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.

In the framework of the ESA regional initiative Arctic+ Salinity, a new Sea Surface Salinity (SSS) product (v4) has been developed from the Soil Moisture and Ocean Salinity (SMOS) measurements. The data processing starts from the SMOS Level 0 (L0) data and applies specific algorithms for reducing the contamination close to the ice edge at Level 1 (L1) (in the Brightness Temperature reconstruction) and at Level 2 (L2) (in the correction of biases in the salinity retrieval). In this work we explain these algorithms and present the new 13-year temporal series of SMOS SSS maps as well as the main results of the quality assessment.

The Faraday Rotation (FR) is a non-negligible effect at the L-band, which is the operation frequency of the Soil Moisture and Ocean Salinity (SMOS) mission. This effect introduces a rotation in the electromagnetic field polarization when propagating through the ionosphere that must be compensated. Recently, a methodology was developed in order to retrieve the Vertical Total Electron Content (VTEC) from SMOS radiometric data with the aim to better correct the Faraday rotation effect [1]. In that work, systematic patterns in the retrieved Faraday Rotation Angle (FRA) were detected. In this paper, these systematic patterns are characterized and corrected to improve the quality of the retrieved VTEC maps. These maps can be then re-used in the SMOS level 2 processor for the correction of the FRA in the mission. The impact of using the SMOS-derived VTEC maps instead of the VTEC data from GPS measurements on the ocean brightness temperature measurement has also been analyzed. Results of this analysis show that the usage of those maps allows a significant enhancement in the quality of the brightness temperatures, which will lead to an improvement on salinity retrievals.

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.

In this work, a methodology to correct the Faraday Rotation Angle (FRA) is presented. It consists of calculating a systematic error pattern introduced by MIRAS, which is calculated in zones where the FRA tends to zero. Once calculated, this error is subtracted in the rest of the measurements. In both cases, the FRA is calculated following a process of minimization of the equation that relates the SMOS full polarization radiometric measurements to that parameter.

A method for internally calibrating microwave total power radiometers by using only one level of noise injection is presented. It is based on having a previous accurate characterization of the receiver noise temperature, which used de facto as a second calibration standard. The method proves to be at least equivalent to the classical two level, as demonstrated through their intercomparison using the data provided by the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) on board the European Space Agency Soil Moisture and Ocean Salinity (SMOS) Satellite. The long-term stability in terms of retrieved brightness temperature using both methods has similar trends with a small advantage for the one-point approach proposed here.

Temporal stability of the radiometer MIRAS, on board the SMOS satellite, is analyzed using long series of data spanning for about 6 years. Brightness temperature retrieved from one orbit per day over the Pacific Ocean is compared against a forward model and the difference between both is analyzed. Plots of the average in central latitudes as a function of time show small seasonal ripples (~±0.5 K) that depend on the calibration strategy chosen. An extremely low drift of only several mK/year is found and in some cases disappears. Hovmoller plots for the same data, consisting of brightness temperature biases as a function of latitude and time, confirm the small ripples and show some latitude dependent structures, probably coming from geophysical signatures.

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.

In this work, a refined methodology to correct the Faraday Rotation angle (FRA) for the SMOS mission is proposed. The method is based on calculating the FRA using the SMOS full-pol radiometric data to obtain VTEC maps applying spatiotemporal filtering techniques, to then making possible the compensation of the FRA effect. By this way, the FRA would be corrected from the data that is measuring the satellite and not using an external database.

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.

In this work, a new methodology to estimate the Faraday Rotation Angle (FRA) from SMOS full-pol brightness temperature is presented. This approach is focused on retrieving the Vertical Total Electron Content (VTEC) and then, the FRA. for most of the SMOS overpass, with the final aim of improving geophysical retrievals.

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 impact of Radio Frequency Interference (RFI) is a very serious problem for spaceborne microwave radiometry. Many Soil Moisture Ocean Salinity (SMOS) images show serious contamination by the RFI. SMOS is even more impacted by the RFI than real aperture case because the grating lobes are usually higher in Synthetic Aperture Interferometric Radiometer (SAIR) compared to real aperture one. RFI effects should properly be mitigated or filtered out to retrieve the geophysical parameters from SMOS measurements. This work presents a feasibility study of RFI mitigation/filtering for SAIRs. Instead of dealing with brightness temperature image directly, RFI filtering of the subspace of covariance matrix is introduced.

