A sea ice thickness retrieval model for 1.4 GHz radiometry and application to airborne measurements over low salinity sea ice

Abstract. In preparation for the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission we investigated the potential of L-band (1.4 GHz) radiometery to measure sea ice thickness. Sea ice brightness temperature was measured at 1.4 GHz and ice thickness were measured along nearly coincident flight tracks during the SMOS Sea-Ice campaign in the Bay of Bothnia in March 2007. A research aircraft was equipped with the L-band Radiometer EMIRAD and coordinated with helicopter based electromagnetic induction (EM) ice thickness measurements. We developed a three layer (ocean-ice-atmosphere) dielectric slab model for the calculation of ice thickness from brightness temperature. The dielectric properties depend on the relative brine volume which is a function of the bulk ice salinity and temperature. The model calculations suggest a thickness sensitivity of up to 1.5 m for low-salinity (multi-year or brackish) sea ice. For Arctic first year ice the modeled thickness sensitivity is roughly half a meter. It reduces to a few centimeters for temperatures approaching the melting point. Although the campaign was conducted under such unfavorable melting conditions and despite limited spatial overlap between the L-band and EM-measurements was small we demonstrate a large potential for retrieving the ice thickness in the range of 0.2 to 1.5 m. Furthermore, we show that the ice thickness derived from SMOS measurements would be complementary to ESA's CryoSat-2 mission in terms of the error characteristics and the spatio-temporal coverage.

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A sea-ice thickness retrieval model for 1.4 GHz radiometry and application to airborne measurements over low salinity sea-ice

The Cryosphere ◽

10.5194/tc-4-583-2010 ◽

2010 ◽

Vol 4 (4) ◽

pp. 583-592 ◽

Cited By ~ 59

Author(s):

L. Kaleschke ◽

N. Maaß ◽

C. Haas ◽

S. Hendricks ◽

G. Heygster ◽

...

Keyword(s):

Sea Ice ◽

Brightness Temperature ◽

Ice Thickness ◽

First Year ◽

Sea Ice Thickness ◽

Model Calculations ◽

Airborne Measurements ◽

Low Salinity ◽

Research Aircraft ◽

L Band

Abstract. In preparation for the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, we investigated the potential of L-band (1.4 GHz) radiometry to measure sea-ice thickness. Sea-ice brightness temperature was measured at 1.4 GHz and ice thickness was measured along nearly coincident flight tracks during the SMOS Sea-Ice campaign in the Bay of Bothnia in March 2007. A research aircraft was equipped with the L-band Radiometer EMIRAD and coordinated with helicopter based electromagnetic induction (EM) ice thickness measurements. We developed a three layer (ocean-ice-atmosphere) dielectric slab model for the calculation of ice thickness from brightness temperature. The dielectric properties depend on the relative brine volume which is a function of the bulk ice salinity and temperature. The model calculations suggest a thickness sensitivity of up to 1.5 m for low-salinity (multi-year or brackish) sea-ice. For Arctic first year ice the modelled thickness sensitivity is less than half a meter. It reduces to a few centimeters for temperatures approaching the melting point. The campaign was conducted under unfavorable melting conditions and the spatial overlap between the L-band and EM-measurements was relatively small. Despite these disadvantageous conditions we demonstrate the possibility to measure the sea-ice thickness with the certain limitation up to 1.5 m. The ice thickness derived from SMOS measurements would be complementary to ESA's CryoSat-2 mission in terms of the error characteristics and the spatiotemporal coverage. The relative error for the SMOS ice thickness retrieval is expected to be not less than about 20%.

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Effects of decimetre-scale surface roughness on L-band Brightness Temperature of Sea Ice

10.5194/tc-2019-110 ◽

2019 ◽

Author(s):

Maciej Miernecki ◽

Lars Kaleschke ◽

Nina Maaß ◽

Stefan Hendricks ◽

Sten Schmidl Søbjrg

Keyword(s):

