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    <titleInfo>
        <title>Advancement in Bedfast Lake ICE Mapping From Sentinel-1 Sar Data</title>
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    <name type="personal">
        <namePart type="given">Claude</namePart>
        <namePart type="family">Duguay</namePart>
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    <name type="personal">
        <namePart type="given">Junqian</namePart>
        <namePart type="family">Wang</namePart>
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    <abstract>Algorithms for the generation of a bedfast/floating lake ice product from Sentinel-1A/B synthetic aperture radar (SAR) data were implemented, cross-compared, and validated for various permafrost regions (Alaska, Canada and Russia). The algorithms consisted of: 1) thresholding; 2) Iteration Region Growing with Semantics (IRGS); and 3) K-means. The thresholding algorithm (92.4%) was found to perform slightly better on average than the IRGS algorithm (90.1%), and to outperform K-means (85.3%). The thresholding algorithm was therefore selected for implementation of a processing chain to generate a novel bedfast/floating lake ice product. Using a time series of Sentinel-1 SAR data, the new map product shows the day of year (DOY) when the ice becomes bedfast or remains afloat for individual lake sections.</abstract>
    <identifier type="citekey">Duguay-2019-Advancement</identifier>
    <identifier type="doi">10.1109/igarss.2019.8900650</identifier>
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        <url>https://gwf-uwaterloo.github.io/gwf-publications/G19-53001</url>
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    <part>
        <date>2019</date>
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%0 Journal Article
%T Advancement in Bedfast Lake ICE Mapping From Sentinel-1 Sar Data
%A Duguay, Claude
%A Wang, Junqian
%J IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
%D 2019
%I IEEE
%F Duguay-2019-Advancement
%X Algorithms for the generation of a bedfast/floating lake ice product from Sentinel-1A/B synthetic aperture radar (SAR) data were implemented, cross-compared, and validated for various permafrost regions (Alaska, Canada and Russia). The algorithms consisted of: 1) thresholding; 2) Iteration Region Growing with Semantics (IRGS); and 3) K-means. The thresholding algorithm (92.4%) was found to perform slightly better on average than the IRGS algorithm (90.1%), and to outperform K-means (85.3%). The thresholding algorithm was therefore selected for implementation of a processing chain to generate a novel bedfast/floating lake ice product. Using a time series of Sentinel-1 SAR data, the new map product shows the day of year (DOY) when the ice becomes bedfast or remains afloat for individual lake sections.
%R 10.1109/igarss.2019.8900650
%U https://gwf-uwaterloo.github.io/gwf-publications/G19-53001
%U https://doi.org/10.1109/igarss.2019.8900650

Markdown (Informal)

[Advancement in Bedfast Lake ICE Mapping From Sentinel-1 Sar Data](https://gwf-uwaterloo.github.io/gwf-publications/G19-53001) (Duguay & Wang, GWF 2019)

• Advancement in Bedfast Lake ICE Mapping From Sentinel-1 Sar Data (Duguay & Wang, GWF 2019)

ACL

• Claude Duguay and Junqian Wang. 2019. Advancement in Bedfast Lake ICE Mapping From Sentinel-1 Sar Data. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.