The Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the very first time, systematic passive L-band (1420 - 1427 MHz) measurements from space with a spatial resolution of ~v40 Km. Within the frame of the studies conducted by CESBIO and CNES for the next generations of high-resolution L-band imaging radiometers based on aperture synthesis, the concept of unconnected interferometry plays a key role in the roadmap because it is a solution for improving the spatial resolution. Before deploying such a mission, many issues have to be solved. This is why CNES has initiated the Unconnected L-band Interferometer Demonstrator (ULID) which aims at demonstrating the capability to operate unconnected interferometry in orbit. ULID system relies on a small constellation of three identical nano-satellites flying in close range formation and transmitting to ground the signals acquired by similar detectors onboard all satellites to allow synchronization and correlation computation. This contribution is concerned by the demonstration of our ability to cope with synchronization problems between the detectors.

The Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the very first time, systematic passive L-band (1420 - 1427 MHz) measurements from space with a spatial resolution of ~40 Km. Preliminary results of studies conducted within the frame of a High Resolution (HR) follow-on mission are presented. The SMOS-HR project has undergone a Phase 0 study by the French space agency. The aim of this contribution is to improve the spatial resolution capabilities of SMOS-HR from ~10 Km to ~4 Km with the aid of a swarm of nano-satellites orbiting close to the connected array selected for SMOS-HR. After SMOS and SMOS-HR, this third generation named SMOS-NEXT will be the very first one to perform aperture synthesis from space with both connected and unconnected interferometric measurements.

The SMOS mission, launched in 2009, has been followed by Aquarius and SMAP, but follow-on missions are still in the preliminary phases. One of the main expected improvements is on the spatial resolution, for which a 10-fold increase is needed. Regardless of the choice on acquisition principle (real aperture or interferometry) such a massive improvement cannot be addressed with current technology. But interferometry has an advantage here in the sense that it can be distributed over multiple satellites. The mission described in this paper is the first step towards a complete system to satisfy these challenging requirements. Such a massive improvement cannot be addressed with current technology and requires a major revisit of the acquisition of interferometric measurements. Of course, technological advances targeted by this mission concept is of far wider interest than L-band interferometry. The mission described in this paper is the first step towards a complete system to satisfy these challenging requirements.

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 Soil Moisture and Ocean Salinity (SMOS) satellite is performing systematic L-band observations since 2009, allowing a large number of science and operational applications. Several recent studies have shown the need of the continuity of L-band observations, in particular with an increased angular resolution. In this contribution, two instrumental concepts are presented to reach native resolutions of 5–10 km. In addition, using airborne data, it is also shown that the accuracy of downscaling coarser resolution L-band data to 5–10 km using a high resolution auxiliary data set, is significantly lower than that of native high resolution observations.

After almost 10 years in operation (SMOS- Aquarius - SMAP) the very high potential of L band radiometry is clearly demonstrated. Several applications are already operational (assimilation at ECMWF, for hurricanes, for sea ice etc.) so it is crucial to maintain such measurements. To do so while satisfying the current missions specifications is also of prime importance. Degrading spatial resolution is thus a significant step back which will impact science and applications). These missions are now getting older and the goal of the study presented in this paper is to assess which planned mission could fulfill the requirements to ensure data continuity. For this purpose, an extensive users' requirements study was performed in 2018-2019 assessing what would be required in the near future as well as when L band radiometry was absolutely necessary to satisfy the requirements. From the gathered results a cluster analysis was performed and the only.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) has been providing the longest consistent data record of passive L-band (1.4 GHz) observations for more than ten years. SMOS, as well as the NASA missions SMAP and Aquarius have demonstrated the interest of L-band observations for land, ocean and cryosphere studies. The continuity of L-band observations must be assured taking into account that the spatial resolution (~ 40 km) of SMOS and SMAP is too coarse for some applications. Disaggregation strategies can be implemented but using airborne data, we show that the quality of the downscaled data cannot match that of an instrument with higher native resolution. The goal of the SMOS-HR (High Resolution) mission is to ensure the continuity of L-band observations while increasing the native resolution to 10 km. SMOS-HR will carry an array of ~ 230 antennas to perform aperture synthesis. The antenna distribution has been optimized to reduce the aliasing in the reconstructed images and SMOS-HR will incorporate advanced on-board Radio Frequency Interferences (RFI) mitigation techniques.

The Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the very first time, systematic passive L-band (1420−1427 MHz) measurements from space with a spatial resolution of ~50 Km. This contribution presents preliminary results of studies conducted for a High Resolution (HR) follow-on mission. The SMOS-HR project is currently undergoing a Phase 0 study by the French space agency. The goal is to ensure continuity of L-band measurements while increasing the spatial resolution to ~10 Km without degrading the radiometric sensitivity and keeping the revisit time of 3 days unchanged.

Continuation of brightness temperature measurements at L- band is a major asset for soil moisture and ocean salinity studies in the long term. The SMOS mission, launched in 2009, has been followed by Aquarius and SMAP, but follow-on missions are still in the preliminary phases. One of the main expected improvements is on the spatial resolution, for which a 10-fold increase is needed.Such a massive improvement cannot be addressed with current technology and requires a major revisit of the acquisition of interferometric measurements.The mission described in this paper is the first step towards a complete system to satisfy these challenging requirements.

Since 2009, the SMOS mission has been acquiring brightness temperature measurements to derive soil moisture and ocean salinity. With a revisit time of three days everywhere on the globe, it has become one of the first mission to provide global assessment of these two parameters in near real time. The only instrument carried by SMOS satellite is a two dimensional radiometric interferometer, operating between 1400 and 1420 MHz. Despite this being a protected band, it has been obvious since day one that man-made emissions, either close and powerful or directly in-band, were contaminating the measurements. This was foreseen to some extent and some filtering algorithms had been designed prior to the launch. Although quite efficient, the very high diversity of RFI source characteristics made it very difficult to identify reliably all contaminations. Thus, since then, it has been a constant effort to try to identify better all these sources and assess their impact on SMOS measurements.

The Soil Moisture and Ocean Salinity mission has been collecting data for over 9 years. The whole data currently being reprocessed (Version 721 for levels 1 and 2 and version 4 for level 3 CATDS) an used to see trends and finalise potential applications. This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 9 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events. Also we now have access the Soil Moisture Active and Passive (SMAP) mission and there are obvious synergisms to infer.

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.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the first time, systematic passive L-band (1.4 GHz) measurements from space. This new data set, with a spatial resolution of ~40 km, has allowed a number of outstanding results over land (soil moisture, vegetation properties, frozen soils, ...), ocean (salinity, meso-scale phenomena, river plumes, high winds, ...) and cryosphere. SMOS, together with the NASA missions SMAP and Aquarius, have demonstrated the interest of the continuity of L-band observations. However, higher spatial resolution (1-10 km) is needed for applications related to water resources management and food security, for instance. Over the ocean as well as in coastal areas, higher resolution will bring the possibility to study in detail meso-scale processes and salinity (and density) variations closer to the coast. Over ice, higher spatial resolution will allow to monitor melting events in the coastal regions of Antarctica, for instance. In order to ensure the continuity of Earth observations in the L-band, while improving the resolution of the current generation of radiometers, new mission concepts are needed. We present the SMOS-HR (High-Resolution) project, which is currently in Phase 0 at CNES (Centre National d'Etudes Spatiales).

