Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network
Melt ponds on sea ice strongly reduce the surface albedo and accelerate the decay of Arctic sea ice. Due to different spectral properties of snow, ice, and water, the fractional coverage of these distinct surface types can be derived from multispectral sensors like the Moderate Resolution Image Spec...
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Copernicus Publications
2012
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00025969 2023-05-15T13:11:19+02:00 Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network Rösel, A. Kaleschke, L. Birnbaum, G. 2012-04 electronic https://doi.org/10.5194/tc-6-431-2012 https://noa.gwlb.de/receive/cop_mods_00025969 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00025924/tc-6-431-2012.pdf https://tc.copernicus.org/articles/6/431/2012/tc-6-431-2012.pdf eng eng Copernicus Publications The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-6-431-2012 https://noa.gwlb.de/receive/cop_mods_00025969 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00025924/tc-6-431-2012.pdf https://tc.copernicus.org/articles/6/431/2012/tc-6-431-2012.pdf uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2012 ftnonlinearchiv https://doi.org/10.5194/tc-6-431-2012 2022-02-08T22:49:27Z Melt ponds on sea ice strongly reduce the surface albedo and accelerate the decay of Arctic sea ice. Due to different spectral properties of snow, ice, and water, the fractional coverage of these distinct surface types can be derived from multispectral sensors like the Moderate Resolution Image Spectroradiometer (MODIS) using a spectral unmixing algorithm. The unmixing was implemented using a multilayer perceptron to reduce computational costs. Arctic-wide melt pond fractions and sea ice concentrations are derived from the level 3 MODIS surface reflectance product. The validation of the MODIS melt pond data set was conducted with aerial photos from the MELTEX campaign 2008 in the Beaufort Sea, data sets from the National Snow and Ice Data Center (NSIDC) for 2000 and 2001 from four sites spread over the entire Arctic, and with ship observations from the trans-Arctic HOTRAX cruise in 2005. The root-mean-square errors range from 3.8 % for the comparison with HOTRAX data, over 10.7 % for the comparison with NSIDC data, to 10.3 % and 11.4 % for the comparison with MELTEX data, with coefficient of determination ranging from R2=0.28 to R2=0.45. The mean annual cycle of the melt pond fraction per grid cell for the entire Arctic shows a strong increase in June, reaching a maximum of 15 % by the end of June. The zonal mean of melt pond fractions indicates a dependence of the temporal development of melt ponds on the geographical latitude, and has its maximum in mid-July at latitudes between 80° and 88° N. Furthermore, the MODIS results are used to estimate the influence of melt ponds on retrievals of sea ice concentrations from passive microwave data. Results from a case study comparing sea ice concentrations from ARTIST Sea Ice-, NASA Team 2-, and Bootstrap-algorithms with MODIS sea ice concentrations indicate an underestimation of around 40 % for sea ice concentrations retrieved with microwave algorithms. Article in Journal/Newspaper albedo Arctic Beaufort Sea National Snow and Ice Data Center Sea ice The Cryosphere Niedersächsisches Online-Archiv NOA Arctic The Cryosphere 6 2 431 446 |
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English |
topic |
article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Rösel, A. Kaleschke, L. Birnbaum, G. Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network |
topic_facet |
article Verlagsveröffentlichung |
description |
Melt ponds on sea ice strongly reduce the surface albedo and accelerate the decay of Arctic sea ice. Due to different spectral properties of snow, ice, and water, the fractional coverage of these distinct surface types can be derived from multispectral sensors like the Moderate Resolution Image Spectroradiometer (MODIS) using a spectral unmixing algorithm. The unmixing was implemented using a multilayer perceptron to reduce computational costs. Arctic-wide melt pond fractions and sea ice concentrations are derived from the level 3 MODIS surface reflectance product. The validation of the MODIS melt pond data set was conducted with aerial photos from the MELTEX campaign 2008 in the Beaufort Sea, data sets from the National Snow and Ice Data Center (NSIDC) for 2000 and 2001 from four sites spread over the entire Arctic, and with ship observations from the trans-Arctic HOTRAX cruise in 2005. The root-mean-square errors range from 3.8 % for the comparison with HOTRAX data, over 10.7 % for the comparison with NSIDC data, to 10.3 % and 11.4 % for the comparison with MELTEX data, with coefficient of determination ranging from R2=0.28 to R2=0.45. The mean annual cycle of the melt pond fraction per grid cell for the entire Arctic shows a strong increase in June, reaching a maximum of 15 % by the end of June. The zonal mean of melt pond fractions indicates a dependence of the temporal development of melt ponds on the geographical latitude, and has its maximum in mid-July at latitudes between 80° and 88° N. Furthermore, the MODIS results are used to estimate the influence of melt ponds on retrievals of sea ice concentrations from passive microwave data. Results from a case study comparing sea ice concentrations from ARTIST Sea Ice-, NASA Team 2-, and Bootstrap-algorithms with MODIS sea ice concentrations indicate an underestimation of around 40 % for sea ice concentrations retrieved with microwave algorithms. |
format |
Article in Journal/Newspaper |
author |
Rösel, A. Kaleschke, L. Birnbaum, G. |
author_facet |
Rösel, A. Kaleschke, L. Birnbaum, G. |
author_sort |
Rösel, A. |
title |
Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network |
title_short |
Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network |
title_full |
Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network |
title_fullStr |
Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network |
title_full_unstemmed |
Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network |
title_sort |
melt ponds on arctic sea ice determined from modis satellite data using an artificial neural network |
publisher |
Copernicus Publications |
publishDate |
2012 |
url |
https://doi.org/10.5194/tc-6-431-2012 https://noa.gwlb.de/receive/cop_mods_00025969 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00025924/tc-6-431-2012.pdf https://tc.copernicus.org/articles/6/431/2012/tc-6-431-2012.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
albedo Arctic Beaufort Sea National Snow and Ice Data Center Sea ice The Cryosphere |
genre_facet |
albedo Arctic Beaufort Sea National Snow and Ice Data Center Sea ice The Cryosphere |
op_relation |
The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-6-431-2012 https://noa.gwlb.de/receive/cop_mods_00025969 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00025924/tc-6-431-2012.pdf https://tc.copernicus.org/articles/6/431/2012/tc-6-431-2012.pdf |
op_rights |
uneingeschränkt info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5194/tc-6-431-2012 |
container_title |
The Cryosphere |
container_volume |
6 |
container_issue |
2 |
container_start_page |
431 |
op_container_end_page |
446 |
_version_ |
1766246894184431616 |