Digital beam forming is a signal processing technique that mixes the signals collected by an antenna array to produce high resolution images. When this paradigm takes place in an imaging radiometer by aperture synthesis onboard a satellite observing the Earth from a low-elevation orbit and when the beams forming is performed numerically with Field-Programmable Gate Arrays (FPGA), the question of the computational cost is a central one. This is the case of the Fine Resolution Explorer for Salinity, Carbon and Hydrology (FRESCH) project which has been recently proposed to the European Space Agency (ESA). This contribution aims at evidencing and solving this computational issue.

The Fine Resolution Explorer for Salinity, Carbon and Hydrology (FRESCH) is presented. The science case and the mission objectives are discussed before presenting the mission concept. FRESCH is an L-band antenna array operated in beamforming mode providing data at a spatial resolution of 10-15 km to study the biogeochemical and physical phenomena taking place at the interfaces of ocean, land and ice. FRESCH has been submitted to the European Space Agency Earth Explorer 12 program.

This paper reports on the main results of SMOS-HR instrument technical study, a new interferometric radiometer dedicated to Soil Moisture and Ocean Salinity measurement from space. The instrument aims at providing enhanced spatial resolution with respect to SMOS and integrating robust RFI (RF Interference) mitigation technique. The instrument key performance requirements and the architecture features and trade-offs are exposed in this paper.

Recent applications of passive L-band observations from space are summarized for ocean, land surface and cryosphere applications. The main limitation of the measurements performed by the current generation of sensors is the spatial resolution. The need of a mission ensuring the continuation of L-band measurements from space with high spatial resolution (10-15 km) is discussed.

Digital Beam Forming (DBF) and Synthetic Aperture Inter-ferometry (SAI) are signal processing techniques that mixes the signals collected by an antenna array to produce high resolution images. This study aims at comparing these two approaches with the aid of simulations conducted at microwaves frequencies within the frame of the Soil Moisture and Ocean Salinity (SMOS) mission which is providing for more than a decade systematic passive L-band measurements from space. Although the two techniques are using the same signals and sharing the same goal, there are few differences that deserve attention. This is the case of the reconstruction floor error whose level and angular signature are significantly lower with the DBF paradigm than with the SAI one.

International audience

International audience

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.

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.

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.

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.

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.

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.

The Soil Moisture and Ocean Salinity (SMOS) mission by ESA has been delivering Level 1 data to the Level 2 users since 2010. The Level 1 processing is divided in three levels: Level 1a, comprising the conversion of the raw data from MIRAS into calibrated visibilities; Level 1b, where the calibrated visibilities are corrected from the effects of unwanted sources (for example, the Sun) and are transformed into Brightness Temperatures; finally, in the Level 1c the Brightness Temperatures are geolocated over the Earth in a discrete grid and several auxiliary parameters are computed and annotated in the L1c final product. The algorithms responsible for the Calibration (L1a), Image Reconstruction (L1b) and Geolocation (L1c) have been validated during the In-Orbit Commissioning Phase that took place in the first half of 2010. However, as the knowledge on the performance of MIRAS increases, it has become necessary to set in motion a review on the current algorithms. This paper focuses on the review of the Image Reconstruction algorithms being conducted by DEIMOS Engenharia and IRAP and it is divided in the following sections: in Section 1 the theory for the proposed alternatives with respect to the baseline algorithm is demonstrated, along with several examples to ilustrate them. All the results have been obtained using the baseline algorithm or the alternatives that have been implemented in the Level 1 Prototype Processor (L1PP); in Section 2 the impact on the Level 1 Operational Processor (L1OP) is assessed, using as reference the L1PP results; and finally, in Section 3 the main conclusions found throughout this paper are summarized.

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.

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 SMOS mission is a European Space Agency project aimed at global monitoring of surface Soil Moisture and Ocean Salinity from radiometric L-band observations. This work is concerned with the contamination of the data collected by SMOS by radio frequency interferences (RFI) which degrade the performance of the mission. RFI events are evidenced on both reference radiometers measurements and interferometric ones. It is explained why well-known standard RFI detection methods cannot be used. A specific method for the SMOS mission is presented and illustrated with data acquired with the reference radiometers during the first year of the mission. The aim of this method is not to localize nor to quantify the RFI sources but only to detect, to quantify and possibly to mitigate the corresponding RFI effects in the signals measured by the three reference radiometers.

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 SMOS mission is a European Space Agency project aimed at global monitoring of surface Soil Moisture and Ocean Salinity from radiometric L-band observations. This paper is concerned with the contamination of the data collected by SMOS by radio-frequency interferences (RFIs) which degrade the performance of the mission. RFI events are evidenced on both reference radiometer measurements and interferometric ones. It is explained why well-known standard RFI detection methods cannot be used. A specific method for the SMOS mission is presented and illustrated with data acquired during the commissioning phase.

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.