SMOS is a space mission led by the European Space Agency and designed to provide global maps of Soil Moisture and Ocean salinity, two important geophysical parameters for understanding the water cycle variations and climate change. The SMOS payload is a 2-D interferometer operating at L-band that consists of 69 elementary antennas located along a Y-shaped structure. Important spatial biases persist in the retrieved brightness temperature (BT) images mainly due to the phenomenon of aliasing inside the field of view of SMOS but also due to the Gibbs oscillations near land/ocean transitions. To minimize these biases, a differential image reconstruction algorithm is used in the operational processor that reduces the contrast of the image to be retrieved. To do that, the contribution of a constant artificial temperature map is removed from the measurements prior to reconstruction and then added back after the reconstruction. In this paper, we show that strong residual biases are still present in the retrieved images. To reduce them, we propose to improve the bias correction algorithm by using a more realistic artificial temperature scene based on separating the land and ocean regions and assigning a constant temperature over land and a Fresnel BT model over the ocean. The artificial scene is also improved by means of representing each pixel by its water fraction percentage to smooth the land/ocean transitions. The improved algorithm is validated over the ocean by comparing the retrieved temperatures to a forward geophysical model but also over land by comparing the retrieved soil moisture toin situmeasurements.

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.

The Soil Moisture and Ocean Salinity mission has been collecting data for over 7 years. The whole data set has been reprocessed (Version 620 for levels 1 and 2 and version 3 for level 3 CATDS) an used to see trends and finalise potential applications. This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 7 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events. Also we now have access the Soil Moisture Active and Passive (SMAP) mission and there are obvious synergisms to infer.

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.

The NASA Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015 to provide global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days using an L-band (active) radar and an L-band (passive) radiometer. The Level 2 radiometer-only soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36-km Earth-fixed grid using brightness temperature observations from descending passes. This paper provides the first comparison of the validated-release L2_SM_P product with soil moisture products provided by the Soil Moisture and Ocean Salinity (SMOS), Aquarius, Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) missions. This comparison was conducted as part of the SMAP calibration and validation efforts. SMAP and SMOS appear most similar among the five soil moisture products considered in this paper, overall exhibiting the smallest unbiased root-mean-square difference and highest correlation. Overall, SMOS tends to be slightly wetter than SMAP, excluding forests where some differences are observed. SMAP and Aquarius can only be compared for a little more than two months; they compare well, especially over low to moderately vegetated areas. SMAP and ASCAT show similar overall trends and spatial patterns with ASCAT providing wetter soil moistures than SMAP over moderate to dense vegetation. SMAP and AMSR2 largely disagree in their soil moisture trends and spatial patterns; AMSR2 exhibits an overall dry bias, while desert areas are observed to be wetter than SMAP.

The Soil Moisture and Ocean Salinity mission has been collecting data for 6 years. The whole data set has just been reprocessed (Version 620 for levels 1 and 2 and version 3 for level 3 CATDS). This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 6 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events.

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.

The Soil Moisture and Ocean Salinity (SMOS) mission is a European Space Agency (ESA) mission aimed at the global monitoring of surface soil moisture and ocean salinity from radiometric L-band observations. This work described in this letter is devoted to the reduction of the systematic error in the reconstruction of brightness temperature maps from SMOS interferometric measurements. Despite the fact that the image reconstruction method currently used was proposed and implemented in the ESA L1 processor for reducing this error, residual offset and ripples still persist. This is particularly penalizing for oceanographic applications with SMOS data. A new approach for reducing this residual error is presented here and illustrated with brightness temperature maps retrieved over the Pacific ocean.

The Soil Moisture and Ocean Salinity (SMOS) mission launched by the European Space Agency in 2009 is devoted to the monitoring of soil moisture and ocean salinity at global scale from L-band spaceborne radiometric observations obtained with a 2-D interferometer. This paper is concerned with the polarization leakage or coupling between SMOS antennas. More precisely, we analyze the impact of the cross-polar antenna patterns on both the image reconstruction procedure and the scene-dependent bias correction. Depending on the level of this coupling, several solutions will be proposed for the retrieval of brightness temperature maps. We will show that the effect of the polarization leakage is relatively small if the interferometric data or correlations are obtained from antennas operating in the same polarization. On the other hand, we will show that the correlations associated to antennas operating in opposite polarizations are highly coupled, and therefore, the polarization leakage should always be considered in the reconstruction. The proposed solutions are compared, over the ocean, to a simulated brightness temperature model and, over the land, to in situ soil moisture data.