Surface Roughness ◽

Sea Ice ◽

Brightness Temperature ◽

Large Scale ◽

Ice Thickness ◽

Emission Model ◽

Vertical Polarization ◽

Sea Ice Thickness ◽

L Band ◽

Scale Surface

Abstract. Sea ice thickness measurements with L-band radiometry is a technique which allows daily, weather-independent monitoring of the polar sea ice cover. The sea-ice thickness retrieval algorithms relay on the sensitivity of the L-band brightness temperature to sea-ice thickness. In this work, we investigate the decimetre-scale surface roughness as a factor influencing the L-band emissions from sea ice. We used an airborne laser scanner to construct a digital elevation model of the sea ice surface. We found that the probability density function of surface slopes is exponential for a range of degrees of roughness. Then we applied the geometrical optics, bounded with the MIcrowave L-band LAyered Sea ice emission model in the Monte Carlo simulation to simulate the effects of surface roughness. According to this simulations, the most affected by surface roughness is the vertical polarization around Brewster's angle, where the decrease in brightness temperature can reach 8 K. The vertical polarization for the same configuration exhibits a 4 K increase. The near-nadir angles are little affected, up to 2.6 K decrease for the most deformed ice. Overall the effects of large-scale surface roughness can be expressed as a superposition of two factors: the change in intensity and the polarization mixing. The first factor depends on surface permittivity, second shows little dependence on it. Comparison of the brightness temperature simulations with the radiometer data does not yield definite results.

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Assessment with Controlled In-Situ Data of the Dependence of L-Band Radiometry on Sea-Ice Thickness

Remote Sensing ◽

10.3390/rs12040650 ◽

2020 ◽

Vol 12 (4) ◽

pp. 650

Author(s):

Pablo Sánchez-Gámez ◽

Carolina Gabarro ◽

Antonio Turiel ◽

Marcos Portabella

Keyword(s):

Soil Moisture ◽

Sea Ice ◽

Brightness Temperature ◽

European Space Agency ◽

Ground Truth ◽

Ice Thickness ◽

Sea Ice Thickness ◽

In Situ Data ◽

L Band

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) missions are providing brightness temperature measurements at 1.4 GHz (L-band) for about 10 and 4 years respectively. One of the new areas of geophysical exploitation of L-band radiometry is on thin (i.e., less than 1 m) Sea Ice Thickness (SIT), for which theoretical and empirical retrieval methods have been proposed. However, a comprehensive validation of SIT products has been hindered by the lack of suitable ground truth. The in-situ SIT datasets most commonly used for validation are affected by one important limitation: They are available mainly during late winter and spring months, when sea ice is fully developed and the thickness probability density function is wider than for autumn ice and less representative at the satellite spatial resolution. Using Upward Looking Sonar (ULS) data from the Woods Hole Oceanographic Institution (WHOI), acquired all year round, permits overcoming the mentioned limitation, thus improving the characterization of the L-band brightness temperature response to changes in thin SIT. State-of-the-art satellite SIT products and the Cumulative Freezing Degree Days (CFDD) model are verified against the ULS ground truth. The results show that the L-band SIT can be meaningfully retrieved up to 0.6 m, although the signal starts to saturate at 0.3 m. In contrast, despite the simplicity of the CFDD model, its predicted SIT values correlate very well with the ULS in-situ data during the sea ice growth season. The comparison between the CFDD SIT and the current L-band SIT products shows that both the sea ice concentration and the season are fundamental factors influencing the quality of the thickness retrieval from L-band satellites.

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Effects of decimetre-scale surface roughness on L-band brightness temperature of sea ice

The Cryosphere ◽

10.5194/tc-14-461-2020 ◽

2020 ◽

Vol 14 (2) ◽

pp. 461-476 ◽

Cited By ~ 1

Author(s):

Maciej Miernecki ◽

Lars Kaleschke ◽

Nina Maaß ◽

Stefan Hendricks ◽

Sten Schmidl Søbjærg

Keyword(s):

Surface Roughness ◽

Sea Ice ◽

Brightness Temperature ◽

Geometrical Optics ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Ice Surface ◽

L Band ◽

Simulation Results ◽

Ice Surface Roughness

Abstract. Sea ice thickness is an essential climate variable. Current L-Band sea ice thickness retrieval methods do not account for sea ice surface roughness that is hypothesised to be not relevant to the process. This study attempts to validate this hypothesis that has not been tested yet. To test this hypothesis, we created a physical model of sea ice roughness based on geometrical optics and merged it into the L-band emissivity model of sea ice that is similar to the one used in the operational sea ice thickness retrieval algorithm. The facet description of sea ice surface used in geometrical optics is derived from 2-D surface elevation measurements. Subsequently the new model was tested with TB measurements performed during the SMOSice 2014 field campaign. Our simulation results corroborate the hypothesis that sea ice surface roughness has a marginal impact on near-nadir TB (used in the current operational retrieval). We demonstrate that the probability distribution function of surface slopes can be approximated with a parametric function whose single parameter can be used to characterise the degree of roughness. Facet azimuth orientation is isotropic at scales greater than 4.3 km. The simulation results indicate that surface roughness is a minor factor in modelling the sea ice brightness temperature. The change in TB is most pronounced at incidence angles greater than 40∘ and can reach up to 8 K for vertical polarisation at 60∘. Therefore current and future L-band missions (SMOS, SMAP, CIMR, SMOS-HR) measuring at such angles can be affected. Comparison of the brightness temperature simulations with the SMOSice 2014 radiometer data does not yield definite results.

