations where SMOS errors are currently being worse characterized. This characterization can be used for the definition of an empirical bias correction and a more accurate cost function which are expected to provide a better SMOS SSS product.

Novel SMOS satellite estimates of Sea Surface salinity in the Mediterranean Sea will be used to infer the spatial and temporal distribution of Alkalinity in this basin, exploiting the direct relationship between salinity and alkalinity. A proper validation of the derived variable will be performed against in-situ data, climatologies and model outputs. The resulting estimates of alkalinity in the Mediterranean Sea will be linked to the overall carbonate system in the broader context of ocean acidification assessment.

Since the beginning of the soil moisture and ocean salinity mission, the pervading presence of radio frequency interferences (RFI) has been one of the most problematic issues. The effect of an RFI is not just a hot spot but also six tails along the three main axes, and the general presence of ripples which degrade the quality of L1 brightness temperature snapshots. The standard mitigation technique is to apply an apodization (Blackman), but such a low-pass filter leaves traces of the tails and spreads the signal of the main lobes. New RFI mitigation techniques, such as nodal sampling, are very effective in reducing the impact of tails and ripples, but in some cases they lead to the spread of the RFI main lobe, with a significant loss of data on the affected area. In this letter, we propose a new technique to reduce their spread by an adaptive thresholding on a bandpass filtered version of the snapshot, with a significant recovery of data.

The triple collocation (TC) technique allows the simultaneous calibration of three independent, collocated data sources, while providing an estimate of their accuracy. In this paper, the TC is adapted to validate different salinity data products along the tropical band. The representativeness error (the true variance resolved by the relatively high-resolution systems but not by the relatively low-resolution system) is accounted for in the validation process. A method based on the intercalibration capabilities of TC is used to estimate the representativeness error for each triplet, which is found to impact between 15% and 50% the error estimation of the different products. The method also sorts the different products in terms of their resolving spatiotemporal scales. Six salinity products (sorted from smaller to larger scales) used were: the in situ data from the Global Tropical Moored Buoy Array (TAO), the GLORYS2V3 ocean reanalysis output provided by Copernicus, the satellite-derived Aquarius Level 3 version 4 (AV4) and Soil Moisture and Ocean Salinity (SMOS) objectively analyzed (SOA) maps, and the climatology maps provided by the World Ocean Atlas (WOA). This calibration study is limited to the year 2013, a year when all the products were available. This validation approach aims to assess the quality of the different salinity products at the satellite-resolved spatiotemporal scales. The results show that, at the AV4 resolved scales, the Aquarius product has an error of 0.17, and outperforms TAO, GLORYS2V3, and the SOA maps. However, at the SOA resolved scales (which are coarser than those of the Aquarius product because of the large OA correlation radii used), the SMOS product has an error of 0.20, slightly lower than that of GLORYS2V3, Aquarius, and TAO. The WOA products show the highest errors. Higher order calibration may lead to a more accurate assessment of the quality of the climatological products.

The Soil Moisture and Ocean Salinity (SMOS) and Aquarius satellite missions have produced the first sea-surface salinity (SSS) maps from space. The quality of the retrieved SSS must be assessed, in terms of its validation against sparse ground truth, but also in terms of its ability to detect and characterize geophysical processes, such as mesoscale features. Such characterization is sometimes elusive due to the presence of noise and processing artifacts that continue to affect state-of-the-art remote sensing SSS maps. A new method, based on singularity analysis, is proposed to contribute to the assessment of the geophysical characteristics of such maps. Singularity analysis can be used to directly assess the spatial consistency of the SSS fields and to improve the estimation of the wavenumber spectra slope through a new method, the singularity power spectra (SPS). To demonstrate the SPS performance and utility, we applied SPS to different gridded SSS maps, such as SMOS and Aquarius high-level products, the output of a numerical simulation, in situ reanalysis, and climatology, as well as to other sea-surface temperature products for reference. The singularity analysis and SPS methods reveal that both the SMOS level 4 and the Aquarius combined active passive products are both able to describe the geometry of the existing geophysical structures and provide consistent spectral slopes. This paper demonstrates that beyond the remaining sources of uncertainty in remote sensing SSS products, valuable dynamical information on the ocean state can be extracted from these SSS products.

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.

