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Open AccessArticle

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

Earth Observation and Modelling, Department of Geography, Kiel University, Ludewig-Meyn-Str. 14, 24098 Kiel, Germany
Alfred Wegener Institute, Klußmannstr. 3d, 27570 Bremerhaven, Germany
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(16), 2623;
Received: 2 July 2020 / Revised: 7 August 2020 / Accepted: 11 August 2020 / Published: 14 August 2020
(This article belongs to the Special Issue Remote Sensing in Sea Ice)
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. View Full-Text
Keywords: hyperspectral; atmospheric correction; melt ponds; sea ice; Arctic; bathymetry hyperspectral; atmospheric correction; melt ponds; sea ice; Arctic; bathymetry
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MDPI and ACS Style

König, M.; Birnbaum, G.; Oppelt, N. Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery. Remote Sens. 2020, 12, 2623.

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