A methodology to retrieve soil moisture (SM) from Soil Moisture and Ocean Salinity (SMOS) data is presented. The method uses a neural network (NN) to find the statistical relationship linking the input data to a reference SM data set. The input data are composed of passive microwaves (L-band SMOS brightness temperatures,$T_{b} $'s) complemented with active microwaves (C-band Advanced Scatterometer (ASCAT) backscattering coefficients), and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) . The reference SM data used to train the NN are the European Centre For Medium-Range Weather Forecasts model predictions. The best configuration of SMOS data to retrieve SM using an NN is using$T_{b} $'s measured with both H and V polarizations for incidence angles from 25° to 60°. The inversion of SM can be improved by ∼10% by adding MODIS NDVI and ASCAT backscattering data and by an additional ∼5% by using local information on the maximum and minimum records of SMOS Tb's (or ASCAT backscattering coefficients) and the associated SM values. The NN-inverted SM is able to capture the temporal and spatial variability of the SM reference data set. The temporal variability is better captured when either adding active microwaves or using a local normalization of SMOS Tb's. The NN SM products have been evaluated againstin situmeasurements, giving results of comparable or better (for some NN configurations) quality to other SM products. The NN used in this paper allows to retrieve SM globally on a daily basis. These results open interesting perspectives such as a near-real-time processor and data assimilation in weather prediction models.

In this communication we present an update on the RFI detection used in the SMOS processing chain and some elements on quantified impact of RFIs on level 2 soil moisture products. The level 2 soil moisture algorithms which included since the beginning a screening mechanism to reject contaminated brightness temperatures is now stricter. New approaches at the level 1 processors are also emerging and will be operational at their next release in 2014. Despite these strengthen procedures, RFIs are still impacting strongly SMOS observations and examples of quantified deterioration are given.

A methodology to retrieve soil moisture (SM) from multiinstrument remote sensing data is presented. The method uses a Neural Network (NN) to find the statistical relationship linking the input data to a reference SM dataset. The input data is composed of passive microwaves (L-band SMOS brightness temperatures), active microwaves (C-band ASCAT backscattering coefficients), and visible and infrared observations by MODIS. The reference SM data used to train the NN are ECMWF model predictions or SMOS L3 SM. After determining the best configuration of input data to retrieve SM using a NN, the NN soil moisture product is evaluated with respect to other global SM products and with respect to in situ measurements. The NN is able to capture the spatial and temporal dynamics of SM, and the SM computed with NNs compares well with the other SM datasets.

The Soil Moisture and Ocean Salinity (SMOS) mission is a European Space Agency project aimed to observe two important geophysical variables, i.e., soil moisture over land and ocean salinity by L-band microwave imaging radiometry. This work is concerned with the contamination of the SMOS data by radio-frequency interferences (RFIs), which degrades the performance of the mission. In this paper, we propose an approach that detects if a given snapshot is contaminated, or not, by RFI. This approach is based on evaluating the kurtosis of each snapshot or data set, using all interferometric measurements provided by the instrument. The obtained kurtosis is considered as an indicator on how much the snapshot is polluted by RFI, thus allowing the user to decide on whether to keep or discard it.

The Soil Moisture and Ocean Salinity (SMOS) satellite was launched by the European Space Agency on November 2, 2009. Its payload, i.e., Microwave Imaging Radiometer with Aperture Synthesis, which is a 2-D L-band interferometric radiometer, measures the brightness temperatures (BTs) in the protected 1400-1427-MHz band. Although this band was preserved for passive measurements, numerous radio frequency interferences (RFIs) are clearly visible in SMOS data. One method to get rid of these interferences is to create a synthetic signal as close as possible to the measured interference and subtract it from the instrument visibilities. In this paper, we describe an approach to create such a signal and on how to use it for geolocalization of the emitters. Then, different methods for assessing the quality of the mitigation are introduced. Due to the complexity of estimating the effects of mitigation globally, it is finally proposed to use mitigation results to create flag maps about the estimated RFI impact, to be associated with BT measurements.

