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

Camps, A.; Gourrion, Jérôme; Tarongi, J.M.; Vall, Mercedes; Gutierrez, Antonio P.; Barbosa, José; Castro, Rita

doi:10.3390/a4030155

Cited by 56 publications

(21 citation statements)

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“…To identify contaminated pixels, we first apply the pixel-based SMOS RFI flags and subsequently identify remaining snapshots containing values above 300 K, which is outside of the geophysical range expected over polar oceans. Since RFI can either partly or completely destroy a snapshot [19], we remove the entire snapshot for simplicity, resulting in an increased data loss for these cases (see also Tian-Kunze et al [3]). …”

Section: Smosmentioning

confidence: 99%

A Consistent Combination of Brightness Temperatures from SMOS and SMAP over Polar Oceans for Sea Ice Applications

Tetzlaff

Kaleschke

2018

Remote Sensing

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Abstract:Passive microwave measurements at L-band from ESA's Soil Moisture and Ocean Salinity (SMOS) mission can be used to retrieve sea ice thickness of up to 0.5-1.0 m. Since 2015, NASA's Soil Moisture Active Passive (SMAP) mission provides brightness temperatures (TB) at the same frequency. Here, we explore the possibility of combining SMOS and SMAP TBs for sea ice thickness retrieval. First, we compare daily TBs over polar ocean and sea ice regions. For this purpose, the multi-angular SMOS measurements have to be fitted to the SMAP incidence angle of 40 • . Using a synthetical dataset for testing, we evaluate the performance of different fitting methods. We find that a two-step regression fitting method performs best, yielding a high accuracy even for a small number of measurements of only 15. Generally, SMOS and SMAP TBs agree very well with correlations exceeding 0.99 over sea ice but show an intensity bias of about 2.7 K over both ocean and sea ice regions. This bias can be adjusted using a linear fit resulting in a very good agreement of the retrieved sea ice thicknesses. The main advantages of a combined product are the increased number of daily overpasses leading to an improved data coverage also towards lower latitudes, as well as a continuation of retrieved timeseries if one of the sensors stops delivering data.

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Section: Smosmentioning

confidence: 99%

A Consistent Combination of Brightness Temperatures from SMOS and SMAP over Polar Oceans for Sea Ice Applications

Tetzlaff

Kaleschke

2018

Remote Sensing

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“…More details on the algorithm implementation can be found in [38]. Figure 4 shows the results of the proposed RFI detection/cancellation algorithm over real SMOS data acquired over the Caribbean sea: (a) the original brightness temperature image corrupted by a strong RFI, (b) the RFI "cleaned" brightness temperature; (c) the estimated RFI, and (d) the confidence of the RFI sources removed level (from 0 to 1, 0 being totally unreliable, and 1 being totally reliable).…”

Section: Radio Frequency Interference Detection and Mitigationmentioning

confidence: 99%

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

et al. 2012

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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.

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“…For the previous SMOS RFI detection methods using L1C data [8], [10], [15], the local maxima with BT > 350 K are selected as candidates of RFI spots. In the MUSIC pseudo-spectrum, local maxima are selected regardless of the spectrum values, i.e., a fixed threshold cannot be applied.…”

Section: F Local Peak Detectionmentioning

confidence: 99%

“…However, Radio-Frequency Interferences (RFI) have been detected since the first in-orbit measurements. RFI hamper the geophysical parameter retrieval in the affected pixels [5]- [8]. The interference nature in the impulse response of MIRAS makes contaminations even more serious [5], [7].…”

Section: Introductionmentioning

confidence: 99%

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Improved MUSIC-Based SMOS RFI Source Detection and Geolocation Algorithm

Park

González-Gambau²,

Camps

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission has been providing L-band brightness temperature using its instrument, the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS).In the measurements, the negative effect of Radio-Frequency Interference (RFI) is clearly present, deteriorating the quality of geophysical parameter retrieval. Detection and geolocation of RFI sources are essential to remove or at least mitigate the RFI impacts, and ultimately improve the performance of parameter retrieval. This paper discusses a new approach to SMOS RFI source detection, based on MUSIC (MUltiple SIgnal Classification) algorithm. Recently, the feasibility of MUSIC DOA (Direction Of Arrival) estimation has been shown for the RFI source detection of the Synthetic Aperture Interferometric Radiometer. This paper refines MUSIC RFI source detection algorithm, and tailors it to the SMOS scenario. To consolidate the RFI source detection procedure, several required steps are devised, including the rank estimation of the covariance matrix, local peak detection and thresholds, and multiple-snapshot processing. The developed method is tested using a number of SMOS visibility samples. In the test results, the MUSIC method shows an improvement on the accuracy and precision of the RFI source geolocation, compared with a simple detection method based on the local peaks of Brightness Temperature (BT) images. The MUSIC results especially outperform the SMOS BT image on the spatial resolution. Index TermsRadio-Frequency Interferences (RFI), synthetic aperture radiometry, microwave radiometry, beamforming, Direction of Arrival (DOA) estimation, Soil Moisture and Ocean Salinity (SMOS) mission.H. Park, A. Camps, and M. Vall-llossera are with the Remote

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

Cited by 56 publications

References 10 publications

A Consistent Combination of Brightness Temperatures from SMOS and SMAP over Polar Oceans for Sea Ice Applications

A Consistent Combination of Brightness Temperatures from SMOS and SMAP over Polar Oceans for Sea Ice Applications

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

Improved MUSIC-Based SMOS RFI Source Detection and Geolocation Algorithm

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