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The Faraday rotation (FR) is a nonnegligible 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 FR effect [1] . In that work, systematic patterns in the retrieved FR angle (FRA) were detected. In this article, these systematic patterns are characterized and corrected to improve the quality of the retrieved VTEC maps. These maps can be then reused 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 global positioning system (GPS) measurements on the ocean brightness temperatures (TB) 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 TB, 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

The quality of the Soil Moisture and Ocean Salinity (SMOS) sea surface salinity (SSS) measurements has been noticeably improved in the past years. However, for some applications, there are still some limitations in the use of the Level-2 ocean salinity product. First, the SSS measurements are still affected by a latitudinal and seasonal bias. Second, the high standard deviation of the SSS error could significantly degrade part of the SSS signal. Finally, the coverage of the Level-2 salinity measurements is significantly reduced after applying filtering criteria to discard the poor-quality retrievals. In this work, we apply nodal sampling to the SMOS brightness temperatures (TBs), which effectively reduces the standard deviation of the TB error; then, we use debiased non-Bayesian retrieval for the mitigation of systematic biases on SSS and the statistical filtering criteria of the degraded salinity retrievals; and finally, we comprehensively characterize the residual latitudinal and seasonal biases and derive a correction for the retrieved SSS. We generate three years of an enhanced SMOS Level-2 Ocean Salinity product and we compare its performances with the ones corresponding to the European Space Agency SMOS Level-2 Ocean Salinity product (v662)

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 her

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 databas

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

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

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

The microwave imaging radiometer with aperture synthesis (MIRAS) is formed by 69 total power radiometers, of which three are the noise-injection type. Their calibration is reviewed on the basis of the data gathered during more than eight years of operation. Internally calibrated gain and offset corrections with improved temporal stability are presented. New front-end loss characterization with lower seasonal dependence originated from external temperature swings is also proposed. Finally, a methodology to validate the external calibrations, with the instrument pointing to the cold sky, is developed. It seems to indicate that the change of orientation of the instrument, with associated thermal variations, may induce small changes in the radiometer front-end losses, thus introducing calibration errors

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

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

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

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

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

RFI (Radio Frequency Interference) sources, Sun signal and even land-sea transitions may generate large sidelobes that corrupt SMOS brightness temperature products and therefore, the quality of the soil moisture and sea surface salinity retrievals. This work focuses on the reduction of this Gibbs-like contamination in brightness temperature scenes using an alternative image reconstruction approach. This technique is based on sampling the signal at the nodal points, that is, at those points at which the oscillating interference causes the minimum distortion to the geophysical signal. The method has been successfully tested using ocean views significantly reducing general ripples and sidelobes in the contaminated brightness temperature images. In addition, it reduces the standard deviation of the difference between the SMOS measurements and the theoretical model by 1K, which is crucial to improve the quality of the sea surface salinity retrievals

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 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 $pm$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

Soil Moisture and Ocean Salinity (SMOS) brightness temperature synthesized images are obtained after a comprehensive error correction procedure that takes into account both on-ground and in-flight calibration measurements. However, the final images are still contaminated by small, although nonnegligible, spatial errors: the so-called pixel bias. Since spatial errors in the 2-D SMOS images are not zero mean along track, these errors produce clearly visible artifacts aligned to this direction. Fortunately, spatial errors have been found to be very stable and can be minimized once the image distortion pattern is properly measured by observing a target at a uniform brightness temperature distribution. This letter describes the procedure to compute a multiplicative mask that largely reduces spatial errors over the ocean. Preliminary results to assess the mask performance are also presented by computing the reduction of the rms spatial error for a number of targets selected to have significant temporal and geographical diversity. © 2011 IEEE

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

This work summarizes the main findings of the activities carried out in the past years by the Microwave Radiometry Team at the Technical University of Catalonia (UPC) in the field of sea surface salinity retrieval within the frame of the SMOS mission in collaboration with the Institute of Marine Sciences (ICM/CMIMA-CSIC). They cover the measurement of the dielectric permittivity of the sea water at L-band, the impact of the sea emissivity of the surface roughness (waves, swell, currents, rain...) and oil spills, and its comparison with numerical models, as well as the developemnt of sea surface salinity retrieval algorithms fos SMOS