It has recently been found that the visibility denormalization process introduces a spatial error distribution due to small sporadic offset jumps in the PMS detectors. The radiometric impact of this error at system level is very low. However, due to the good performance of the SMOS instrument, a study has recently been conducted to evaluate the amplitude of such visibility errors and develop a mitigation technique. The main results of this study are summarized in this presentation.

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.

It has recently been demonstrated that boresight averaged Faraday rotation angle (FRA) can be retrieved directly from SMOS full-pol radiometric data. However, in order to extend FRA retrievals to the full Alias-Free Field of View (AF-FoV), SMOS relatively poor pixel radiometric sensitivity and accuracy must be compensated by spatial and temporal averaging. This requires some kind of tradeoff to constrain systematic FRA estimation bias both within SMOS AF-FoV and along the orbit. This work presents the first results given by a SMOS end-to-end FRA simulator, currently under development, that is used to trim and assess the performance of several FRA retrieval approaches.

Since each of the individual elements of the MIRAS array is a total power radiometer, the zero-spacing visibility can be obtained by the average of all the corresponding antenna temperatures. The main advantage of this option with respect to using the NIR measurements is that amplitude calibration is more consistent between zero-spacing visibility and the rest. On the other hand, total power radiometers are not usually as stable as noise injection radiometers, so a small loose of stability could be expected. Preliminary results show, however, similar performance.

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.

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.

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission has been providing L-band brightness temperature (BT) using its instrument, the Microwave Imaging Radiometer using Aperture Synthesis. In the measurements, the negative effect of radio frequency interference (RFI) is clearly present, deteriorating the quality of geophysical parameter retrieval. Detection and geolocation of RFI sources are essential to remove or at least mitigate the RFI impacts and ultimately improve the performance of parameter retrieval. This paper discusses a new approach to SMOS RFI source detection, based on the MUltiple SIgnal Classification (MUSIC) algorithm. Recently, the feasibility of MUSIC direction-of-arrival estimation has been shown for the RFI source detection of the synthetic aperture interferometric radiometer. This paper refines the MUSIC RFI source detection algorithm and tailors it to the SMOS scenario. To consolidate the RFI source detection procedure, several required steps are devised, including the rank estimation of the covariance matrix, local peak detection and thresholds, and multiple-snapshot processing. The developed method is tested using a number of SMOS visibility samples. In the test results, the MUSIC method shows an improvement on the accuracy and precision of the RFI source geolocation, compared with a simple detection method based on the local peaks of BT images. The MUSIC results especially outperform the SMOS BT image on the spatial resolution.

The Dome-C region, in the East Antarctic Plateau, has been used for calibration/validation of satellite microwave radiometers since the 1970's. However, its use as an independent external target has been recently questioned due to some spatial inhomogeneities found in L-band airborne and satellite observations. This work evidences the influence of the Antarctic ice thickness spatial variations on the measured SMOS and Aquarius brightness temperatures (TB). The possible effects of subglacial water and bedrock on the acquired radiometric signals have also been analyzed. A 3-months no-daylight period during the Austral winter has been selected. Four transects over East Antarctica have been defined to study the spatial variations. A good agreement between SMOS and Aquarius TBchanges and ice thickness variations over the whole Antarctica has been observed, obtaining linear correlations of 0.6-0.7 and slopes of 8.6-9.5 K/km. The subglacial lakes may affect the vertical physical temperature profile and/or the dielectric properties of the ice layers above. As expected, the subglacial bedrock is not contributing to the measured TB, since the maximum estimated L-band penetration depth is ~1-1.5 km.

In the SMOS measurements, the negative effect of Radio-Frequency Interference (RFI) is clearly present, deteriorating the Brightness Temperature (BT) images. RFI sources have to be monitored and accurately geolocated in order to improve the quality of geophysical parameter retrieval from the SMOS BT image, and eventually develop algorithm to cancel them. This paper present a case study of SMOS RFI source detection and geolocation based on MUSIC (MUltiple SIgnal Classification) algorithm. Thanks to the high angular/spatial resolution of MUSIC algorithm, the RFI sources densely populated in East Asia can be separately geolocated, which is difficult using SMOS standard brightness temperature images.

