Jérôme Gourrion

SMOS first data analysis for sea surface salinity determination

Soil Moisture and Ocean Salinity (SMOS), launched on 2 November 2009, is the first satellite mission addressing sea surface salinity (SSS) measurement from space. Its unique payload is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at the L-band frequency. This article presents a summary of SSS retrieval from SMOS observations and shows initial results obtained one year after launch. These results are encouraging, but also indicate that further improvements at various data processing levels are needed and hence are currently under investigation.

SMOS Semi-Empirical Ocean Forward Model Adjustment

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

Rfianalysis in smos imagery

SMOS im agery has been an alyzed to stud y: 1) radio- frequency in terference ( RFI) d etection an dm itigation algorithms, and 2) the statistical properties of RFI. Results show that with a high probability og detection (∼0.75), the probability of false alarm is very high as well (∼0.68), and most snap-shots seem to be contaminated, even though the estimated RFI value is so weak, and the im pact in the SMOS im agery is not even nooticeable. Results of the detection and mitigation algorithm are presented, with the statistical analysis of more than 13000 L1b snap-shots.

Radio-Frequency Interference Detection and Mitigation Algorithms for Synthetic Aperture Radiometers

The European Space Agency (ESA) successfully launched the Soil Moisture and Ocean Salinity (SMOS) mission in November 2, 2009. SMOS uses a new type of instrument, a synthetic aperture radiometer named MIRAS that provides full-polarimetric multi-angular L-band brightness temperatures, from which regular and global maps of Sea Surface Salinity (SSS) and Soil Moisture (SM) are generated. Although SMOS operates in a restricted band (1400-1427 MHz), radio-frequency interference (RFI) appears in SMOS imagery in many areas of the world, and it is an important issue to be addressed for quality SSS and SM retrievals. The impact on SMOS imagery of a sinusoidal RFI source is reviewed, and the problem is illustrated with actual RFI encountered by SMOS. Two RFI detection and mitigation algorithms are developed (dual-polarization and full-polarimetric modes), the performance of the second one has been quantitatively evaluated in terms of probability of detection and false alarm (using a synthetic test scene), and results presented

Characterization of the SMOS Instrumental Error Pattern Correction Over the Ocean

The Soil Moisture and Ocean Salinity (SMOS) mission was launched on November 2nd, 2009 aiming at providing sea surface salinity (SSS) estimates over the oceans with frequent temporal coverage. The detection and mitigation of residual instrumental systematic errors in the measured brightness temperatures are key steps prior to the SSS retrieval. For such purpose, the so-called ocean target transformation (OTT) technique is currently used in the SMOS operational SSS processor. In this paper, an assessment of the OTT is performed. It is found that, to compute a consistent and robust OTT, a large ensemble of measurements is required. Moreover, several effects are reported to significantly impact the OTT computation, namely, the apparent instrument (temporal) drift, forward model imperfections, auxiliary data (used by forward model) uncertainty and external error sources, such as galactic noise and Sun effects (among others). These effects have to be properly mitigated or filtered during the OTT computation, so as to successfully retrieve SSS from SMOS measurements.

Minimization of Image Distortion in SMOS Brightness Temperature Maps Over the Ocean

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.

Review of the CALIMAS Team Contributions to European Space Agency’s Soil Moisture and Ocean Salinity Mission Calibration and Validation

This work summarizes the activities carried out by the SMOS (Soil Moisture and Ocean Salinity) Barcelona Expert Center (SMOS-BEC) team in conjunction with the CIALE/Universidad de Salamanca team, within the framework of the European Space Agency (ESA) CALIMAS project in preparation for the SMOS mission and during its first year of operation. Under these activities several studies were performed, ranging from Level 1 (calibration and image reconstruction) to Level 4 (land pixel disaggregation techniques, by means of data fusion with higher resolution data from optical/infrared sensors). Validation of SMOS salinity products by means of surface drifters developed ad-hoc, and soil moisture products over the REMEDHUS site (Zamora, Spain) are also presented. Results of other preparatory activities carried out to improve the performance of eventual SMOS follow-on missions are presented, including GNSS-R to infer the sea state correction needed for improved ocean salinity retrievals and land surface parameters. Results from CALIMAS show a satisfactory performance of the MIRAS instrument, the accuracy and efficiency of the algorithms implemented in the ground data processors, and explore the limits of spatial resolution of soil moisture products using data fusion, as well as the feasibility of GNSS-R techniques for sea state determination and soil moisture monitoring.

Preliminary validation of SMOS products (levels 3 and 4)

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

Overview of SMOS Level 2 Ocean Salinity processing and first results

SMOS (Soil Moisture and Ocean Salinity), launched in November 2, 2009 is the first satellite mission addressing the salinity measurement from space through the use of MIRAS (Microwave Imaging Radiometer with Aperture Synthesis), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at L-band. This paper presents a summary of the sea surface salinity retrieval approach implemented in SMOS, as well as first results obtained after completing the mission commissioning phase in May 2010. A large number of papers have been published about salinity remote sensing and its implementation in the SMOS mission. An extensive list of references is provided here, many authored by the SMOS ocean salinity team, with emphasis on the different physical processes that have been considered in the SMOS salinity retrieval algorithm.

Toward an Optimal Estimation of the SMOS Antenna-Frame Systematic Errors

After 2.5 years of the Soil Moisture and Ocean Salinity (SMOS) mission, the characterization of residual instrumental systematic errors in the measured brightness temperatures (TB) is still rather poor. This, in turn, negatively impacts the sea surface salinity retrievals and, as such, notably limits the missions success. The error mitigation methodology currently used operationally, the so-called Ocean Target Transformation (OTT), mixes both instrumental and model-induced errors. In this paper, it is proposed to distinguish errors by their type of impact on the TB images: mean brightness level, incidence angle dependence, and azimuth angle dependence. A new approach to characterize the azimuth-dependent errors is proposed. First, a careful data selection strategy is applied. Then, an empirically fitted model, which only accounts for the TB incidence angle dependence, is subtracted from the mean TB images of the selected data sets to estimate the systematic antenna-frame errors. The robustness of this methodology is assessed through the estimated anomaly pattern stability when computed for different geophysical conditions, periods of time, and latitudinal bands. The residual variability ranges from 0.03 K to 0.14 K, whereas the OTT variability is about 0.5 K. The new method is forward model independent and generic. It can therefore be applied to estimate the antenna-frame systematic errors over land and ice. Moreover, it proves to be very effective in separating different sources of error and can therefore be used to further characterize other error components and improve the various SMOS forward model terms.