The Soil Moisture and Ocean Salinity (SMOS) mission is a European Space Agency project aimed at global monitoring of surface SMOS from radiometric L-band observations. This letter is concerned with the reduction of the systematic error (or bias) in the reconstruction of radiometric brightness temperature maps from SMOS interferometric measurements. A recent and efficient method has been proposed for reducing this error. However, a residual bias still persists. A new approach for reducing this bias down to residual values less than 0.1 K is presented here and illustrated with numerical simulations.

Synthetic aperture imaging radiometers (SAIR) are powerful instruments for high-resolution observation of the Earth surface at low microwave frequencies. This article deals with the impact of correlators and receivers failures on the reconstruction process which aims at inverting the interferometric data for retrieving the radiometric brightness temperature distribution of the scene under observation. Numerical simulations are carried out for the SMOS space mission, a project led by the European Space Agency and devoted to the remote sensing of soil moisture and ocean salinity from a low orbit platform.

Synthetic aperture imaging radiometers are powerful sensors for high-resolution observations of the Earth at low microwave frequencies. Within this context, the European Space Agency is currently developing the soil moisture and ocean salinity (SMOS) mission devoted to the monitoring of SMOS at global scale from L-band spaceborne radiometric observations obtained with a 2-D interferometer. This paper is concerned with the reconstruction of radiometric brightness temperature maps from interferometric measurements. More exactly, it extends the concept of ldquoband-limited resolving matrixrdquo to the case of the processing of dual-polarimetric data.

Synthetic aperture imaging radiometers (SAIRs) are powerful instruments for high-resolution observation of planetary surfaces at low microwave frequencies. This paper is concerned with the reconstruction of radiometric brightness temperature maps from SAIR interferometric measurements. Even in the absence of modeling errors and radiometric noise, a systematic error, or bias, has been observed in the reconstructed maps. The origin of this bias is analyzed and an efficient solution is proposed for reducing it. The core reconstruction procedure is not changed, and no additional measurements are needed. Throughout the scientific rationale, particular emphasis is laid on numerical simulations carried out for the Soil Moisture and Ocean Salinity space mission, a project led by the European Space Agency and devoted to the remote sensing of soil moisture and ocean salinity from a low-orbit platform

Synthetic aperture imaging radiometers (SAIRs) are powerful instruments for high-resolution observation of planetary surfaces at low microwave frequencies. This paper is concerned with the reconstruction of radiometric brightness temperature maps from SAIR interferometric measurements. Even in the absence of modeling errors and radiometric noise, a systematic error, or bias, has been observed in the reconstructed maps. The origin of this bias is analyzed and an efficient solution is proposed for reducing it. The core reconstruction procedure is not changed, and no additional measurements are needed. Throughout the scientific rationale, particular emphasis is laid on numerical simulations carried out for the Soil Moisture and Ocean Salinity space mission, a project led by the European Space Agency and devoted to the remote sensing of soil moisture and ocean salinity from a low-orbit platform

It is now well established that synthetic aperture imaging radiometers are powerful sensors for high-resolution observations of the Earth at low microwave frequencies. This article deals with the reconstruction of radiometric brightness temperature maps from interferometric measurements. The corresponding inverse problem is often ill-posed unless a regularizing constraint is introduced in order to provide a unique and stable solution. Standard regularizing approaches are presented, the corresponding solutions are analyzed and the links between their physical and mathematical meanings are established. To support the theory, numerical simulations are presented and analyzed with emphasis on stability and error analysis

Synthetic Aperture Imaging Radiometers (SAIR) are powerful instruments for high-resolution observation of planetary surfaces at low microwave frequencies. This article is concerned with the reconstruction of radiometric brightness temperature maps from SAIR interferometric measurements. Even in the absence of modeling errors and radiometric noise, a systematic error, or bias, has been observed in the reconstructed maps. The origin of this bias is analyzed and an efficient solution is proposed for reducing it. Particular emphasis is laid on numerical simulations carried out for the SMOS space mission, a project led by the European Space Agency and devoted to the remote sensing of Soil Moisture and Ocean Salinity from a low orbit platform.

This paper is concerned with the reconstruction of radiometric brightness temperature maps from interferometric measurements. The corresponding inverse problem is often ill-posed unless a regularizing constraint is introduced in order to provide a unique and stable solution. Standard regularizing approaches are compared and illustrated with numerical simulations carried out in reference to the Soil Moisture and Ocean Salinity space mission led by the European Space Agency.

This article is concerned with the apodization windows to be applied to brightness temperature maps reconstructed from complex visibilities provided by the MIRAS (Microwave Imaging Radiometer with Aperture Synthesis) instrument on board the SMOS (Soil Moisture and Ocean Salinity space mission) spacecraft in order to achieve a close to uniform pixel at the Earth's surface level

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

Synthetic aperture imaging radiometers (SAIRs) are potential powerful instruments for high-resolution observation of planetary surfaces at low microwave frequencies. This paper deals with the reconstruction of radiometric brightness temperature maps from SAIR interferometric measurements. It is demonstrated that the corresponding inverse problem is not well-posed and must, therefore, be regularized in order to provide a unique and stable solution. A new approach is presented by referring to the notion of modeling operator and to the concept of a resolving matrix of the instrument. To illustrate the theory, numerical simulations are carried out in reference to the Soil Moisture and Ocean Salinity space mission led by the European Space Agency. The results are discussed with emphasis on stability and error analysis.

