Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery
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 (Atmosp...
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Online Access: | https://doi.org/10.3390/rs12162623 |
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ftmdpi:oai:mdpi.com:/2072-4292/12/16/2623/ 2023-10-09T21:48:17+02:00 Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery Marcel König Gerit Birnbaum Natascha Oppelt agris 2020-08-14 application/pdf https://doi.org/10.3390/rs12162623 eng eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs12162623 https://creativecommons.org/licenses/by/4.0/ Remote Sensing Volume 12 Issue 16 Pages: 2623 hyperspectral atmospheric correction melt ponds sea ice Arctic bathymetry Text 2020 ftmdpi https://doi.org/10.3390/rs12162623 2023-09-10T23:53:32Z 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. Text Arctic Sea ice MDPI Open Access Publishing Arctic Remote Sensing 12 16 2623 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
hyperspectral atmospheric correction melt ponds sea ice Arctic bathymetry |
spellingShingle |
hyperspectral atmospheric correction melt ponds sea ice Arctic bathymetry Marcel König Gerit Birnbaum Natascha Oppelt Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery |
topic_facet |
hyperspectral atmospheric correction melt ponds sea ice Arctic bathymetry |
description |
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. |
format |
Text |
author |
Marcel König Gerit Birnbaum Natascha Oppelt |
author_facet |
Marcel König Gerit Birnbaum Natascha Oppelt |
author_sort |
Marcel König |
title |
Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery |
title_short |
Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery |
title_full |
Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery |
title_fullStr |
Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery |
title_full_unstemmed |
Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery |
title_sort |
mapping the bathymetry of melt ponds on arctic sea ice using hyperspectral imagery |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12162623 |
op_coverage |
agris |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Remote Sensing Volume 12 Issue 16 Pages: 2623 |
op_relation |
https://dx.doi.org/10.3390/rs12162623 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs12162623 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
16 |
container_start_page |
2623 |
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1779311341203357696 |