Radio-frequency interference (RFI) seriously affects the retrieval of geophysical parameters from the measurements of microwave radiometers. An accurate geolocation of the RFI is crucial to effectively switch off illegal transmitters. In this letter, a new RFI localization method is proposed to improve the achievable angular resolution by using beamforming and direction-of-arrival (DOA) estimation techniques. The proposed RFI localization techniques can be employed in synthetic aperture interferometric radiometers, such as the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission. Two DOA estimation techniques are tested for the RFI localization: Capon and MUSIC. The feasibility of these methods is demonstrated with SMOS data. In the test results, the MUSIC beamforming shows a better performance of RFI localization than the SMOS Fourier imaging and the Capon, in terms of accuracy and resolution.

Remotely sensed measurements acquired by the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite are processed in a uniform equal-area grid, the Icosahedral Snyder Equal Area (ISEA) 4H9. Brightness temperature measurements are projected onto that grid (the so-called Level 1C), as well as sea surface salinity and soil moisture estimates (Level 2). The ISEA grid has been chosen for its characteristics of equal area and almost uniform intercell spacing. Nevertheless, when considering the SMOS viewing geometry, the measurement footprint size, and the processing applied to those measurements, this choice may be revisited. With this objective, the ISEA 4H9 grid is compared to other equal-area grids with different sizes and orientations with respect to the satellite track. The best configuration resulted to be a 25-km-width grid symmetrical with respect to satellite track. This grid appeared to be better suited for improving SMOS Level 2 retrieval algorithms as well as to serve as input for higher level data production, since it best accounts for the instrument's viewing geometry and substantially reduces the correlation between adjacent grid cells.

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.

Surface Soil Moisture (SSM) affects the soil surface energy balance and thus affects the Land Surface Temperature (LST), and viceversa. Currently, LST and SSM are remotely sensed using TIR sensors and L-band radiometers, respectively. The NASA's Terra/Aqua missions provide full coverage of LST measurements under clear sky conditions using MODIS. The ESA's SMOS mission is the first satellite providing frequent SSM and ocean salinity observations at global scale. In this paper, a sensitivity study about the relationship of the LST and SSM is performed using in-situ measurements from the REMEDHUS network and spaceborne observations from MODIS and SMOS. Results show that the correlation between SSM and LST (both in-situ and remotely sensed) is highest using the daily maximum LST. This could help improving SSM algorithms and deriving new SSM products at higher resolution from the synergy of microwave and TIR observations.

The spectral window at L-band (1.400 - 1.427 GHz) is reserved for passive microwave remote sensing. This band is well-suited to retrieve soil moisture and ocean salinity due to emissivity of soil and seawater decreases with moisture and salinity, respectively, affecting microwave radiation of the Earth's surface. Nowadays, there are two space missions devoted to Earth observation with L-band radiometers on-board: the SMOS mission from the ESA and the Aquarius/SAC-D mission from the NASA and CONAE. Both missions are providing the first TB measurements of the Earth's surface at 1.413 GHz. Thus, it is a great opportunity to compare SMOS and Aquarius TBs and verify the continuity and consistency of the data. This inter-comparison is a key requirement needed to use data of both radiometers for meteorological, hydrological and climatological studies on a long term.

An innovative downscaling approach to obtain fine-scale soil moisture estimates from 40 km SMOS observations has been developed. It optimally blends SMOS multi-angular and full-polarimetric information with MODIS visible/data into high resolution soil moisture maps. The core of the algorithm is a model that linksmicrowave/optical sensitivity to soilmoisture and linearly relates the two instruments across spatial scales. This algorithm has been implemented at SMOS-BEC facilities and near real-time maps of disaggregated soil moisture over the Iberian Peninsula are being distributed. In this work, the temporal and spatial variability of these maps is evaluated through comparison with ground-basedmesurements acquired at the REMEDHUS soil moisture network, in the central part of the Duero basin, Spain. Results from a two-year time-series comparison show that downscaled soil moisture maps compare well with in situ data and nicely reproduce soil moisture dynamics at a 1 km spatial scale.

