Assessment of EOS Aqua AMSR-E Arctic Sea Ice Concentrations Using Landsat-7 and Airborne Microwave Imagery

Cavalieri, Donald J.; Markus, T.; Hall, Dorothy K.; Gasiewski, Albin J.; Klein, M.; Ivanoff, Alvaro

doi:10.1109/tgrs.2006.878445

Cited by 61 publications

(42 citation statements)

References 14 publications

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“…Evidence for a thin ice SIC bias have been found in a number of studies [28][29][30][31]. In the present study, emphasis is put on the occurrence and structure of the leads rather than onto a correct quantification of SIC in the lead itself.…”

Section: Discussionmentioning

confidence: 83%

Investigating High-Resolution AMSR2 Sea Ice Concentrations during the February 2013 Fracture Event in the Beaufort Sea

2014

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Leads with a length on the order of 1000 km occurred in the Beaufort Sea in February 2013. These leads can be observed in Moderate Resolution Imaging Spectroradiometer (MODIS) images under predominantly clear sky conditions. Sea ice concentrations (SIC) derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) using the Bootstrap (BST) algorithm fail to show the lead occurrences, as is visible in the MODIS images. In contrast, SIC derived from AMSR2 using the Arctic Radiation and Turbulence Interaction Study (ARTIST) sea ice algorithm (ASI) reveal the lead structure, due to the higher spatial resolution possible when using 89-GHz channel data. The ASI SIC are calculated from brightness temperatures interpolated on three different grids with resolutions of 3.125 km (ASI-3k), 6.25 km (ASI-6k) and 12.5 km (ASI-12k) to investigate the effect of the spatial resolution. Single-swath data is used to study the effect of temporal sampling in comparison to daily averages. For a region of interest in the Beaufort Sea, BST and ASI-3k show area-averaged SIC of 97% ± 0.7% and 93% ± 7.0%, respectively. For ASI-6k, the area-averaged SIC are similar to ASI-3k, while ASI-12k data show more agreement with BST. Visual comparison with MODIS True Color imagery exhibits good agreement with ASI-3k. In particular, ASI-3k are able to reproduce lead structure and size in the sea ice cover, which are not or are less visible in the other SIC data. The results will be valuable for selecting a SIC data product for studies of the interaction between ocean, ice, and atmosphere in the polar regions.Remote Sens. 2014, 6 3842

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Section: Discussionmentioning

confidence: 83%

Investigating High-Resolution AMSR2 Sea Ice Concentrations during the February 2013 Fracture Event in the Beaufort Sea

2014

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“…Extensive field work has been conducted in the Barrow area (e.g., George et al [13], Eicken et al [64], Druckenmiller et al [14]), including collection of snow depth data, laser profilometer data and other aerial observations as part of the NASA AMSR-E validation campaigns for sea ice products in March 2003 and 2006, AMSRIce03 and AMSRIce06 (see Herzfeld et al [31], Maslanik et al [63], Cavalieri et al [65], Sturmet al [66], Markus et al [67], Heinrichs et al [68], Rivas et al [69], Stroeve et al [70]). …”

Section: In-situ Observations and Related Data Analysis Resultant Fromentioning

confidence: 99%

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

et al. 2016

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Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line.

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“…in Spreen et al (2008), and visual/infrared (VIS / IR) earth-observation data, e.g. in Cavalieri et al (2006), have been made. The VIS / IR comparison requires clear-sky conditions or a reliable cloud mask.…”

Section: Discussionmentioning

confidence: 99%

“…All the compared algorithms used AMSR-E (Advanced Microwave Scanning Radiometer Earth Observing System) radiometer data as their input. In Cavalieri et al (2006) a comparison between Landsat-7-based ice concentrations and NASA Team 2 algorithm concentrations was made for some images, and RMSEs were in the range of 1-7 and biases in the range of 0-3. However, these results cannot directly be compared to our error measures because the comparisons have been made between different quantities.…”

Section: Discussionmentioning

confidence: 99%

A sea ice concentration estimation algorithm utilizing radiometer and SAR data

Karvonen

2014

The Cryosphere

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Abstract.We have studied the possibility of combining the high-resolution synthetic aperture radar (SAR) segmentation and ice concentration estimated by radiometer brightness temperatures. Here we present an algorithm for mapping a radiometer-based concentration value for each SAR segment. The concentrations are estimated by a multi-layer perceptron (MLP) neural network which has the AMSR-2 (Advanced Microwave Scanning Radiometer 2) polarization ratios and gradient ratios of four radiometer channels as its inputs. The results have been compared numerically to the gridded Finnish Meteorological Institute (FMI) ice chart concentrations and high-resolution AMSR-2 ASI (ARTIST Sea Ice) algorithm concentrations provided by the University of Hamburg and also visually to the AMSR-2 bootstrap algorithm concentrations, which are given in much coarser resolution. The differences when compared to FMI daily ice charts were on average small. When compared to ASI ice concentrations, the differences were a bit larger, but still small on average. According to our comparisons, the largest differences typically occur near the ice edge and sea-land boundary. The main advantage of combining radiometerbased ice concentration estimation and SAR segmentation seems to be a more precise estimation of the boundaries of different ice concentration zones.

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Assessment of EOS Aqua AMSR-E Arctic Sea Ice Concentrations Using Landsat-7 and Airborne Microwave Imagery

Cited by 61 publications

References 14 publications

Investigating High-Resolution AMSR2 Sea Ice Concentrations during the February 2013 Fracture Event in the Beaufort Sea

Investigating High-Resolution AMSR2 Sea Ice Concentrations during the February 2013 Fracture Event in the Beaufort Sea

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

A sea ice concentration estimation algorithm utilizing radiometer and SAR data

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