It is now well established that Synthetic Aperture Imaging Radiometers (SAIR) promise to be powerful sensors for high-resolution observations of the Earth at low microwave frequencies. Within this context, the European Space Agency (ESA) is currently developing the SMOS mission. This propagation of modelling errors within a reconstruction process that attempts to retrieve the brightness temperature of a scene under observation from interferometric measurements depends on the knowledge of the values of the parameters involved in the modelling of the instrument. This contribution describes an approach to characterize these modelling parameters, once the instrument has been launched into space, with an accuracy such that the propagation of errors through the reconstruction process is still under control.

It is now well established that Synthetic Aperture Imaging Radiometers (SAIR) promise to be powerful sensors for high-resolution observations of the Earth at low microwave frequencies. Within this context, the European Space Agency is currently developing the Soil Moisture and Ocean Salinity mission. The present work aims to study the errors attributable to the fringe washing function onto the reconstructed brightness temperature map. With the aid of regularized reconstruction methods, it is shown that the spatial resolution and the radiometric sensitivity of the SMOS instrument are little influenced by the fringe wash phenomenon even when the received signal is blurred.

It is now well established that synthetic aperture imaging radiometers promise to be powerful sensors for high-resolution observations of the Earth at low microwave frequencies. Within this context, the European Space Agency (ESA) is currently developing the SMOS (Soil Moisture and Ocean Salinity) mission. The Y-shaped array selected for SMOS is fitted with equally spaced antennae and leads to natural hexagonal sampling grids. This article deals with the reconstruction of radiometric brightness temperature maps over such grids from interferometer measurements. The corresponding inverse problem is stated without any reference to the numerical method that could be used to solve it. To support the theory, numerical simulations are presented and analyzed with emphasis on stability and error analysis.

It is now well established that synthetic aperture imaging radiometers promise to be powerful sensors for high-resolution observations of the Earth at low microwave frequencies. Within this context, the European Space Agency (ESA) is currently developing the SMOS (Soil Moisture and Ocean Salinity) mission. The Y-shaped array selected for SMOS is fitted with equally spaced antennae and leads to a natural hexagonal sampling of the Fourier plane. This article deals with the improvement of the apodization function to be applied to the complex visibilities. The aim of this function is to reduce the Gibbs phenomenon produced by the finite extent of the star-shaped frequency coverage and the resulting sharp frequency cut-off.

It is now well established that synthetic aperture imaging radiometers promise to be powerful sensors for high-resolution observations of the Earth at low microwave frequencies. Within this context, the European Space Agency is currently developing the Soil Moisture and Ocean Salinity (SMOS) mission. The Y-shaped array selected for SMOS is fitted with equally spaced antennae and leads to a natural hexagonal sampling of the Fourier plane. This paper deals with the choice of the apodization function to be applied to the complex visibilities. The aim of this function is to reduce the Gibbs phenomenon produced by the finite extent of the star-shaped frequency coverage and the resulting sharp frequency cut-off. A large number of windows are introduced. A comparison of these in terms of their spatial domain properties is given, according to criteria relevant for remote sensing of the Earth's surface. This paper also describes how discrete Fourier transform calculations over hexagonal grids can be performed using a simple algorithm. Actually, standard fast Fourier transform algorithms designed for Cartesian grids and which have a long track record of optimization can be reused. Finally, an interpolation formula is given for resampling data from hexagonal grids without introducing any aliasing artifacts in the resampled data.

The Soil Moisture and Ocean Salinity (SMOS) space mission is currently undergoing phase-B studies at the European Space Agency. The SMOS payload is an L-band interferometric radiometer based on a two-dimensional aperture synthesis concept. This paper presents the first images obtained by a demonstrator of the SMOS instrument over land surfaces at the Avignon test site in 1999.

Presents a new approach to the problems of Fourier synthesis in the experimental context of aperture synthesis. Depending on what is emphasized, this method is called FIRST or WIPE:FIRST for the principles (Fourier Interpolation and Reconstruction via Shannon-type Techniques), and WIPE for the corresponding deconvolution method (WIPE reminds of CLEAN, a well-known deconvolution technique in astronomy). The regularization principle of FIRST refers to the Shannon sampling formula and to theoretical considerations related to multiresolution analysis. To describe the imaging kernel of FIRST (WIPE), the authors adopt a terminology derived from that of CLEAN. At each iteration of the selected constructive process (conjugate gradients for example), WIPE compares the dusty map with the dusty map of the model. In the corresponding truncated discrete convolution, the discrete point-spread function, the dusty beam, has two components: the traditional dirty beam and the regularization beam. Besides the clarity of the principles, the authors show, in a concrete manner, the advantages of WIPE over CLEAN. In a more general way, they also indicate how FIRST can be used for analysing the results provided by other methods.>