The local oscillators (LOs) of the Soil Moisture and Ocean Salinity mission payload are used to shift the operating frequency of the 72 receivers to an optimal intermediate frequency needed for the signal processing. The LO temperature variations produce phase errors in the visibility, which result in a blurring of the reconstructed brightness temperature (Tb) image. At the end of the commissioning phase, it was decided to calibrate the LO every 10 min while waiting for a more in-depth analysis. During short periods of time, the LO calibration has been performed every 2 min to assess the impact of a higher calibration rate on the quality of the data. In this paper, by means of a decimation experiment, the relative errors of 6- and 10-min calibration interval data sets are estimated using the 2 min as a reference. A noticeable systematic across- and along-track pattern of amplitude ±0.3 K is observed for Tb differences between 10 and 2 min, whereas this is reduced between 6 and 2 min. A simulation experiment confirms that the nature of such systematic pattern is due to the visibility phase errors induced by the LO calibration rate. Such pattern is propagated into the sea surface salinity (SSS) retrievals. Overall, the SSS error increase (relative to the 2 min SSS data) is about 0.39 and 0.14 psu for the 10- and 6-min data sets, respectively. This paper shows that a LO calibration rate of at least 6 min would noticeably improve the SSS retrievals.

The Local Oscillators (LO) of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) onboard the Soil Moisture and Ocean Salinity (SMOS) satellite are used to maintain the operating frequency of the 69 receivers. The phase of the LO drifts over time, in turn blurring the MIRAS brightness temperature (TB) measurements. After a pre-launch assessment, it was decided to calibrate the LO every 10 minutes to reduce the phase drifts. During short periods of the first 2.5 years of SMOS mission, the LO calibration has been performed every 2 minutes to assess the impact of a higher calibration frequency on the quality of the data. In this study, relative differences (10-min TBs versus 2-min TBs) of about 0.3 K are shown, which lead to non-negligible relative differences of about 0.2-0.3 practical salinity units (psu) in the retrieved sea surface salinity (SSS). However, when performing independent validation against Argo float SSS data at Level 3 (spatio-temporally averaged SSS products), no significant differences are found between 10-min and 2-min data. This is due to the fact that current SMOS SSS accuracy (relative to Argo) is about 0.6-0.8 psu, thus masking the relatively smaller LO calibration frequency effect.

Interpolation strategies for calibration of the Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency are tested and compared. Calibration strategy (how and how often) is critical in achieving the required performance of any instrument, but it is even more important in very complex instruments such as the new family of synthetic aperture interferometric radiometers and, in particular, in the Microwave Imaging Radiometer by Aperture Synthesis instrument aboard the SMOS mission. On one hand, frequent calibration reduces the available observation time. On the other hand, the calibration requirements for soil moisture applications are more relaxed than those for ocean salinity, so the intercalibration time requirements are very different. Since SMOS drifts are stationary, half-orbit information is available to perform different interpolation strategies. In this letter, these approaches are tested to estimate the calibration parameters between consecutive calibrations. The average root-mean-square phase error is then used to find the optimum interpolation strategy and intercalibration time. On the other side, in real-time instruments, the “future” calibration data are not available at the time of taking the measurements, and predictors are required to estimate the evolution of the calibration parameters from past data only. For these systems, the extended Kalman filter can be used. The intercalibration time in a real-time instrument is evaluated, and the requirements and performances are compared to offline instruments.

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.

After the successful launching of the Soil Moisture and Ocean Salinity satellite in November 2009, continuous streams of data started to be regularly downloaded and made available to be processed. The first six months of operation were fully dedicated to the In-Orbit Commissioning Phase, with an intense activity aimed at bringing the satellite and instrument into a fully operational condition. Concerning the payload Microwave Imaging Radiometer with Aperture Synthesis, it was fully characterized using specific orbits dedicated to check all instrument modes. The procedures, already defined during the on-ground characterization, were repeated so as to obtain realistic temperature characterization and updated internal calibration parameters. External calibration maneuvers were tested for the first time and provided absolute instrument calibration, as well as corrections to internal calibration data. Overall, performance parameters, such as stability, radiometric sensitivity and radiometric accuracy were evaluated. The main results of this activity are presented in this paper, showing that the instrument delivers stable and well-calibrated data thanks to the combination of external and internal calibration and to an accurate thermal characterization. Finally, the quality of the visibility calibration is demonstrated by producing brightness temperature images in the alias-free field of view using standard inversion techniques. Images of ocean, ice, and land are given as examples.

