Estimation of melt pond fraction over high-concentration Arctic sea ice using AMSR-E passive microwave data

2016 ◽  
Vol 121 (9) ◽  
pp. 7056-7072 ◽  
Author(s):  
Yasuhiro Tanaka ◽  
Kazutaka Tateyama ◽  
Takao Kameda ◽  
Jennifer K. Hutchings
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Passive Microwave ◽  
High Concentration ◽  
Melt Pond ◽  
Arctic Sea ◽  
Microwave Data

scholarly journals Recent changes in the Arctic melt Season

2006 ◽  
Vol 44 ◽  
pp. 367-374 ◽  
Author(s):  
Julienne Stroeve ◽  
Thorsten Markus ◽  
Walter N. Meier ◽  
Jeff Miller
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Significant Trend ◽  
Passive Microwave ◽  
The Arctic ◽  
Regional Variability ◽  
Arctic Sea ◽  
Microwave Data

AbstractMelt-season duration, melt-onset and freeze-up dates are derived from Satellite passive microwave data and analyzed from 1979 to 2005 over Arctic Sea ice. Results indicate a Shift towards a longer melt Season, particularly north of Alaska and Siberia, corresponding to large retreats of Sea ice observed in these regions. Although there is large interannual and regional variability in the length of the melt Season, the Arctic is experiencing an overall lengthening of the melt Season at a rate of about 2 weeks decade–1. In fact, all regions in the Arctic (except for the central Arctic) have Statistically Significant (at the 99% level or higher) longer melt Seasons by >1 week decade–1. The central Arctic Shows a Statistically Significant trend (at the 98% level) of 5.4 days decade–1. In 2005 the Arctic experienced its longest melt Season, corresponding with the least amount of Sea ice Since 1979 and the warmest temperatures Since the 1880s. Overall, the length of the melt Season is inversely correlated with the lack of Sea ice Seen in September north of Alaska and Siberia, with a mean correlation of –0.8.

Derivation of melt pond coverage on Arctic sea ice using MODIS observations

2008 ◽  
Vol 112 (5) ◽  
pp. 2605-2614 ◽  
Author(s):  
Mark A. Tschudi ◽  
James A. Maslanik ◽  
Donald K. Perovich
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Melt Pond ◽  
Arctic Sea

scholarly journals Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery

2020 ◽  
Vol 12 (16) ◽  
pp. 2623 ◽  
Author(s):  
Marcel König ◽  
Gerit Birnbaum ◽  
Natascha Oppelt
Keyword(s):  
Sea Ice ◽  
Hyperspectral Imagery ◽  
Atmospheric Correction ◽  
Hyperspectral Data ◽  
Arctic Sea Ice ◽  
Atmospheric Conditions ◽  
Melt Pond ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Highly Correlated

Hyperspectral remote-sensing instruments on unmanned aerial vehicles (UAVs), aircraft and satellites offer new opportunities for sea ice observations. We present the first study using airborne hyperspectral imagery of Arctic sea ice and evaluate two atmospheric correction approaches (ATCOR-4 (Atmospheric and Topographic Correction version 4; v7.0.0) and empirical line calibration). We apply an existing, field data-based model to derive the depth of melt ponds, to airborne hyperspectral AisaEAGLE imagery and validate results with in situ measurements. ATCOR-4 results roughly match the shape of field spectra but overestimate reflectance resulting in high root-mean-square error (RMSE) (between 0.08 and 0.16). Noisy reflectance spectra may be attributed to the low flight altitude of 200 ft and Arctic atmospheric conditions. Empirical line calibration resulted in smooth, accurate spectra (RMSE < 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated (r = 0.86) and accurate (RMSE = 4.04 cm), and the model explains a large portion of the variability (R2 = 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne hyperspectral data is possible subject to accurate atmospheric correction. Furthermore, we see the necessity to improve existing approaches with Arctic-specific atmospheric profiles and aerosol models and/or by using multiple reference targets on the ground.

scholarly journals Snow Depth Retrieval on Arctic Sea Ice From Passive Microwave Radiometers—Improvements and Extensions to Multiyear Ice Using Lower Frequencies

2018 ◽  
Vol 123 (10) ◽  
pp. 7120-7138 ◽  
Author(s):  
Philip Rostosky ◽  
Gunnar Spreen ◽  
Sinead L. Farrell ◽  
Torben Frost ◽  
Georg Heygster ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
Passive Microwave ◽  
Arctic Sea ◽  
Microwave Radiometers ◽  
Multiyear Ice ◽  
Lower Frequencies

scholarly journals Comparison of different retrieval techniques for melt ponds on Arctic sea ice from Landsat and MODIS satellite data

2011 ◽  
Vol 52 (57) ◽  
pp. 185-191 ◽  
Author(s):  
Anja Rösel ◽  
Lars Kaleschke
Keyword(s):  
Sea Ice ◽  
Principal Component ◽  
Arctic Sea Ice ◽  
Additional Advantage ◽  
Melt Pond ◽  
Spectral Bands ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Very High Spatial Resolution ◽  
Landsat 7

AbstractMelt ponds are regularly observed on the surface of Arctic sea ice in late spring and summer. They strongly reduce the surface albedo and accelerate the decay of Actic sea ice. Until now, only a few studies have looked at the spatial extent of melt ponds on Arctic sea ice. Knowledge of the melt-pond distribution on the entire Arctic sea ice would provide a solid basis for the parameterization of melt ponds in existing sea-ice models. Due to the different spectral properties of snow, ice and water, a multispectral sensor such as Landsat 7 ETM+ is generally applicable for the analysis of distribution. an additional advantage of the ETM+ sensor is the very high spatial resolution (30 m). an algorithm based on a principal component analysis (PCA) of two spectral channels has been developed in order to determine the melt-pond fraction. PCA allows differentiation of melt ponds and other surface types such as snow, ice or water. Spectral bands 1 and 4 with central wavelengths at 480 and 770 nm, respectively, are used as they represent the differences in the spectral albedo of melt ponds. A Landsat 7 ETM+ scene from 19 July 2001 was analysed using PCA. the melt-pond fraction determined by the PCA method yields a different spatial distribution of the ponded areas from that developed by others. A MODIS subset from the same date and area is also analysed. the classification of MODIS data results in a higher melt-pond fraction than both Landsat classifications.

Percolation blockage: A process that enables melt pond formation on first year Arctic sea ice

2017 ◽  
Vol 122 (1) ◽  
pp. 413-440 ◽  
Author(s):  
Chris Polashenski ◽  
Kenneth M. Golden ◽  
Donald K. Perovich ◽  
Eric Skyllingstad ◽  
Alexandra Arnsten ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
First Year ◽  
Melt Pond ◽  
Arctic Sea
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