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

2011 ◽  
Vol 52 (57) ◽  
pp. 177-184 ◽  
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.

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

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.

scholarly journals Retrieval of sea ice thickness distribution in the seasonal ice zone from airborne L‐band SAR

2009 ◽  
Vol 30 (12) ◽  
pp. 3171-3189 ◽  
Author(s):  
T. Toyota ◽  
K. Nakamura ◽  
S. Uto ◽  
K. I. Ohshima ◽  
N. Ebuchi
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
L Band ◽  
Seasonal Ice Zone

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

2020 ◽  
Vol 14 (2) ◽  
pp. 461-476 ◽  
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.

scholarly journals Interannual sea ice thickness variability in the Bay of Bothnia

2018 ◽  
Vol 12 (11) ◽  
pp. 3459-3476 ◽  
Author(s):  
Iina Ronkainen ◽  
Jonni Lehtiranta ◽  
Mikko Lensu ◽  
Eero Rinne ◽  
Jari Haapala ◽  
...  
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Data Sets ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Thickness Variability ◽  
Fast Ice ◽  
Ridged Ice

Abstract. While variations of Baltic Sea ice extent and thickness have been extensively studied, there is little information about drift ice thickness, distribution, and its variability. In our study, we quantify the interannual variability of sea ice thickness in the Bay of Bothnia during the years 2003–2016. We use various different data sets: official ice charts, drilling data from the regular monitoring stations in the coastal fast ice zone, and helicopter and shipborne electromagnetic soundings. We analyze the different data sets and compare them to each other to characterize the interannual variability, to discuss the ratio of level and deformed ice, and to derive ice thickness distributions in the drift ice zone. In the fast ice zone the average ice thickness is 0.58±0.13 m. Deformed ice increases the variability of ice conditions in the drift ice zone, where the average ice thickness is 0.92±0.33 m. On average, the fraction of deformed ice is 50 % to 70 % of the total volume. In heavily ridged ice regions near the coast, mean ice thickness is approximately half a meter thicker than that of pure thermodynamically grown fast ice. Drift ice exhibits larger interannual variability than fast ice.

scholarly journals New insight from CryoSat-2 sea ice thickness for sea ice modelling

2018 ◽  
Author(s):  
David Schröder ◽  
Danny L. Feltham ◽  
Michel Tsamados ◽  
Andy Ridout ◽  
Rachel Tilling
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Atmospheric Conditions ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Melt Pond ◽  
The Impact

Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50 % of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

Sea ice deformation and thickness in the Western Ross Sea

2021 ◽  
Author(s):  
Wolfgang Rack ◽  
Daniel Price ◽  
Christian Haas ◽  
Patricia J. Langhorne ◽  
Greg H. Leonard
Keyword(s):  
Sea Ice ◽  
Satellite Images ◽  
Ross Sea ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Ice Dynamics ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
Terra Nova Bay ◽  
Western Ross Sea

<p>Sea ice cover is arguably the longest and best observed climate variable from space, with over four decades of highly reliable daily records of extent in both hemispheres. In Antarctica, a slight positive decadal trend in sea ice cover is driven by changes in the western Ross Sea, where a variation in weather patterns over the wider region forced a change in meridional winds. The distinguishing wind driven sea ice process in the western Ross Sea is the regular occurrence of the Ross Sea, McMurdo Sound, and Terra Nova Bay polynyas. Trends in sea ice volume and mass in this area unknown, because ice thickness and dynamics are particularly hard to measure.</p><p>Here we present the first comprehensive and direct assessment of large-scale sea-ice thickness distribution in the western Ross Sea. Using an airborne electromagnetic induction (AEM) ice thickness sensor towed by a fixed wing aircraft (Basler BT-67), we observed in November 2017 over a distance of 800 km significantly thicker ice than expected from thermodynamic growth alone. By means of time series of satellite images and wind data we relate the observed thickness distribution to satellite derived ice dynamics and wind data. Strong southerly winds with speeds of up to 25 ms<sup>-1</sup> in early October deformed the pack ice, which was surveyed more than a month later.</p><p>We found strongly deformed ice with a mean and maximum thickness of 2.0 and 15.6 m, respectively. Sea-ice thickness gradients are highest within 100-200 km of polynyas, where the mean thickness of the thickest 10% of ice is 7.6 m. From comparison with aerial photographs and satellite images we conclude that ice preferentially grows in deformational ridges; about 43% of the sea ice volume in the area between McMurdo Sound and Terra Nova Bay is concentrated in more than 3 m thick ridges which cover about 15% of the surveyed area. Overall, 80% of the ice was found to be heavily deformed and concentrated in ridges up to 11.8 m thick.</p><p>Our observations hold a link between wind driven ice dynamics and the ice mass exported from the western Ross Sea. The sea ice statistics highlighted in this contribution forms a basis for improved satellite derived mass balance assessments and the evaluation of sea ice simulations.</p>

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

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.

Estimating the thickness of ridged sea ice from ship observations in the Ross Sea

2003 ◽  
Vol 15 (1) ◽  
pp. 47-54 ◽  
Author(s):  
TINA TIN ◽  
MARTIN O. JEFFRIES ◽  
MIKKO LENSU ◽  
JUKKA TUHKURI
Keyword(s):  
Sea Ice ◽  
Ross Sea ◽  
Low Cost ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Equivalent Thickness ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
Using Data ◽  
Original Formula

Ship-based observations of sea ice thickness using the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol provide information on ice thickness distribution at relatively low cost. This protocol uses a simple formula to calculate the mass of ice in ridges based on surface observations. We present two new formulae and compare these with results from the “Original” formula using data obtained in the Ross Sea in autumn and winter. The new “r-star” formula uses a more realistic ratio of sail and keel areas to transform dimensions of sails to estimates of mean keel areas. As a result, estimates of “equivalent thickness” (i.e. mean thickness of ice in ridged areas) increased by over 200%. The new “Probability” formula goes one step further, by incorporating the probability that a sail is associated with a keel underwater, and the probability that keels may be found under level surfaces. This resulted in estimates of equivalent thickness comparable with the Original formula. Estimates of equivalent thickness at one or two degree latitude resolution are sufficiently accurate for validating sea ice models. Although ridges are small features in the Ross Sea, we have shown that they constitute a significant fraction of the total ice mass.

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

2010 ◽  
Vol 4 (4) ◽  
pp. 583-592 ◽  
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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