A new debiased non-Bayesian methodology has demonstrated to be very effective for the retrieval of Sea Surface Salinity (SSS) from brightness temperature (TB) measured by Soil Moisture and Ocean Salinity (SMOS) interferometric radiometer. Applying this methodology it is possible to retrieve SSS values in marginal seas or cold waters where the operational retrieval does not. Another important improvement is the possibility of defining a SMOS-based climatology to characterize spatial biases. Recently, using data from the Soil Moisture Active Passive (SMAP) mission, JPL has started to produce a new 9-km resolution TBproduct. The existence of such product offers the possibility of increasing the spatial resolution and quality of the mentioned SMOS SSS product using fusion techniques. The aim of this work is to produce high resolution SSS maps in marginal seas derived from the fusion of SMAP 9-km TBand SMOS non-Bayesian debiased SSS products.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been in orbit for over 6 years, and its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) in two dimensions keeps working well. The data for almost this whole period has been reprocessed with the new fully polarimetric version (v620) of the Level-1 processor which also includes refined calibration schema for the antenna losses. This reprocessing has allowed the assessment of an improved performance benchmark, a better understanding of the observations, and the preparation of a new version (v700) of the Level-1 processor with further potential.

New ocean products from the Soil Moisture and Ocean Salinity (SMOS) mission are being developed at the Barcelona Expert Centre. Besides the already operational 9-day and monthly sea surface salinity (SSS) products, two additional daily SSS products have been recently become operational: a simple user-friendly product containing all swath-based Level 2 data for each day, and a more elaborated product that uses multifractal fusion techniques to increase the spatial and temporal resolution. Finally, experimental BEC products are also presented which provide SSS values in regions strongly affected by radio-frequency interference (RFI). Recent progress on Land-Sea contamination mitigation has been applied to the BEC products.

This work is devoted to describe the new processing techniques that are being conceived, developed and implemented at the Barcelona Expert Centre (BEC) for the generation of sea surface salinity (SSS) maps from the Soil Mooisture and Ocean Salinity (SMOS) mission. Several algorithms to mitigate the ripples and sidelobes present in the SMOS brightness temperature (TB) images, to characterize the spatial correlations in the SMOS antennas, to correct for the systematic SSS-derived biases, and to improve the spatial and temporal resolution of the SSS products, have been recently developed and are presented in this paper.

Soil moisture and ocean salinity (SMOS) brightness temperature (TB) images and calibrated visibilities are related by the so-called G-matrix. Due to the incomplete sampling at some spatial frequencies, sharp transitions in the TB scenes generate a Gibbs-like contamination ringing and spread sidelobes. In the current SMOS image reconstruction strategy, a Blackman window is applied to the Fourier components of the TBs to diminish the amplitude of artifacts such as ripples, as well as other Gibbs-like effects. In this paper, a novel image reconstruction algorithm focused on the reduction of Gibbs-like contamination in TB images is proposed. It is based on sampling the TB images at the nodal points, that is, at those points at which the oscillating interference causes the minimum distortion to the geophysical signal. Results show a significant reduction of ripples and sidelobes in strongly radio-frequency interference contaminated images. This technique has been thoroughly validated using snapshots over the ocean, by comparing TBs reconstructed in the standard way or using the nodal sampling (NS) with modeled TBs. Tests have revealed that the standard deviation of the difference between the measurement and the model is reduced around 1 K over clean and stable zones when using NS technique with respect to the SMOS image reconstruction baseline. The reduction is approximately 0.7 K when considering the global ocean. This represents a crucial improvement in TB quality, which will translate in an enhancement of the retrieved geophysical parameters, particularly the sea surface salinity.

Singularity analysis has proven to be a complementary tool to the Advanced Scatterometer (ASCAT) inversion residual (or maximum likelihood estimator) in terms of wind quality control (QC). In this paper, a new implementation scheme of singularity exponent (SE) is developed for ASCAT data analysis. It combines the wavelet projections of the gradient measurements of multiple parameters into the analysis, ensuring that the analyzed parameters contribute equally to the final singularity map. Therefore, the underlying geophysical phenomena in the different ASCAT-derived parameters can be effectively revealed simultaneously on a unique map of SEs. The validation using both buoy winds and European Centre for Medium-Range Weather Forecasting forecast wind output shows that the newly derived SE significantly improves the current ASCAT wind QC. In particular, poor-quality ASCAT measurements at low-wind and high-variability conditions (w <; 4 m/s) can be effectively screened using the new SE.

