scholarly journals Sea-ice surface roughness estimates from airborne laser scanner and laser altimeter observations in Fram Strait and north of Svalbard

2015 ◽  
Vol 56 (69) ◽  
pp. 235-244 ◽  
Author(s):  
Justin F. Beckers ◽  
Angelika H.H. Renner ◽  
Gunnar Spreen ◽  
Sebastian Gerland ◽  
Christian Haas
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Laser Scanner ◽  
Ice Thickness ◽  
Fram Strait ◽  
Laser Altimeter ◽  
Airborne Laser Scanner ◽  
Ice Surface ◽  
Airborne Laser ◽  
Ice Surface Roughness

AbstractWe present sea-ice surface roughness estimates, i.e. the standard deviation of relative surface elevation, in the Arctic regions of Fram Strait and the Nansen Basin north of Svalbard acquired by an airborne laser scanner and a single-beam laser altimeter in 2010. We compare the scanner to the altimeter and compare the differences between the two survey regions. We estimate and correct sensor roll from the scanner data using the hyperbolic response of the scanner over a flat surface. Measurement surveys had to be longer than 5 km north of Svalbard and longer than 15 km in Fram Strait before the statistical distribution in surface roughness from the scanner and altimeter became similar. The shape of the surface roughness probability distributions agrees with those of airborne electromagnetic induction measurements of ice thickness. The ice in Fram Strait had a greater mean surface roughness, 0.16 m vs 0.09 m, and a wider distribution in roughness values than the ice in the Nansen Basin. An increase in surface roughness with increasing ice thickness was observed over fast ice found in Fram Strait near the coast of Greenland but not for the drift ice.

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 Estimation of Level and Deformed First-Year Sea Ice Surface Roughness in the Canadian Arctic Archipelago from C- and L-Band Synthetic Aperture Radar

2019 ◽  
Vol 45 (3-4) ◽  
pp. 457-475 ◽  
Author(s):  
Silvie Marie Cafarella ◽  
Randall Scharien ◽  
Torsten Geldsetzer ◽  
Stephen Howell ◽  
Christian Haas ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Synthetic Aperture Radar ◽  
Canadian Arctic ◽  
Synthetic Aperture ◽  
First Year ◽  
Canadian Arctic Archipelago ◽  
Ice Surface ◽  
L Band ◽  
Ice Surface Roughness

scholarly journals C-Band Polarimetric Coherences and Ratios for Discriminating Sea Ice Roughness

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Mukesh Gupta ◽  
Randall K. Scharien ◽  
David G. Barber
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Two Dimensions ◽  
The Arctic ◽  
Rapid Decline ◽  
Ice Surface ◽  
Ice Roughness ◽  
Direct Measurements ◽  
Ice Surface Roughness ◽  
Spaceborne Radar

The rapid decline of sea ice in the Arctic has resulted in a variable sea ice roughness that necessitates improved methods for efficient observation using high-resolution spaceborne radar. The utility of C-band polarimetric backscatter, coherences, and ratios as a discriminator of ice surface roughness is evaluated. An existing one-dimensional backscatter model has been modified to two-dimensions (2D) by considering deviation in the orientation (i.e., the slopes) in azimuth and range direction of surface roughness simultaneously as an improvement in the model. It is shown theoretically that the circular coherence (ρRRLL) decreases exponentially with increasing surface roughness. The crosspolarized coherence (ρHHVH) is found to be less sensitive to surface roughness, whereas the copolarized coherence (ρVVHH) decreases at far-range incidence angles for all ice types. A complete validation of the adapted 2D model using direct measurements of surface roughness is suggested as an avenue for further research.

Mapping Arctic Sea Ice Surface Roughness with Multi-angle Imaging SpectroRadiometry

2020 ◽  
Author(s):  
Thomas Johnson ◽  
Michel Tsamados ◽  
Jan-Peter Muller ◽  
Julienne Stroeve
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Distribution Functions ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
Training Dataset ◽  
Support Vector ◽  
Laser Measurements ◽  
Ice Surface ◽  
Ice Surface Roughness

<div> <div> <p>Surface roughness is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer melt pond extent, while also closely related to ice age. High resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness have remained elusive and do not extended over multi-decadal time-scales.  The MISR (Multi-angle Imaging SpectroRadiometer) instrument acquires optical imagery at 275m (red channel) and 1.1 km (all channels) resolutions from nine near-simultaneous camera view zenith angles sampling specular anisotropy, since 1999. Extending on previous work to model sea ice surface roughness from MISR angular reflectance signatures, a training dataset of cloud-free pixels and coincident probability distribution functions of lidar derived elevations from the Airborne Topographic Mapper (ATM) is generated. Surface roughness, defined as the standard deviation of the within-pixel elevations to a best-fit plane, is modelled using Support Vector Regression with a Radial Basis Function kernel, hyperparameters are tuned using GridSearchCV, and performance is assessed using nested cross-validation. We present derived instantaneous and monthly averaged sea ice roughness products at 1.1km and 17.6km resolution over the timespan of IceBridge campaigns (March and April for 2009-2018) on an EASE-2 (Equal-Area Scalable Earth) grid. These show considerable promise in detecting newly formed smooth ice from polynyas, and detailed surface features such as ridges and leads.</p> </div> </div><div> </div>

Late Summer Arctic Sea Ice Surface Roughness Signatures in C-Band SAR Data

2016 ◽  
Vol 9 (3) ◽  
pp. 1199-1215 ◽  
Author(s):  
Ane S. Fors ◽  
Camilla Brekke ◽  
Sebastian Gerland ◽  
Anthony P. Doulgeris ◽  
Justin F. Beckers
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Late Summer ◽  
Arctic Sea Ice ◽  
Ice Surface ◽  
Arctic Sea ◽  
Sar Data ◽  
Ice Surface Roughness

scholarly journals Retrieval of the thickness of undeformed sea ice from simulated C-band compact polarimetric SAR images

