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Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter
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2022 (English)In: Remote Sensing, E-ISSN 2072-4292, Vol. 14, no 18Article in journal (Refereed) Published
Abstract [en]

The melt pond fraction (MPF) is an important geophysical parameter of climate and the surface energy budget, and many MPF datasets have been generated from satellite observations. However, the reliability of these datasets suffers from short temporal spans and data gaps. To improve the temporal span and spatiotemporal continuity, we generated a long-term spatiotemporally continuous MPF dataset for Arctic sea ice, which is called the Northeast Normal University-melt pond fraction (NENU-MPF), from Moderate Resolution Imaging Spectroradiometer (MODIS) data. First, the non-linear relationship between the MODIS reflectance/geometries and the MPF was constructed using a genetic algorithm optimized back-propagation neural network (GA-BPNN) model. Then, the data gaps were filled and smoothed using a statistical-based temporal filter. The results show that the GA-BPNN model can provide accurate estimations of the MPF (R2 = 0.76, root mean square error (RMSE) = 0.05) and that the data gaps can be efficiently filled by the statistical-based temporal filter (RMSE = 0.047; bias = −0.022). The newly generated NENU-MPF dataset is consistent with the validation data and with published MPF datasets. Moreover, it has a longer temporal span and is much more spatiotemporally continuous; thus, it improves our knowledge of the long-term dynamics of the MPF over Arctic sea ice surfaces.

Place, publisher, year, edition, pages
2022. Vol. 14, no 18
Keywords [en]
melt pond fraction, sea ice, Arctic, artificial neural network, statistical-based temporal filter, MODIS
National Category
Natural Sciences
Research subject
SWEDARCTIC 2005, Beringia 2005
Identifiers
URN: urn:nbn:se:polar:diva-8970DOI: 10.3390/rs14184538OAI: oai:DiVA.org:polar-8970DiVA, id: diva2:1726774
Available from: 2023-01-13 Created: 2023-01-13 Last updated: 2023-08-28Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • Other style
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  • de-DE
  • en-GB
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  • nn-NO
  • nn-NB
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