Since its launch in 2009, the European Space Agency mission Soil Moisture and Ocean Salinity (SMOS) has provided valuable information on soil moisture, sea surface salinity data, and other geophysical variables. Due to its innovative instrument, an L-band 2-D synthetic aperture interferometric radiometer, SMOS is able to provide high-resolution L2 data (as compared with other L-band missions). However, SMOS processing is complex, giving rise to the emergence of some unexpected biases. In this paper, we have analyzed the spatial structure of two-point correlations owing to the SMOS synthetic antenna, finding that they are not negligible. Those correlations can be characterized by means of effective point spread functions (PSFs). This paper indicates that the SMOS PSF matrix can be computed in a fast way from measured data without the need for any model or auxiliary data. Furthermore, this matrix can be described in terms of a convolution kernel. The knowledge of that convolution kernel can be used to improve the quality of the SMOS image and to assess the effect of changes of processing procedures, including calibration methods.

In this paper, anomalous spatial gradients are investigated by an image processing method, known as singularity analysis, which is proposed to complement the current Advanced Scatterometer (ASCAT) quality control (QC) by using the singularity exponent (SE). The quality of ASCAT winds is known to be generally degraded, with increasing values of the inversion residual or maximum-likelihood estimator (MLE). In the current ASCAT Wind Data Processor (AWDP), an MLE-based QC is adopted to filter poor-quality winds, which has proven to be effective in screening artifacts in the ASCAT winds, associated with increased subcell wind variability and other phenomena such as confused sea state. However, some poorly verifying winds, which appear in areas with moist convection, are not screened by the operational QC. The extension of the QC procedure with SEs is investigated, based on a comprehensive analysis of quality-sensitive parameters, using the European Centre for Medium-range Weather Forecasts (ECMWF) model winds, the Tropical Rainfall Measuring Mission's (TRMM) Microwave Imager (TMI) rain data, and tropical buoy wind and precipitation data as reference, taking into account their spatial and temporal representation. The validation results show that the proposed method indeed effectively removes ASCAT winds in spatially variable conditions. It filters three times as many wind vectors as the operational QC, while preserving verification statistics with local buoys. We find that not the rain itself, but the extreme local wind variability associated with rain appears to generally decrease the consistency between ASCAT, buoy, and ECMWF winds.

The consistency of MIRAS amplitude calibration along the mission is a key issue to ensure a stable and accurate long-term dataset of ESA's SMOS soil moisture and ocean salinity data. Recent studies have revealed latitudinal and seasonal drifts in some receivers of the instrument. This paper focuses on the assessment of MIRAS amplitude calibration consistency using the available long-term dataset. A methodology is presented to evaluate residual amplitude calibration errors and to investigate the origin of these deviations.

The current ASCAT Wind Data Processor (AWDP) uses the 2D variational ambiguity removal (2DVAR) scheme to select a unique wind field from a set of retrieved ambiguities. This has led to spatially consistent and accurate ASCAT Level 2 wind products. Nevertheless, recent research shows that 2DVAR picks up the wrong wind direction ambiguities in regions where the background field shows mislocation of fronts (convergence) or misses convective systems. In this paper, the exploitation of complementary information derived from the inversion and from an image processing technique is proposed to improve the current 2DVAR for ASCAT in mesoscale conditions.

The Advanced Scatterometer (ASCAT) onboard the Metop satellite series is designed to measure the global ocean surface wind vector. Generally, ASCAT provides wind products at excellent quality. Occasionally, though, ASCAT-derived winds are degraded by rain. Therefore, identification of rain can help to better understand the rain impact on scatterometer wind quality and to develop a proper quality control (QC) approach for scatterometer data processing. In this letter, an image processing method, known as singularity analysis (SA), is used to detect the presence of rain such that rain-contaminated wind vector cells are flagged. The performance of SA for rain detection is validated using ASCAT Level-2 data collocated with satellite radiometer rain data. The rain probability as a function of SA singularity exponent is calculated and compared with other rain sensitive parameters, such as the wind inversion residual or maximum-likelihood estimator (MLE). The results indicate that the SA is effective in detecting ASCAT rain-contaminated data. Moreover, SA is a complementary rain indicator to the MLE parameter, thus showing great potential for an improved scatterometer QC.