2016 ◽  
Vol 10 (4) ◽  
pp. 1529-1545 ◽  
Author(s):  
Xi Zhang ◽  
Wolfgang Dierking ◽  
Jie Zhang ◽  
Junmin Meng ◽  
Haitao Lang
Keyword(s):  
Sea Ice ◽  
Incidence Angle ◽  
Ice Thickness ◽  
First Year ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Ice Surface ◽  
Ice Conditions ◽  
Level Ice ◽  
Ice Surface Roughness

Abstract. In this paper we introduce a parameter for the retrieval of the thickness of undeformed first-year sea ice that is specifically adapted to compact polarimetric (CP) synthetic aperture radar (SAR) images. The parameter is denoted as the "CP ratio". In model simulations we investigated the sensitivity of the CP ratio to the dielectric constant, ice thickness, ice surface roughness, and radar incidence angle. From the results of the simulations we deduced optimal sea ice conditions and radar incidence angles for the ice thickness retrieval. C-band SAR data acquired over the Labrador Sea in circular transmit and linear receive (CTLR) mode were generated from RADARSAT-2 quad-polarization images. In comparison with results from helicopter-borne measurements, we tested different empirical equations for the retrieval of ice thickness. An exponential fit between the CP ratio and ice thickness provides the most reliable results. Based on a validation using other compact polarimetric SAR images from the same region, we found a root mean square (rms) error of 8 cm and a maximum correlation coefficient of 0.94 for the retrieval procedure when applying it to level ice between 0.1 and 0.8 m thick.

scholarly journals Parameterization of Centimeter-Scale Sea Ice Surface Roughness Using Terrestrial LiDAR

2015 ◽  
Vol 53 (3) ◽  
pp. 1271-1286 ◽  
Author(s):  
Jack C. Landy ◽  
Dustin Isleifson ◽  
Alexander S. Komarov ◽  
David G. Barber
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Terrestrial Lidar ◽  
Ice Surface ◽  
Ice Surface Roughness

scholarly journals Arctic Sea Ice Surface Roughness Derived from Multi-Angular Reflectance Satellite Imagery

2018 ◽  
Author(s):  
Anne W. Nolin
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
Ice Surface ◽  
Ice Roughness ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Angular Reflectance ◽  
Ice Surface Roughness

Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer melt ponds, and ice albedo. We have developed a method for mapping sea ice surface roughness using angular reflectance data from the Multi-angle Imaging SpectroRadiometer (MISR) and lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). Using an empirical data modeling approach, we derived estimates of Arctic sea ice roughness ranging from centimeters to decimeters meters within the MISR 275-m pixel size. Using independent ATM data for validation, we find that histograms of lidar and multi-angular roughness values are nearly identical for areas with roughness <20 cm but that for rougher regions, the MISR-derived roughness has a narrower range of values than the ATM data. The algorithm is able to accurately identify areas that transition between smooth and rough ice. Because of its coarser spatial scale, MISR-derived roughness data have a variance of about half that ATM roughness data.

Numerical and Experimental Evaluation of Terrestrial LiDAR for Parameterizing Centimeter-Scale Sea Ice Surface Roughness

2015 ◽  
Vol 53 (9) ◽  
pp. 4887-4898 ◽  
Author(s):  
Jack C. Landy ◽  
Alexander S. Komarov ◽  
David G. Barber
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Experimental Evaluation ◽  
Terrestrial Lidar ◽  
Ice Surface ◽  
Ice Surface Roughness

scholarly journals The Best of Both Worlds: Connecting Remote Sensing and Arctic Communities for Safe Sea Ice Travel

ARCTIC ◽  
2021 ◽  
Vol 73 (4) ◽  
pp. 461-484 ◽  
Author(s):  
Rebecca A. Segal ◽  
Randall K. Scharien ◽  
Frank Duerden ◽  
Chui-Ling Tam
Keyword(s):  
Remote Sensing ◽  
Surface Roughness ◽  
Sea Ice ◽  
Image Data ◽  
Community Members ◽  
Edge Information ◽  
Knowledge Based ◽  
Ice Surface ◽  
Inuit Knowledge ◽  
Ice Surface Roughness

  Northern communities are increasingly interested in technology that provides information about the sea ice environment for travel purposes. Synthetic aperture radar (SAR) remote sensing is widely used to observe sea ice independently of sunlight and cloud cover, however, access to SAR in northern communities has been limited. This study 1) defines the sea ice features that influence travel for two communities in the Western Canadian Arctic, 2) identifies the utility of SAR for enhancing mobility and safety while traversing environments with these features, and 3) describes methods for sharing SAR-based maps. Three field seasons (spring and fall 2017 and spring 2018) were used to engage residents in locally guided research, where applied outputs were evaluated by community members. We found that SAR image data inform and improve sea ice safety, trafficability, and education. Information from technology is desired to complement Inuit knowledge-based understanding of sea ice features, including surface roughness, thin sea ice, early and late season conditions, slush and water on sea ice, sea ice encountered by boats, and ice discontinuities. Floe edge information was not a priority. Sea ice surface roughness was identified as the main condition where benefits to trafficability from SAR-based mapping were regarded as substantial. Classified roughness maps are designed using thresholds representing domains of sea ice surface roughness (smooth ice/maniqtuk hiku, moderately rough ice/maniilrulik hiku, rough ice/maniittuq hiku; dialect is Inuinnaqtun). These maps show excellent agreement with local observations. Overall, SAR-based maps tailored for on-ice use are beneficial for and desired by northern community residents, and we recommend that high-resolution products be routinely made available in communities.

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