SMAP L-Band Microwave Radiometer: RFI Mitigation Prelaunch Analysis and First Year On-Orbit Observations

Mohammed, Priscilla N.; Aksoy, Mustafa; Piepmeier, Jeffrey R.; Johnson, Joel T.; Bringer, Alexandra

doi:10.1109/tgrs.2016.2580459

Cited by 93 publications

(54 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The situation has now improved, but the presence of RFI still masks SMOS observations, severely limiting its coverage in some regions [27]. The impact of RFI is considerably reduced in SMAP, since a number of hardware and software measures were implemented to detect and where possible mitigate its effects [28]. In this regard, recent studies combining the brightness temperatures of the two missions are particularly promising [29].…”

Section: Smos Soil Moisturementioning

confidence: 99%

Dominant Features of Global Surface Soil Moisture Variability Observed by the SMOS Satellite

2019

View full text Add to dashboard Cite

Soil moisture observations are expected to play an important role in monitoring global climate trends. However, measuring soil moisture is challenging because of its high spatial and temporal variability. Point-scale in-situ measurements are scarce and, excluding model-based estimates, remote sensing remains the only practical way to observe soil moisture at a global scale. The ESA-led Soil Moisture and Ocean Salinity (SMOS) mission, launched in 2009, measures the Earth's surface natural emissivity at L-band and provides highly accurate soil moisture information with a 3-day revisiting time. Using the first six full annual cycles of SMOS measurements (June 2010-June 2016), this study investigates the temporal variability of global surface soil moisture. The soil moisture time series are decomposed into a linear trend, interannual, seasonal, and high-frequency residual (i.e., subseasonal) components. The relative distribution of soil moisture variance among its temporal components is first illustrated at selected target sites representative of terrestrial biomes with distinct vegetation type and seasonality. A comparison with GLDAS-Noah and ERA5 modeled soil moisture at these sites shows general agreement in terms of temporal phase except in areas with limited temporal coverage in winter season due to snow. A comparison with ground-based estimates at one of the sites shows good agreement of both temporal phase and absolute magnitude. A global assessment of the dominant features and spatial distribution of soil moisture variability is then provided. Results show that, despite still being a relatively short data set, SMOS data provides coherent and reliable variability patterns at both seasonal and interannual scales. Subseasonal components are characterized as white noise. The observed linear trends, based upon one strong El Niño event in 2016, are consistent with the known El Niño Southern Oscillation (ENSO) teleconnections. This work provides new insight into recent changes in surface soil moisture and can help further our understanding of the terrestrial branch of the water cycle and of global patterns of climate anomalies. Also, it is an important support to multi-decadal soil moisture observational data records, hydrological studies and land data assimilation projects using remotely sensed observations.

show abstract

Section: Smos Soil Moisturementioning

confidence: 99%

Dominant Features of Global Surface Soil Moisture Variability Observed by the SMOS Satellite

2019

View full text Add to dashboard Cite

show abstract

“…This is illustrated in recent experience with SMOS, Aquarius and SMAP, each of which have experienced significant RFI, especially over land [11,15,17] even though they are single frequency radiometers which measure in the protected band at 1.413 GHz. Figure 2 is an example of the RFI experienced by Aquarius [11].…”

Section: Rfi At L-bandmentioning

confidence: 99%

“…The SMAP receiver also oversamples in time, but also divides the 24 MHz L-band received signal into 16 subbands. SMAP then applies RFI detection algorithms developed to detect pulsed interference, narrowband interference, and other sources of interference using the kurtosis statistic [15]. Fig 6 is an example illustrating SMAP's RFI processing aspects.…”

Section: Trends In Rfi Detection and Mitigationmentioning

confidence: 99%

“…The lower portions of the figure show SMAP's 16 subchannel spectrogram (in this case for multiple footprints) before (left) and after (right) RFI detection algorithms are applied to remove corrupted portions of the spectrogram. In this case, extreme RFI corruption results in loss of footprint measurements during this overpass [15]. While SMAP's RFI processor is capable of detecting and removing some RFI corrupted portions of the spectrogram, doing so reduces the measurement sensitivity and results in a degraded measurement of the desired geophysical products.…”

Section: Trends In Rfi Detection and Mitigationmentioning

confidence: 99%

See 1 more Smart Citation

RFI and Remote Sensing of the Earth From Space

Vine

2019

J. Astron. Instrum.

View full text Add to dashboard Cite

Passive microwave remote sensing of the Earth from space provides information essential for understanding the Earth’s environment and its evolution. Parameters such as soil moisture, sea surface temperature and salinity, and profiles of atmospheric temperature and humidity are measured at frequencies determined by the physics (e.g. sensitivity to changes in desired parameters) and by the availability of suitable spectrum free from interference. Interference from man-made sources (radio frequency interference) is an impediment that in many cases limits the potential for accurate measurements from space. A review is presented here of the frequencies employed in passive microwave remote sensing of the Earth from space and of the associated experience with RFI and contemporary approaches to address the problem.

show abstract

“…The preceding technical details of SMAP were presented in Entekhabi et al (2014). SMAP also includes on-board detection and filtering of radio frequency interference (RFI) while SMOS does not (Mohammed et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Combined SMAP–SMOS thin sea ice thickness retrieval

et al. 2019

View full text Add to dashboard Cite

The spaceborne passive microwave sensors Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) provide brightness temperature data in the L band (1.4 GHz). At this low frequency the atmosphere is close to transparent and in polar regions the thickness of thin sea ice can be derived. SMOS measurements cover a large incidence angle range, whereas SMAP observes at a fixed 40 • incidence angle. By using brightness temperatures at a fixed incidence angle obtained directly (SMAP), or through interpolation (SMOS), thin sea ice thickness retrieval is more consistent as the incidence angle effects do not have to be taken into account. Here we transfer a retrieval algorithm for the thickness of thin sea ice (up to 50 cm) from SMOS data at 40 to 50 • incidence angle to the fixed incidence angle of SMAP. The SMOS brightness temperatures (TBs) at a given incidence angle are estimated using empirical fit functions. SMAP TBs are calibrated to SMOS to provide a merged SMOS-SMAP sea ice thickness product. The new merged SMOS-SMAP thin ice thickness product was improved upon in several ways compared to previous thin ice thickness retrievals. (i) The combined product provides a better temporal and spatial coverage of the polar regions due to the usage of two sensors. (ii) The radio frequency interference (RFI) filtering method was improved, which results in higher data availability over both ocean and sea ice areas. (iii) For the intercalibration between SMOS and SMAP brightness temperatures the root mean square difference (RMSD) was reduced by 30 % relative to a prior attempt. (iv) The algorithm presented here allows also for separate retrieval from any of the two sensors, which makes the ice thickness dataset more resistant against failure of one of the sensors. A new way to estimate the uncertainty of ice thickness retrieval was implemented, which is based on the brightness temperature sensitivities.

show abstract

SMAP L-Band Microwave Radiometer: RFI Mitigation Prelaunch Analysis and First Year On-Orbit Observations

Cited by 93 publications

References 14 publications

Dominant Features of Global Surface Soil Moisture Variability Observed by the SMOS Satellite

Dominant Features of Global Surface Soil Moisture Variability Observed by the SMOS Satellite

RFI and Remote Sensing of the Earth From Space

Combined SMAP–SMOS thin sea ice thickness retrieval

Contact Info

Product

Resources

About