In this study, the rain impact on the ASCAT operational Level 2 retrieved wind quality and the effectiveness of the quality control (QC) are investigated. It is shown that ASCAT is much less affected by direct rain effects, such as ocean splashing, but effects of increased wind variability appear to dominate. The operational QC proves to be effective in screening these artifacts, but at the expense of valuable winds. An image processing method, known as the singularity analysis, is proposed in this study to complement the current QC, and its potential is illustrated.

A prerequisite for the successful retrieval of geophysical parameters from remote sensing measurements is the development of an accurate forward model. The European Space Agency Soil Moisture and Ocean Salinity (SMOS), carrying onboard an L-band interferometric radiometer (Microwave Interferometric Radiometer using Aperture Synthesis), was launched on November 2009. Due to the lack of L-band passive ocean measurements from space, several prelaunch forward models were developed and initially used in the SMOS ocean salinity operational processor. In this paper, an update of the prelaunch semi-empirical forward model is presented, using for the first time, real SMOS data. In particular, the ocean surface emissivity modulation at L-band due to rough sea surface is reviewed and reanalyzed. A new model definition is provided with the help of a simple neural network. The improvement is quantified in terms of retrieved salinity accuracy compared with the climatology and concerns essentially the range of wind speeds higher than 12 m·s-1.

The quality of the Ku-band scatterometer-derived winds is known to be degraded by the presence of rain. Little work has been done in characterizing the impact of rain on C-band scatterometer winds, such as those from the Advanced Scatterometer (ASCAT) onboard Metop-A. In this paper, the rain impact on the ASCAT operational level 2 quality control (QC) and retrieved winds is investigated using the European Centre for Medium-range Weather Forecasts (ECMWF) model winds, the Tropical Rainfall Measuring Mission's (TRMM) Microwave Imager (TMI) rain data, and tropical buoy wind and precipitation data as reference. In contrast to Ku-band, it is shown that C-band is much less affected by direct rain effects, such as ocean splash, but effects of increased wind variability appear to dominate ASCAT wind retrieval. ECMWF winds do not well resolve the airflow under rainy conditions. ASCAT winds do but also show artifacts in both the wind speed and wind direction distributions for high rain rates (RRs). The operational QC proves to be effective in screening these artifacts but at the expense of many valuable winds. An image-processing method, known as singularity analysis, is proposed in this paper to complement the current QC, and its potential is illustrated. QC at higher resolution is also expected to result in improved screening of high RRs.

SMOS salinity inversion consists of minimizing the residual between measured and modeled brightness temperatures. The minimization procedure is a great challenge and crucial step, but its success depends on the quality of the forward model. Consequently, we present an empirical update of pre-launch L-band emissivity forward models, where the essential improvement is related to the emissivity by a rough sea surface. The improvement is quantified in terms of retrieved salinity accuracy compared to the climatology.

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.

With the advent of ESA's SMOS Mission, we have the opportunity for the first time of measuring Sea Surface Salinity (SSS) from the space and at a synoptic scale. However, the MIRAS instrument onboard SMOS is a new concept of instrument, and the adjustment and calibration of this interferometric radiometer poses great challenges. In this paper, we show the present status of Level 3 and 4 salinity maps, which are supposed to give accurate climatological descriptions of SSS, describing the attained accuracy and analyzing the geophysical consistence of those maps. A discussion on future improvements is also issued.

Preliminary results obtained during the commissioning phase of the Soil Moisture and Ocean Salinity (SMOS) mission are described, devoting special attention to the characterization of the systematic errors found in the measurements and the corresponding impact in the retrieved salinity product. The identified issues and objectives to consolidate and improve the processing chain are also described.

Recent advances in the theory of turbulence, with the introduction of the Microcanonical Multifractal Formalism has favored the development of new techniques for the analysis of remotely sensed data, particularly of scalers as SST. In this work we show that these techniques allow to uncover a fascinating picture in which many features of global ocean circulation patterns emerge in a distinct way. Applications include the characterization of transport, estimation of eddy-mediated mixing, the characterization of the coupling of ENSO perturbation with the equatorial instabilities and a long etc.