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Combined SMAP–SMOS thin sea ice thickness retrieval

The Cryosphere ◽

10.5194/tc-13-675-2019 ◽

2019 ◽

Vol 13 (2) ◽

pp. 675-691 ◽

Cited By ~ 11

Author(s):

Cătălin Paţilea ◽

Georg Heygster ◽

Marcus Huntemann ◽

Gunnar Spreen

Keyword(s):

Soil Moisture ◽

Sea Ice ◽

Brightness Temperature ◽

Incidence Angle ◽

Data Availability ◽

Ice Thickness ◽

Polar Regions ◽

Sea Ice Thickness ◽

Brightness Temperatures ◽

Spatial Coverage

Abstract. 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.

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Retrieval of sea-ice thickness distribution in the Sea of Okhotsk from ALOS/PALSAR backscatter data

Annals of Glaciology ◽

10.3189/172756411795931732 ◽

2011 ◽

Vol 52 (57) ◽

pp. 177-184 ◽

Cited By ~ 7

Author(s):

Takenobu Toyota ◽

Shuji Ono ◽

Kohei Cho ◽

Kay I. Ohshima

Keyword(s):

Surface Roughness ◽

Sea Ice ◽

Sea Of Okhotsk ◽

Thickness Distribution ◽

Ice Thickness ◽

Alos Palsar ◽

Sea Ice Thickness ◽

Promising Tool ◽

L Band ◽

Interferometric Sar

AbstractAlthough satellite data are useful for obtaining ice-thickness distribution for perennial sea ice or in stable thin-sea-ice areas, they are still largely an unresolved issue for the seasonal ice zone (SIZ). We address this problem using L-band synthetic aperture radar (SAR). In the SIZ, ice-thickness growth is closely related to deformation, so surface roughness is expected to correlate with ice thickness. L-band SAR, suitable for detecting such surface roughness, is a promising tool for obtaining thickness distribution. This idea was supported by an airborne polarimetric and interferometric SAR (Pi-SAR) validation. To extend this result to spaceborne L-band SAR with coarser resolution, we conducted in situ measurements of ice thickness and surface roughness in February 2008 in the southern Sea of Okhotsk with an icebreaker in coordination with the Advanced Land Observing Satellite (ALOS)/Phased Array-type L-band SAR (PALSAR) orbit. A helicopter-borne laser profiler was used to improve the estimation of surface roughness. It was found that backscatter coefficients (HH) correlated well with ice thickness (R = 0.86) and surface roughness (R = 0.70), which confirms the possibility of determining ice-thickness distribution in the SIZ. the interannual variation of PALSAR-derived ice-thickness distribution in the southern Sea of Okhotsk is also discussed.

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On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

The Cryosphere ◽

10.5194/tc-12-993-2018 ◽

2018 ◽

Vol 12 (3) ◽

pp. 993-1012 ◽

Cited By ~ 8

Author(s):

Lu Zhou ◽

Shiming Xu ◽

Jiping Liu ◽

Bin Wang

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Snow Depth ◽

Microwave Remote Sensing ◽

Ice Cover ◽

Ice Thickness ◽

Laser Altimetry ◽

Sea Ice Thickness ◽

Passive Remote Sensing ◽

L Band

Abstract. The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

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On the Retrieval of Sea Ice Thickness and Snow Depth using Concurrent Laser Altimetry and L-Band Remote Sensing Data

10.5194/tc-2017-133 ◽

2017 ◽

Author(s):

Lu Zhou ◽

Shiming Xu ◽

Jiping Liu ◽

Bin Wang

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Snow Depth ◽

Ice Cover ◽

Retrieval Algorithm ◽

Ice Thickness ◽

Laser Altimetry ◽

Sea Ice Thickness ◽

Passive Remote Sensing ◽

L Band

Abstract. The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, are key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved, as compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry, as well the uncertainty associated with the radiation model are potential sources of error. The proposed retrieval algorithm (or methodology) can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat and ICESat-2.