After the six-month long In-Orbit Commissioning Phase (IOCP) the SMOS satellite started to work in its fully operational mode. During the IOCP, the payload MIRAS was completely characterized, both in short- and long-term, and the optimum calibration rate for in-flight operation was established. The results show that the amplitude of the visibility is very stable, thus allowing a very low calibration rate, and that the phase has a systematic and periodic variation, easily tracked with short but frequent internal calibration sequences. Absolute calibration for antenna temperature is carried out by external maneuvers to account for drift in the reference Noise Injection Radiometer. Brightness temperature images of good quality are obtained by inverting the calibrated visibility. The images show features compatible with ocean salinity over ocean and soil moisture over land.

Soil Moisture and Ocean Salinity (SMOS) mission is the second of European Space Agency's (ESA) Living Planet Programme Earth Explorer Opportunity Missions. SMOS's objective is to provide global and frequent Soil Moisture and Sea Surface Salinity maps. The single payload embarked on SMOS is the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS), it is a 2D interferometric radiometer operating at the protected L-band with a nominal frequency of 1413.5 MHz. Since SMOS is the first 2D interferometric radiometers put in orbit so far, the characterization of the interferometrically measured brightness temperatures is an attractive topic for the scientific community. This study is focused on the estimation of the systematic antenna-based pattern in the measured brightness temperatures. Two improvements to the currently used method (Ocean Target Transformation) are proposed: 1) The elimination of the use of any forward model in the estimation of the bias. 2) The homogenization of the geophysical parameters distribution within the SMOS Field of View. Ocean Target Transformation is introduced in section 2, the proposed model-free methodology is described in section 3, while the effect of homogenizing the geophysical parameters distribution inside the FOV is assessed in section 4. The main conclusions are presented in section 5.

The SMOS in-orbit commissioning phase will start at launching and will last about 6 months. During this phase an extensive activity will start, particularly aimed at providing full confidence on the products that the mission will produce during its operational phase. As part of this activity, the payload MIRAS will be fully characterized using specific orbits dedicated to check all instrument modes. First, the procedures carried out during the on-ground characterization will be repeated so as to obtain a realistic temperature characterization and updated internal calibration. Additionally, a new concept of external calibration, consisting of periodic sky looks, will be tested providing the first-ever absolute instrument calibration parameters. Finally, the imaging capability of the instrument both in dual polarization and full polarimetric will be assessed using images of the Earth surface and the Galaxy. As a final result, the higher level overall performance parameters, such as stability, radiometric sensitivity, radiometric accuracy and absolute accuracy will be evaluated.

The one-point calibration method will be tested during MIRAS/SMOS commissioning phase in order to assess its capability to remove the effects of the noise distribution network. This would provide and enhanced performance of the instrument by reducing systematic errors in the imaging process (image distortion), the so-called pixel bias. This work reviews the theoretical background of the one-point calibration approach and presents an error assessment.

The on-ground characterization of the synthetic aperture radiometer onboard the Soil Moisture and Ocean Salinity mission is described. Characterization includes basic functionality, internal calibration, thermal cycling, response to point and flat sources, self-radio-frequency interference, and others. The description of the different tests performed as well as the detailed results are provided. The results show that the instrument is very stable and has all gains and offsets consistent with the ones obtained at subsystem level. On the other hand, the phase of the visibility has a larger variation with temperature than expected, a small signal leakage from the local oscillators is present, and a small interference from the X-band transmitter during short periods of time has been detected. The implementation of internal-calibration procedures, along with the accurate thermal characterization performed, have been used to produce highly accurate brightness-temperature values well within specifications.

