Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: A Synthetic Analysis

Schwank, Mike; Mätzler, Christian; Wiesmann, Andreas; Wegmüller, U.; Pulliainen, Jouni; Lemmetyinen, Juha; Rautiainen, Kimmo; Derksen, Chris; Toose, Peter; Drusch, Matthias

doi:10.1109/jstars.2015.2422998

Cited by 64 publications

(64 citation statements)

References 23 publications

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“…This approach is similar to that used by other authors (e.g. Mills and Heygster, 2011b;Maaß, 2013;Schwank et al, 2015). These assumptions are realistic for the emission of sea ice that is thicker than about 60 cm at the observing frequency of SMOS, as discussed in Sect.…”

Section: Osi Saf and Other Sea Ice Data Productsmentioning

confidence: 81%

“…Dry snow can be considered a lossless medium at 1.4 GHz, due to the fact that the imaginary part of ε is very small compared with the real part, as stated in Schwank et al (2015). That means that there is no attenuation in the snow layer, and therefore its attenuation coefficient, α snow , is considered zero.…”

Section: Osi Saf and Other Sea Ice Data Productsmentioning

confidence: 99%

“…There exist a variety of algorithms with which to retrieve SIC from T B observations tuned to those higherfrequency sensors, that is, frequencies between 6 and 89 GHz (e.g. Cavalieri et al, 1984;Comiso, 1986;Ramseier, 1991;Smith, 1996;Markus and Cavalieri, 2000;Kaleschke et al, 2001;Shokr et al, 2008). Those algorithms present different advantages and drawbacks depending on frequency, spatial resolution, atmospheric effects, physical temperature, and others.…”

Section: Introductionmentioning

confidence: 99%

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New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator

et al. 2017

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Abstract. Monitoring sea ice concentration is required for operational and climate studies in the Arctic Sea. Technologies used so far for estimating sea ice concentration have some limitations, for instance the impact of the atmosphere, the physical temperature of ice, and the presence of snow and melting. In the last years, L-band radiometry has been successfully used to study some properties of sea ice, remarkably sea ice thickness. However, the potential of satellite Lband observations for obtaining sea ice concentration had not yet been explored.In this paper, we present preliminary evidence showing that data from the Soil Moisture Ocean Salinity (SMOS) mission can be used to estimate sea ice concentration. Our method, based on a maximum-likelihood estimator (MLE), exploits the marked difference in the radiative properties of sea ice and seawater. In addition, the brightness temperatures of 100 % sea ice and 100 % seawater, as well as their combined values (polarization and angular difference), have been shown to be very stable during winter and spring, so they are robust to variations in physical temperature and other geophysical parameters. Therefore, we can use just two sets of tie points, one for summer and another for winter, for calculating sea ice concentration, leading to a more robust estimate.After analysing the full year 2014 in the entire Arctic, we have found that the sea ice concentration obtained with our method is well determined as compared to the Ocean and Sea Ice Satellite Application Facility (OSI SAF) dataset. However, when thin sea ice is present (ice thickness 0.6 m), the method underestimates the actual sea ice concentration.Our results open the way for a systematic exploitation of SMOS data for monitoring sea ice concentration, at least for specific seasons. Additionally, SMOS data can be synergistically combined with data from other sensors to monitor panArctic sea ice conditions.

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Section: Osi Saf and Other Sea Ice Data Productsmentioning

confidence: 81%

Section: Osi Saf and Other Sea Ice Data Productsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator

et al. 2017

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“…The NASA Aquarius instrument on board the Argentine SAC-D spacecraft acquired L-band observations between September 2012 to July 2015 (Lagerloef et al, 2013), and the NASA Soil Moisture Active Passive (SMAP) satellite was launched in January 2015 (Entekhabi et al, 2015). In addition to monitoring soil moisture and sea surface salinity, these missions also provide useful measurements for cryospheric applications including monitoring the freeze-thaw state of the land surface (Rautiainen et al, , 2014Roy et al, 2015), estimating snow density and ground permittivity (Schwank et al, 2015;Lemmetyinen et al, 2016), and retrieving the thickness of thin sea ice (Kaleschke et al, 2016(Kaleschke et al, , 2012.…”

Section: Introductionmentioning

confidence: 99%

Radio-frequency interference mitigating hyperspectral L-band radiometer

Toose

Roy

Solheim³

et al. 2017

Geosci. Instrum. Method. Data Syst.

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Abstract. Radio-frequency interference (RFI) can significantly contaminate the measured radiometric signal of current spaceborne L-band passive microwave radiometers. These spaceborne radiometers operate within the protected passive remote sensing and radio-astronomy frequency allocation of 1400-1427 MHz but nonetheless are still subjected to frequent RFI intrusions. We present a unique surfacebased and airborne hyperspectral 385 channel, dual polarization, L-band Fourier transform, RFI-detecting radiometer designed with a frequency range from 1400 through ≈ 1550 MHz. The extended frequency range was intended to increase the likelihood of detecting adjacent RFI-free channels to increase the signal, and therefore the thermal resolution, of the radiometer instrument. The external instrument calibration uses three targets (sky, ambient, and warm), and validation from independent stability measurements shows a mean absolute error (MAE) of 1.0 K for ambient and warm targets and 1.5 K for sky. A simple but effective RFI removal method which exploits the large number of frequency channels is also described. This method separates the desired thermal emission from RFI intrusions and was evaluated with synthetic microwave spectra generated using a Monte Carlo approach and validated with surface-based and airborne experimental measurements.

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“…L'humidité des sols étant un traceur des pluies, il est également possible de corriger les estimations de précipitations liquides obtenues par satellite en assimilant l'humidité superficielle dans des modèles hydrologiques, permettant ainsi d'obtenir une estimation des précipitations plus réaliste (Brocca et al, 2016). D'autres travaux (Schwank et al, 2015) montrent qu'il est possible d'obtenir la densité de la neige. Une équipe à l'Institut météorologique finlandais travaille par ailleurs sur le suivi au niveau mondial du gel et du dégel (Rautiainen et al, 2016).…”

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