A linear model to derive melt pond depth from hyperspectral data
Melt ponds are key elements in the energy balance of Arctic sea ice. Observing their temporal evolution is crucial for understanding melt processes and predicting sea ice evolution. Remote sensing is the only technique that enables large-scale observations of Arctic sea ice. However, monitoring vert...
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ftcopernicus:oai:publications.copernicus.org:tcd81426 2023-05-15T14:57:52+02:00 A linear model to derive melt pond depth from hyperspectral data König, Marcel Oppelt, Natascha 2019-12-09 application/pdf https://doi.org/10.5194/tc-2019-261 https://tc.copernicus.org/preprints/tc-2019-261/ eng eng doi:10.5194/tc-2019-261 https://tc.copernicus.org/preprints/tc-2019-261/ eISSN: 1994-0424 Text 2019 ftcopernicus https://doi.org/10.5194/tc-2019-261 2020-07-20T16:22:32Z Melt ponds are key elements in the energy balance of Arctic sea ice. Observing their temporal evolution is crucial for understanding melt processes and predicting sea ice evolution. Remote sensing is the only technique that enables large-scale observations of Arctic sea ice. However, monitoring vertical melt pond evolution in this way is challenging because most of the optical signal reflected by a pond is defined by the scattering characteristics of the underlying ice. Without knowing the influence of melt water on the reflected signal, the water depth cannot be determined. To solve the problem, we simulated the way melt water changes the reflected spectra of bare ice. We developed a model based on the slope of the log-scaled remote sensing reflectance at 710 nm. We validated the model using 49 in situ melt pond spectra and corresponding depths from ponds on dark and bright ice. Retrieved pond depths are precise ( RMSE = 2.81 cm) and highly correlated with in situ measurements ( r = 0.89; p = 4.34e−17). The model further explains a large portion of the variation in pond depth ( R 2 = 0.74). Our results indicate that pond depth is retrievable from optical data under clear sky conditions. This technique is potentially transferrable to hyperspectral remote sensors on UAVs, aircraft and satellites. Text Arctic Sea ice Copernicus Publications: E-Journals Arctic |
institution |
Open Polar |
collection |
Copernicus Publications: E-Journals |
op_collection_id |
ftcopernicus |
language |
English |
description |
Melt ponds are key elements in the energy balance of Arctic sea ice. Observing their temporal evolution is crucial for understanding melt processes and predicting sea ice evolution. Remote sensing is the only technique that enables large-scale observations of Arctic sea ice. However, monitoring vertical melt pond evolution in this way is challenging because most of the optical signal reflected by a pond is defined by the scattering characteristics of the underlying ice. Without knowing the influence of melt water on the reflected signal, the water depth cannot be determined. To solve the problem, we simulated the way melt water changes the reflected spectra of bare ice. We developed a model based on the slope of the log-scaled remote sensing reflectance at 710 nm. We validated the model using 49 in situ melt pond spectra and corresponding depths from ponds on dark and bright ice. Retrieved pond depths are precise ( RMSE = 2.81 cm) and highly correlated with in situ measurements ( r = 0.89; p = 4.34e−17). The model further explains a large portion of the variation in pond depth ( R 2 = 0.74). Our results indicate that pond depth is retrievable from optical data under clear sky conditions. This technique is potentially transferrable to hyperspectral remote sensors on UAVs, aircraft and satellites. |
format |
Text |
author |
König, Marcel Oppelt, Natascha |
spellingShingle |
König, Marcel Oppelt, Natascha A linear model to derive melt pond depth from hyperspectral data |
author_facet |
König, Marcel Oppelt, Natascha |
author_sort |
König, Marcel |
title |
A linear model to derive melt pond depth from hyperspectral data |
title_short |
A linear model to derive melt pond depth from hyperspectral data |
title_full |
A linear model to derive melt pond depth from hyperspectral data |
title_fullStr |
A linear model to derive melt pond depth from hyperspectral data |
title_full_unstemmed |
A linear model to derive melt pond depth from hyperspectral data |
title_sort |
linear model to derive melt pond depth from hyperspectral data |
publishDate |
2019 |
url |
https://doi.org/10.5194/tc-2019-261 https://tc.copernicus.org/preprints/tc-2019-261/ |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-2019-261 https://tc.copernicus.org/preprints/tc-2019-261/ |
op_doi |
https://doi.org/10.5194/tc-2019-261 |
_version_ |
1766329969936433152 |