This work introduces the ongoing initiatives by the "RFI in Remote Sensing Working Group" within the IEEE Standards Association. The Working Group is taking the lead in standardizing the evaluation of radio frequency interference (RFI) impact on spaceborne microwave remote sensing. This standardization effort aims to enhance the monitoring of RFI and improve the effectiveness of sharing information related to it. It is developing a standard titled "P4006 Standard for Remote Sensing Frequency Band Radio Frequency Interference (RFI) Impact Assessment". This paper presents these efforts and highlights the recent activities undertaken by this working group.

In this paper, we describe the attempt of the Frequency Allocation in Remote Sensing Technical Committee (FARS-TC) from the IEEE Geoscience and Remote Sensing Society (GRSS) to standardize the impact assessment of radio frequency interference (RFI) especially in the field of spaceborne remote sensing. Multiple radio services have been sharing a common radio band with remote sensing satellites, resulting in inevitable interferences between each other. In other cases, some services accidentally emit their signal to nearby frequency band. Detecting interference and locating its source is a necessary task to guarantee data integrity. Given that remote sensing satellites carry unique payloads, the effect that RFI have depends on each sensor. For this reason, the RFI impact assessments have been usually developed with one mission or application in mind.Standardizing the impact assessment of RFI will benefit to make a public identification system so that we can monitor RFI effectively. In this paper, we introduce the working group in the IEEE Standards Association named "P4006 Standard for Remote Sensing Frequency Band Radio Frequency Interference (RFI) Impact Assessment" which is handled by the IEEE GRSS FARS-TC and its recent activities.

This study presents a new signal processing method to mitigate RFI, based on the Karhunen-Loève Transform. The main property of this method is that it is adaptive and RFI-agnostic – i.e., it is able to potentially mitigate any type of RFI waveform. It is based on the estimation of the covariance matrix of the process, and the derivation of a basis that spans the RFI subspace. The methodology to find this basis is explained in detail. The theoretical performance obtained using KLT is explored by simulations, along with the evaluation of the influence of the system parameters. In addition, a couple of examples with real data are provided.

The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), together with NASA's Soil Moisture Active Passive (SMAP) mission, is providing a wealth of information to the user community for a wide range of applications. Although both missions are still operational, they have significantly exceeded their design life time. For this reason, ESA is looking at future mission concepts, which would adequately address the requirements of the passive L-band community beyond SMOS and SMAP. This article proposes one mission concept, TriHex, which has been found capable of achieving high spatial resolution, radiometric resolution, and accuracy, approaching the user needs. This is possible by the combination of aperture synthesis, formation flying, the use of general circular orbits, and alias-free imaging.

Radio frequency interference (RFI) is an increasing problem in microwave radiometry, particularly for Earth observation, because the antennas are pointing toward the Earth. RFI has been observed at the L-band in European Space Agency’s (ESA’s) Soil Moisture and Ocean Salinity (SMOS) Earth Explorer mission, National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active and Passive (SMAP) and Aquarius missions, and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and WindSat missions at 10.7 and 18.7 GHz (Misra and de Matthaeis, 2014). Therefore, dedicated onboard systems are, nowadays, a must to detect and remove contaminated measurements, improving radiometric accuracy and increasing the spatial coverage. In this work, a novel detection technique, specially tailored for synthetic aperture interferometric radiometers (SAIRs), is proposed and its performance analyzed. It is based on the change of the shape of the cross correlation function at lags different from zero under the presence of RFI. The performance of the proposed technique is compared to other common RFI detection algorithms, and its limitations and advantages are discussed. Postcorrelation detection performance is found to outperform other commonly used algorithms, such as kurtosis. In addition, it presents some convenient properties for its practical application in correlation and real aperture radiometers.

Radio Frequency Interference (RFI) is an increasing problem particularly for Earth Observation using Microwave Radiometry. RFI has been observed, for example, at L-band by the ESA’s SMOS (Soil Moisture and Ocean Salinity) Earth Explorer and by NASA’s SMAP (Soil Moisture Active Passive) and Aquarius missions, as well as at C-band by AMSR-E and AMRS-2; and at 10.7 GHz and 18.7 GHz by AMSR-E, AMRS-2, WindSat and GMI [1], [2]. Therefore, systems dedicated to interference detection and removal of contaminated measurements are nowadays a must in order to improve the radiometric accuracy and reduce the loss of spatial coverage caused by interference. In this work, the feasibility of using the Empirical Mode Decomposition (EMD) technique for RFI mitigation is explored. The EMD, also known as Hilbert-Huang Transform (HHT), is an algorithm that decomposes the signal into Intrinsic Mode Functions (IMF). The achieved performance is analyzed, and the opportunities and caveats that this type of methods present are described. EMD is found to be a practical RFI mitigation method, albeit presenting some limitations, and a considerable complexity. Nevertheless, in some conditions, EMD exhibits a better performance than other commonly used methods (such as frequency binning). In particular, it has been found that EMD performs well for RFI affecting the < 25% lower part of the Intermediate Frequency (IF) bandwidth.

The European Space Agency (ESA) has recently published the Soil Moisture and Ocean Salinity Mission (SMOS) third mission reprocessing data, which include the changes in calibration and image reconstruction that have been made to the Level 1 Operational Processor (L1OP) version v724 during the past few years. The new L1 processor incorporates several improvements to the calibration and image reconstruction algorithms. The present article proposes a new methodology used by the SMOS team to evaluate the quality and stability of the L1 data. This is done by comparison with an ocean forward model over a well-known, sea-surface salinity stable, region. The L1 reprocessed radiometry accuracy and stability are studied in detail. The results presented here confirm the improvement on data quality expected for the new baseline algorithm. Furthermore, the remaining biases and open points are described and analyzed, establishing the basis for their improvement in following reprocessing campaigns.

Microwave radiometers can be considerably affected by Radio Frequency Interference (RFI). These man-made interference conceal the underlying natural signal, preventing the retrieval of geophysical variables. Adopting on-board detection and mitigation techniques is a requirement to reduce the impact of RFI. Several families of RFI detection algorithms have been developed over the last years (e.g. [1]–[4]). In this work, a new detection technique is proposed and its performance analyzed. It is based in the distortion of the shape of the cross-correlation function at lags different from zero under the presence of RFI. Its performance is compared to other common RFI mitigation algorithms. Proposed methods' performance is found to surpass other common algorithms such as signal Kurtosis, while presenting some convenient properties for its practical application in correlation and synthetic aperture radiometers.

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.

Radio Frequency Interference (RFI) is an increasing problem in Microwave Radiometry, particularly for Earth Observation. It has been observed at L-band in ESA's SMOS (Soil Moisture and Ocean Salinity) Earth Explorer mission, as well as in NASA's SMAP (Soil Moisture Active and Passive) and Aquarius missions, as well as in AMSR-E and WindSat missions at 10.7 GHz and 18.7 GHz [1]. Therefore, dedicated on-board systems are a must to detect and remove contaminated measurements, improve the radiometric accuracy, and guarantee a larger spatial coverage. In this work, the influence of quantization and sampling in the performance of several RFI detection and mitigation techniques is studied for different types of microwave radiometers. It will be shown that coarse quantization schemes strongly impairs RFI mitigation performance, a fundamental aspect to be taken into account when defining the architecture of future instruments.

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.

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.