Artificial sources emitting in the protected part of the L-band are polluting the retrievals of ESA's Soil Moisture and Ocean Salinity (SMOS) satellite. Detection and localization of such sources are of interest for the exploitation of science products as well as for the identification of the emitters. A simple and fast method that provides snapshot-wise information is presented. From a statistical analysis of the results, some systematic errors are reported along with their potential causes and an approach to mitigate them. In the case of sources at high geomagnetic latitudes a seasonal variation of the localization error is also noticed; the origin of such phenomenon is still under investigation.

This paper summarizes the rationale for the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission routine calibration plan, including the analysis of the calibration parameter annual variability, and the performances and stability of SMOS images after one year of data. SMOS spends 1.68% of the total observation time in calibration. The instrument performs well within expectations with regard to accuracy and radiometric sensitivity, although spatial ripples are present in SMOS images. Several mechanisms are currently used or under investigation to mitigate this problem. Also, a loss antenna model has recently been introduced to correct for physical temperature-induced effects. This antenna model successfully corrects observed orbital variations, but has difficulties in correcting brightness temperature long-term drifting, as assessed using relatively well-known targets other than the external calibration region-cold space.

This paper presents the progress made in the calibration and image reconstruction of the brightness temperature data provided by the SMOS mission of the European Space Agency. This progress has been made thanks to the learning through the accumulation of results since the launch of the satellite. Current performance of the payload on-board SMOS, the MIRAS interferometer, our main findings on its behavior and open lines of investigation are included in this contribution.

Estimates of soil moisture and surface salinity are of significant importance to improve meteorological and climate prediction. The SMOS mission monitor these quantities, by measuring the brightness temperature by means of L-band aperture synthesis interferometry. Despite the L-band being reserved for Earth and space exploration, SMOS images reveal large number of strong outliers, produced by illegal antennas emitting in this band. In this work we propose a variational approach to recover a super-resolved, denoised brightness temperature map. The measurements are modeled as the superposition of three super-resolved components in the spatial domain: the target brightness temperature map u, an image o modeling the outliers, and Gaussian noise n. This decomposition allows to isolate each of its constituent parts, thanks to a sparsity operator that acts on o, and a bounded variation prior on u that extrapolates its spectrum promoting a non-oscillating behavior. The proposed model is interesting in itself, as it is general enough to be applied to other restoration problems. Experiments on real and synthetic data confirm the suitability of the proposed approach.

The Soil Moisture and Ocean Salinity (SMOS) satellite was launched by ESA on November 2nd, 2009. Its payload MIRAS, is a two-dimensional L-band interferometric radiometer, and it measures brightness temperatures (BT) in the protected 1400-1427 MHz band. Although this band was preserved for passive measurements numerous radio frequency interferences (RFIs) are clearly visible in SMOS' data. One method to get rid of these interferences is to create a synthetic signal as close as possible to the measured interference and subtract it from the instrument's visibilities. Here is described how to create such a signal and how to use it for geo-localization of the sources. Then different methods for assessing the quality of the mitigation are introduced. A possible explanation for the dissimilarity of RFI sources as seen by SMOS is also advanced.

The Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a retrieval algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS algorithm theoretical basis documents to be used to produce the operational algorithm. The principle of the SM retrieval algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5°, flags and quality indices, and other parameters of interest. This paper gives an overview of the algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises.

On one hand, the SMOS mission is an ESA project aimed at global monitoring of surface Soil Moisture and Ocean Salinity from radiometric L-band observations. The single payload of the mission is MIRAS, a Microwave Imaging Radiometer with Aperture Synthesis. It has been successfully lofted into orbit on November 2nd, 2009. On the other hand, AQUARIUS/SAC-D mission is a partnership between NASA and CONAE for monitoring sea surface salinity from space. The observatory includes AQUARIUS, an L-band radiometer/radar combination and the mission is scheduled for launch on June 9th, 2011. This work is concerned with the synergy between both instruments. It is shown how the brightness temperature maps retrieved from MIRAS interferometric measurements can be resampled down to the ground resolution achieved by the three beams of AQUARIUS without introducing any artifact.

