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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2020
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Online Access: | https://doi.org/10.3390/rs12162623 https://doaj.org/article/8c4923817687488fa854caa155acb32d |
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ftdoajarticles:oai:doaj.org/article:8c4923817687488fa854caa155acb32d 2023-05-15T14:52:00+02:00 Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery Marcel König Gerit Birnbaum Natascha Oppelt 2020-08-01T00:00:00Z https://doi.org/10.3390/rs12162623 https://doaj.org/article/8c4923817687488fa854caa155acb32d EN eng MDPI AG https://www.mdpi.com/2072-4292/12/16/2623 https://doaj.org/toc/2072-4292 doi:10.3390/rs12162623 2072-4292 https://doaj.org/article/8c4923817687488fa854caa155acb32d Remote Sensing, Vol 12, Iss 2623, p 2623 (2020) hyperspectral atmospheric correction melt ponds sea ice Arctic bathymetry Science Q article 2020 ftdoajarticles https://doi.org/10.3390/rs12162623 2022-12-31T10:20:00Z 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 ( <math display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math> ) (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 ( <math display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math> < 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated ( <math display="inline"><semantics><mi>r</mi></semantics></math> = 0.86) and accurate ( <math display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math> = 4.04 cm), and the model explains a large portion of the variability ( <math display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math> = 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne ... Article in Journal/Newspaper Arctic Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Remote Sensing 12 16 2623 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
hyperspectral atmospheric correction melt ponds sea ice Arctic bathymetry Science Q |
spellingShingle |
hyperspectral atmospheric correction melt ponds sea ice Arctic bathymetry Science Q 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 Science Q |
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 ( <math display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math> ) (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 ( <math display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math> < 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated ( <math display="inline"><semantics><mi>r</mi></semantics></math> = 0.86) and accurate ( <math display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math> = 4.04 cm), and the model explains a large portion of the variability ( <math display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math> = 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne ... |
format |
Article in Journal/Newspaper |
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 |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12162623 https://doaj.org/article/8c4923817687488fa854caa155acb32d |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Remote Sensing, Vol 12, Iss 2623, p 2623 (2020) |
op_relation |
https://www.mdpi.com/2072-4292/12/16/2623 https://doaj.org/toc/2072-4292 doi:10.3390/rs12162623 2072-4292 https://doaj.org/article/8c4923817687488fa854caa155acb32d |
op_doi |
https://doi.org/10.3390/rs12162623 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
16 |
container_start_page |
2623 |
_version_ |
1766323130494615552 |