A software package to fully process SMOS data from level 0 up to brightness temperature at antenna plane (level 1B) is described. The raw data downloaded from the payload are converted to correlations and voltages; then to calibrated visibility and antenna temperature; and finally to brightness temperature. Results at different levels are saved in separate files for further post-processing and a visualization tool is provided in order to check the data at various levels, as well as producing brightness temperature maps in the (xi, eta) domain. The software has been developed independently from the official SMOS-mission Level-1 processor but a detailed process of cross-checking of data at all levels is being carried out in order to consolidate the end product of both.

This paper summarizes the procedure to estimate the Fringe Washing Function shape. Several tests were conducted at the Maxwell anechoic chamber at ESA-ESTEC facilities on spring 2007. A wrap-up of theoretical and experimental results after data analysis is presented.

The Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) is the single payload of the SMOS (Soil Moisture and Ocean Salinity) mission by the European Space Agency (ESA) to be launched by spring 2009. This work presents the success criteria tool that has been designed to easily assess the impact of any perturbation on the payload system performance. The tool is illustrated by showing some of the main results obtained during the MIRAS system performance tests by the prime contractor, EADS-CASA Espacio, Spain, at ESA premises in ESTEC, The Netherlands, and after payload integration with the Proteus platform at Thales Alenia Space in Cannes, France.

This paper presents a method to check the consistency of the amplitude calibration in interferometric radiometers devoted to Earth observation This tool is based in the intrinsic properties of the internal calibration scheme and can be used to systematically assess in-orbit the quality of the amplitude calibration coefficients. This technique is illustrated taking into account the results of the validation and system performance tests recently undertaken by the MIRAS instrument, the single payload of the Soil Moisture and Ocean Salinity mission of the European Space Agency.

This paper describes AMIRAS, an airborne demonstrator of the Microwave Imaging Radiometer with Aperture Synthesis, which is the instrument onboard ESA's Soil Moisture and Ocean Salinity (SMOS) mission. The main electrical, mechanical, thermal, and control elements of the demonstrator are shown, together with its capabilities and performances as demonstrator of the spaceborne instrument. AMIRAS main tests inside an anechoic chamber, field ground experiments, and its first two maiden flights are reported, and some results of these tests are highlighted. AMIRAS will further be used in some calibration and validation campaigns of the SMOS mission.

MIRAS has two types of in-orbit calibration: external, in which the instrument makes a maneuver to point to the cold sky, and internal, in which noise is injected to the receivers. This one in turn has two modes, one of short duration carried out periodically interspersed with scene measurements, and other lasting a full orbit and used to obtain the more stable parameters and the sensitivity of all parameters with temperature. These procedures have already been implemented and have been tested at EADS-CASA in the fully integrated instrument. The results show that the in-orbit internal calibration accurately retrieves the needed parameters and will allow to obtain high quality data.

In this paper, some results of the MIRAS-SMOS demonstrator campaigns are presented. The follow two campaigns were held: an image validation campaign at IRTA side and a flight demonstrator campaign at Finland. The ultimate objective of the airborne demonstrator is to demonstrate its imaging capabilities by applying improved calibration and inversion algorithms to natural low-contrast targets.

SMOS, acronym for Soil Moisture and Ocean Salinity, is the second selected opportunity mission on "The Living Planet Program: Earth Explorer Opportunity Missions" of the European Space Agency (ESA) [1]. The MIRAS (Microwave Imaging Radiometer with Aperture Synthesis) is the single SMOS pay- load, which is compounded of 69 L-band receivers to form a Y shape interferometer with full on-board calibration capability. The Spanish company EADS-CASA is currently leading the development of a Small scale Airborne prototype of the MIRAS (SAM), in the frame of the MDPP-3 project (MIRAS Demonstrator Pilot Project, stage 3) sponsored by the ESA. Several European institutions, including the Polytechnic University of Catalonia, are participating in the development of the MDPP-3 instrument and test campaigns. The demonstrator consists of 12 receivers called LICEF (4 per arm), 1 reference radiometer (NIR) and includes the same internal calibration system scheme foreseen for MIRAS. The instrument has been designed to be flown in the Skyvan of the LST/HUT (Laboratory of Space Technology, Helsinki University of Technology), where several airborne campaigns over land and sea will be undertaken in order to asses the capability of SMOS calibration and retrieval algorithms to provide soil moisture and ocean salinity.