Ali Khazaal

Mitigation of RFIS for SMOS: A Distributed Approach

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.

Impact of Correlator Efficiency Errors on SMOS Land–Sea Contamination

Land-sea contamination observed in Soil Moisture and Ocean Salinity (SMOS) brightness temperature images is found to have two main contributions: the floor error inherent of image reconstruction and a multiplicative error either in the antenna temperature or in the visibility samples measured by the correlator. The origin of this last one is traced down to SMOS calibration parameters to yield a simple correction scheme, which is validated against several geophysical scenarios. Autoconsistency rules in interferometric synthesis together with redundant and complementary calibration procedures provide a robust SMOS calibration scheme.

Localization of RFI Sources for the SMOS Mission: A Means for Assessing SMOS Pointing Performances

Artificial sources emitting in the protected part of the L-band are contaminating the retrievals of the soil moisture and ocean salinity (SMOS) satellite launched by the European Space Agency (ESA) in November 2009. Detecting and pinpointing such sources is crucial for the improvement of SMOS science products as well as for the identification of the emitters. In this contribution, we present a method to obtain snapshot-wise information about sources of radio-frequency interference (RFI). The localization accuracy of this method is also assessed for observed RFI sources. We also show that RFI localizations constitute a useful data set for assessing the pointing performance of the satellite, and present how it is possible, using the results of this method, to identify and estimate two systematic errors in the geo-location of the satellite field of view. The potential causes and the approaches to mitigate both these errors are discussed.

A Kurtosis-Based Approach to Detect RFI in SMOS Image Reconstruction Data Processor

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.

On the reduction of the systematic error in imaging radiometry by aperture synthesis: a new approach for the SMOS space mission

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 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.

An RFI Index to Quantify the Contamination of SMOS Data by Radio-Frequency Interference

The quality of science data provided by ESA soil moisture and ocean salinity (SMOS) satellite is degraded by the presence of artificial sources emitting in the protected part of the L-band, which is preserved for passive measurements by ITU regulation. These sources appear as high temperature points in SMOS brightness temperature products (e.g., Level 1C products), and may affect the retrievals of both SMOS (e.g., Level 2 products). In this contribution, a method is presented to quantify the impact of radio-frequency interference (RFI) on each SMOS snapshots, through the definition of an “RFI index” based on the number, position, and intensity of the RFI sources present in the snapshot. The main purpose of RFI indices is to provide the user of SMOS scientific data with information to ease the RFI filtering, thus achieving more accurate results. The comparison of RFI indices with the outputs of two different methods providing similar snapshot-wise information about RFI shows that the use of RFI indices reduces the probability of missed RFI detections, without increasing the risk for false alarms.

On the Reduction of the Systematic Error in Imaging Radiometry by Aperture Synthesis: A New Approach for the SMOS Space Mission

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.

RFI in SMOS measurements: Update on detection, localization, mitigation techniques and preliminary quantified impacts on soil moisture products

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.

Effect of the Polarization Leakage on the SMOS Image Reconstruction Algorithm: Validation Using Ocean Model and In Situ Soil Moisture Data

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.

SMOS images restoration from L1A data: A sparsity-based variational approach

Data degradation by radio frequency interferences (RFI) is one of the major challenges that SMOS and other interferometers radiometers missions have to face. Although a great number of the illegal emitters were turned off since the mission was launched, not all of the sources were completely removed. Moreover, the data obtained previously is already corrupted by these RFI. Thus, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this work we propose a variational approach to recover a super-resolved, denoised brightness temperature map based on two spatial components: an image u that models the brightness temperature and an image o modeling the RFI. The approach is totally new to our knowledge, in the sense that it is directly and exclusively based on the visibilities (L1a data), and thus can also be considered as an alternative to other brightness temperature recovery methods.