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Sea Ice Thickness Estimation Based on Regression Neural Networks Using L-Band Microwave Radiometry Data from the FSSCat Mission

Remote Sensing ◽

10.3390/rs13071366 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1366

Author(s):

Christoph Herbert ◽

Joan Francesc Munoz-Martin ◽

David Llaveria ◽

Miriam Pablos ◽

Adriano Camps

Keyword(s):

Neural Networks ◽

Soil Moisture ◽

Sea Ice ◽

Full Range ◽

The Arctic ◽

Ice Thickness ◽

Ancillary Data ◽

Sea Ice Thickness ◽

Ground Truth Data ◽

L Band

Several methods have been developed to provide polar maps of sea ice thickness (SIT) from L-band brightness temperature (TB) and altimetry data. Current process-based inversion methods to yield SIT fail to address the complex surface characteristics because sea ice is subject to strong seasonal dynamics and ice-physical properties are often non-linearly related. Neural networks can be trained to find hidden links among large datasets and often perform better on convoluted problems for which traditional approaches miss out important relationships between the observations. The FSSCat mission launched on 3 September 2020, carries the Flexible Microwave Payload-2 (FMPL-2), which contains the first Reflected Global Navigation Satellite System (GNSS-R) and L-band radiometer on board a CubeSat—designed to provide TB data on global coverage for soil moisture retrieval, and sea ice applications. This work investigates a predictive regression neural network approach with the goal to infer SIT using FMPL-2 TB and ancillary data (sea ice concentration, surface temperature, and sea ice freeboard). Two models—covering thin ice up to 0.6 m and full-range thickness—were separately trained on Arctic data in a two-month period from mid-October to the beginning of December 2020, while using ground truth data derived from the Soil Moisture and Ocean Salinity (SMOS) and Cryosat-2 missions. The thin ice and the full-range models resulted in a mean absolute error of 6.5 cm and 23 cm, respectively. Both of the models allowed for one to produce weekly composites of Arctic maps, and monthly composites of Antarctic SIT were predicted based on the Arctic full-range model. This work presents the first results of the FSSCat mission over the polar regions. It reveals the benefits of neural networks for sea ice retrievals and demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation.

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Deriving sea-ice thickness and ice types in the Sea of Okhotsk using dual-frequency airborne SAR (Pi-SAR) data

Annals of Glaciology ◽

10.3189/172756402781817392 ◽

2002 ◽

Vol 34 ◽

pp. 429-434 ◽

Cited By ~ 12

Author(s):

Takeshi Matsuoka ◽

Seiho Uratsuka ◽

Makoto Satake ◽

Akitsugu Nadai ◽

Toshihiko Umehara ◽

...

Keyword(s):

Sea Ice ◽

Acoustic Doppler Current Profiler ◽

Open Water ◽

Ice Thickness ◽

Dual Frequency ◽

Backscattering Coefficient ◽

Sea Ice Thickness ◽

National Space ◽

L Band ◽

Airborne Sar

AbstractDual-frequency, multi-polarization airborne synthetic aperture radar (Pi-SAR; developed by the Communications Research Laboratory and National Space Development Agency of Japan) observations of the seasonal sea-ice region off the Okhotsk coast of Hokkaido, Japan, were carried out in February 1999 using X- and L-band radar frequencies with a resolution of 1.5 and 3.0 m. In conjunction with the SAR observations, the sea-ice thickness (draft) and velocity were measured by a moored Ice Profiling Sonar (IPS) and an Acoustic Doppler Current Profiler (ADCP). Tracks of the sea ice passing over the IPS were estimated from the time series of the ADCP ice-velocity and -direction data. Along these tracks, the SAR backscattering coefficient profiles were compared with the IPS ice-draft profiles. The results showed that the L-band SAR backs cattering profiles correlated well with the IPS ice-draft data, particularly in the thicker part (a few meters thick) of the rim of first-year ice, which had a large backscattering coefficient. Although the X-band SAR backscattering profiles did not correlate well with the IPS data, thin ice (<10 cm thick) showed a large backscattering coefficient. The L-band SAR and IPS data did not distinguish thin ice from open water.

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