The European Space Agency has launched, November 2nd, 2009, the SMOS mission devoted to the monitoring of Soil Moisture and Ocean Salinity at global scale from L-band space borne radiometric observations obtained with a two dimensional interferometer. This paper is concerned with the retrieval of radiometric brightness temperature maps from interferometric level 1A data provided by SMOS. The impact of some missing data, due to two kinds of failures correlator/receiver failure on the stability of the retrieval method is studied in depth.

The performance requirements of the SMOS payload are demanding in terms of spatial resolution, accuracy, stability and precision, all critical to fulfill its scientific objectives. For this reason a commissioning plan for MIRAS was carefully devised to verify, calibrate and characterize all instrument parameters which could have an impact on its performance. This presentation describes the most important results from the instrument commissioning phase.

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 Soil Moisture and Ocean Salinity (SMOS) mission aims at observing two variables critical for a large scientific community, from biosphere dynamics to climate monitoring. The mission should also provide information on root zone soil moisture and vegetation and contribute to significant research in the field of the cryosphere. The original design, 2D interferometric radiometer at L-band, and principle of measurement makes SMOS a challenge at various technical levels. Moreover, stringent requirements on the estimated variables make the complete processing of SMOS data even more challenging. One of these requirements is concerned with the ability to accurately localize all the footprints of the instrument on the surface of the earth. Based on simulation and sensitivity studies with respect to the final retrieval of soil moisture, this accuracy requirement has been established so that the localization error on each footprint presents a zero mean and a standard deviation of 400 m. This high accuracy is mainly due to the need for knowledge of open water within a footprint, not to bias soil moisture estimation. This accuracy is highly challenging and unprecedented for sensors of this class and resolution. The on board devices that will help characterize the geolocation of the SMOS products include stellar sensor and gyroscopes, which can achieve an accuracy consistent with the requirements in terms of standard deviation. But the overall localization budget is also contaminated by an important bias, due to the mechanical deployment of the instrument antenna arms after launch, and to the launch shift that impacts all the alignments on the satellite (mechanical shift of stellar sensor due to shocks and vibrations, moisture desorption in mechanical brackets...). The purpose of this study is to characterize these biases, obviously inaccessible to on ground measurement and expected not to evolve once in orbit, so that they can be accounted for in the ground processing prior to initiate the soil moisture retrieval.

This contribution summarizes prominent features of the Level 2 algorithm aimed at processing land surface geophysical quantities from the ESA-led SMOS mission. It emphasizes the soil moisture retrieval and describes the decision tree built in order to select appropriate retrieval configurations. The expected performance is illustrated by preliminary results of the algorithm validation.

Absolute radiometric calibration is one of the main elements that contribute to the quality of measurements obtained with optical remote sensing instruments, but maintaining a good calibration accuracy during the whole life of an instrument is a difficult task. Since the sensitivity of an instrument generally changes after launch and degrades with time, many sensors have been equipped with onboard calibration devices. But these devices being not perfectly reliable, independent calibration methods based on natural targets are necessary to validate the results. The Sun glint calibration method is an interband calibration method that uses the specular reflection of the Sun on the ocean surface to transfer the absolute calibration of one reference spectral band to other spectral bands, from visible to short wave infrared wavelengths. Despite the drawback of relying on the absolute calibration of a reference spectral band, this method is one of the rare methods that can provide accurate calibration results for near-infrared spectral bands up to 1650 nm, without requiring costly in situ measurements simultaneously to the satellite overpass. This paper details the Sun glint calibration method and its error budget, and gives the results obtained with the VEGETATION instrument that was recently launched onboard the Systeme Pour l'Observation de la Terre 5 (SPOT-5) satellite. These results compare very well with the results of other calibration methods.