SMOS Radio Frequency Interference Scenario: Status and Actions Taken to Improve the RFI Environment in the 1400–1427-MHz Passive Band

Oliva, Roger; Daganzo, E.; Kerr, Yann H.; Mecklenburg, Susanne; Nieto, S. Soria; Richaume, Philippe; Gruhier, Claire

doi:10.1109/tgrs.2012.2182775

Cited by 248 publications

(142 citation statements)

References 16 publications

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“…4.1 that the radiative transfer modelling works reliably under most conditions in the study area, this points towards an RFI issue because it affects both L1c and L2 data. The mean positive bias in the SMOS brightness temperatures (compare Table 2) adds to this argumentation (Oliva et al, 2012) state that RFI can produce higher SMOS brightness temperatures which would lead to a dry bias in the soil moisture retrievals. The mean positive bias in the SMOS brightness temperatures can partly explain the observed dry bias in the SMOS L2 soil moisture products, that were found by dall'Amico (2012).…”

Section: Comparison With Modelled Brightness Temperatures For the Yeamentioning

confidence: 84%

“…For comparisons between modelled soil moisture and SMOS soil moisture, the mean correlation coefficient in the Vils test site for 2011 is 0.54, the mean bias 0.13 m 3 m 3 . In Europe the performance of the SMOS L2 soil moisture product was considerably affected by radio frequency interference (RFI) since the launch of SMOS (Albergel et al, 2012;Balling et al, 2011), but the amount of contaminated data has exhibited a decrease due to RFI mitigation efforts and switching off of RFI sources (Oliva et al, 2012). In 2010, several RFI sources were obvious in SMOS L1c data in Germany that have disappeared in 2011.…”

Section: F Schlenz Et Al: Analysis Of Smos Brightness Temperature Amentioning

confidence: 99%

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Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

Schlenz¹,

Dall'Amico²,

Mauser³

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract. Soil Moisture and Ocean Salinity (SMOS) L1c brightness temperature and L2 optical depth data are analysed with a coupled land surface (PROMET) and radiative transfer model (L-MEB). The coupled models are validated with ground and airborne measurements under contrasting soil moisture, vegetation and land surface temperature conditions during the SMOS Validation Campaign in May and June 2010 in the SMOS test site Upper Danube Catchment in southern Germany. The brightness temperature root-meansquared errors are between 6 K and 9 K. The L-MEB parameterisation is considered appropriate under local conditions even though it might possibly be further optimised. SMOS L1c brightness temperature data are processed and analysed in the Upper Danube Catchment using the coupled models in 2011 and during the SMOS Validation Campaign 2010 together with airborne L-band brightness temperature data. Only low to fair correlations are found for this comparison (R between 0.1-0.41). SMOS L1c brightness temperature data do not show the expected seasonal behaviour and are positively biased. It is concluded that RFI is responsible for a considerable part of the observed problems in the SMOS data products in the Upper Danube Catchment. This is consistent with the observed dry bias in the SMOS L2 soil moisture products which can also be related to RFI. It is confirmed that the brightness temperature data from the lower SMOS look angles and the horizontal polarisation are less reliable. This information could be used to improve the brightness temperature data filtering before the soil moisture retrieval. SMOS L2 optical depth values have been compared to modelled data and are not considered a reliable source of information about vegetation due to missing seasonal behaviour and a very high mean value. A fairly strong correlation between SMOS L2 soil moisture and optical depth was found (R = 0.65) even though the two variables are considered independent in the study area. The value of coupled models as a tool for the analysis of passive microwave remote-sensing data is demonstrated by extending this SMOS data analysis from a few days during a field campaign to a longer term comparison.

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Section: Comparison With Modelled Brightness Temperatures For the Yeamentioning

confidence: 84%

Section: F Schlenz Et Al: Analysis Of Smos Brightness Temperature Amentioning

confidence: 99%

Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

Schlenz¹,

Dall'Amico²,

Mauser³

et al. 2012

Hydrol. Earth Syst. Sci.

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show abstract

“…The second component of the retrieval algorithm is an iterative optimisation scheme that minimises a Bayesian cost function constructed from the observed and the modelled TBs in order to retrieve the physical parameter values. Preprocessing and post-processing steps are implemented to filter the input and output data for undesired effects like the decrease in quality due to spatial sampling or radio frequency interferences (RFIs) (Oliva et al, 2012;Richaume et al, 2014).…”

Section: Algorithm Overviewmentioning

confidence: 99%

“…-Ascending and descending orbits are processed separately since the impact of RFI (Oliva et al, 2012) and sun corrections (Khazâal et al, 2016) between ascending and descending orbits are very different.…”

Section: Orbit Selectionmentioning

confidence: 99%

The global SMOS Level 3 daily soil moisture and brightness temperature maps

Bitar

Mialon

Kerr

et al. 2017

Earth Syst. Sci. Data

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187

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Abstract. The objective of this paper is to present the multi-orbit (MO) surface soil moisture (SM) and anglebinned brightness temperature (TB) products for the SMOS (Soil Moisture and Ocean Salinity) mission based on a new multi-orbit algorithm. The Level 3 algorithm at CATDS (Centre Aval de Traitement des Données SMOS) makes use of MO retrieval to enhance the robustness and quality of SM retrievals. The motivation of the approach is to make use of the longer temporal autocorrelation length of the vegetation optical depth (VOD) compared to the corresponding SM autocorrelation in order to enhance the retrievals when an acquisition occurs at the border of the swath. The retrieval algorithm is implemented in a unique operational processor delivering multiple parameters (e.g. SM and VOD) using multi-angular dual-polarisation TB from MO. A subsidiary anglebinned TB product is provided. In this study the Level 3 TB V310 product is showcased and compared to SMAP (Soil Moisture Active Passive) TB. The Level 3 SM V300 product is compared to the single-orbit (SO) retrievals from the Level 2 SM processor from ESA with aligned configuration. The advantages and drawbacks of the Level 3 SM product (L3SM) are discussed. The comparison is done on a global scale between the two datasets and on the local scale with respect to in situ data from AMMA-CATCH and USDA ARS Watershed networks. The results obtained from the global analysis show that the MO implementation enhances the number of retrievals: up to 9 % over certain areas. The comparison with the in situ data shows that the increase in the number of retrievals does not come with a decrease in quality, but rather at the expense of an increased time lag in product availability from 6 h to 3.5 days, which can be a limiting factor for applications like flood forecast but reasonable for drought monitoring and climate change studies. The SMOS L3 soil moisture and L3 brightness temperature products are delivered using an open licence and free of charge using a web application

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“…RFI extends over large areas of the world, mainly in Europe, Middle East and Asia, due to the different frequency allocations [33]. Since RFI is highly variable in time and polarization, average values of previous information cannot be reliably used.…”

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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SMOS Radio Frequency Interference Scenario: Status and Actions Taken to Improve the RFI Environment in the 1400–1427-MHz Passive Band

Cited by 248 publications

References 16 publications

Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

The global SMOS Level 3 daily soil moisture and brightness temperature maps

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

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