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...
| Published in: | Remote Sensing |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
MDPI AG
2020
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| Subjects: | |
| Online Access: | https://doi.org/10.3390/rs12162623 https://doaj.org/article/8c4923817687488fa854caa155acb32d |
| Summary: | 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 ... |
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