In this study, we show how different spectral channels of NOAA-AVHRR acquired data can be used to produce a synthetized signal aimed at helping the characterization of plumes associated to fire events. The synthetized signal is computed using a reconstruction formula in the multifractal microcanonical formalism (herein referred to as MMF). The MMF is a recent development in the analysis of complex signals, well adapted to the study of turbulent acquired data, for instance geophysical fluids. It allows the computation, at each point of the signal's domain, of a singularity exponent, characteristic of the scale behaviour of the signal around that point; singularity exponents provide information about the strengths of the transitions inside a signal, and they are related to the multifractal hierarchy associated to structure functions in Fully Developped Turbulence (FDT). In the MMF, it is possible to reconstruct a turbulent signal from the manifold of most singular exponents. We make use of this property by computing supergeometric structures from a thermal infrared channel in NOAA-AVHRR acquired data, and we use the signal's gradient coming from other channels to reconstruct a signal in which plume pixels are easier to detect. This methodology is based on the turbulent properties of the plume accessible from the thermal infrared band; the algorithm is detailed and applied on a specific example, showing a new spatially-based method for helping the determination of plume pixels in NOAA-AVHRR data.

The last generation of satellites leads to the very high-resolution images which offer a high quality of detailed information about the Earth's surface. However, the exploitation of such images becomes more complicated and less efficient as a consequence of the great heterogeneity of the objects displayed. In this paper, we address the problem of edge-preserving smoothing of high-resolution satellite images. We introduce a novel approach as a preprocessing step for feature extraction and/or image segmentation. The method we propose is related with the idea of resolution reduction and is derived from the multifractal formalism used for image compression. First, a multifractal decomposition scheme allows to extract the most singular transitions of the image. Then, an entropy-based criterium enables to consider a particular manifold composed with the most, simultaneously, relevant and singular pixels. Finally, a reconstruction scheme performed over this manifold provides an approximation of the original image. Such an approach is ideal, as it assumes that objects can be reconstructed from their boundary information, and it provides presegmented images where the main structures are preserved.

It is thought that satellite thermal infrared (IR) images can aid to the detection of precipitation, an interesting possibility due to the existence of geostationary satellites with thermal IR sensors which would enable a good spatial and temporal tracking of rain and storms. In this letter, we explore the application of multiscale/multifractal techniques in the design of new methods for the assessment and tracking of pluviometry. We first identify the main streamlines by a singularity analysis of the wavelet projections of the IR record. From the streamlines, we derive a proxy scalar image that represents the result of pure horizontal advection. From the comparison of original and proxy we localize the places at which horizontal advection fails, which we identify with convection places. We illustrate our methodology with thermal IR images from Metosat acquired during heavy tropical rainfall, and compare the results with some data from the Tropical Rainfall Measuring Mission satellite.

International audience

Real-world images are complex objects, difficult to describe but at the same time possessing a high degree of redundancy. A very recent study on the statistical properties of natural images reveals that natural images can be viewed through different partitions which are essentially fractal in nature. One particular fractal component, related to the most singular (sharpest)transitions in the image, seems to be highly informative about the whole scene. In this paper we will show how to decompose the image into their fractal components.We will see that the most singular component is related to (but not coincident with) the edges of the objects present in the scenes. We will propose a new, simple method to reconstruct the image with information contained in that most informative component.We will see that the quality of the reconstruction is strongly dependent on the capability to extract the relevant edges in the determination of the most singular set. We will discuss the results from the perspective of coding, proposing this method as a starting point for future developments.

We present a formalism that leads naturally to a hierarchical description of the different contrast structures in images, providing precise definitions of sharp edges and other texture components. Within this formalism, we achieve a decomposition of pixels of the image in sets, the fractal components of the image, such that each set contains only points characterized by a fixed strength of the singularity of the contrast gradient in its neighborhood. A crucial role in this description of images is played by the behavior of contrast differences under changes in scale. Contrary to naive scaling ideas where the image is thought to have uniform transformation properties (Field, 1987), each of these fractal components has its own transformation law and scaling exponents. A conjecture on their biological relevance is also given.