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Snow thickness estimation on first-year sea ice using microwave and optical remote sensing with melt modelling.

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Title: Snow thickness estimation on first-year sea ice using microwave and optical remote sensing with melt modelling.
Authors: Zheng, Jiacheng1, Geldsetzer, Torsten1, Yackel, John1 yackel@ucalgary.ca
Source: Remote Sensing of Environment. Sep2017, Vol. 199, p321-332. 12p.
Subjects: Snow cover, Oceanography, Aerospace telemetry, Remote-sensing images, Geophysical prospecting
Abstract: The snow cover on first-year sea ice plays a paramount role in thermodynamics by modulating sea ice ablation and accretion processes. However, meteoric accumulation and redistribution of snow on first-year sea ice is highly stochastic over space and time, which makes it a poorly understood parameter. In this study, a region of late-winter snow thickness on first-year sea ice in the Canadian Arctic Archipelago is estimated using time series spaceborne C-band microwave scatterometer and optical MODIS data and a simple snow melt model. Results show good correspondence between the modeled snow thickness and remotely sensed dates of melt onset and pond onset. The mean snowmelt duration for 20 study sites is 24.6 ± 1.2 days, and the estimated mean snow thickness is 14.7 ± 3.0 cm. The overall performance of the model reveals a RMSE of 4.0 cm and a mean bias of 0.3 cm. The methodology shows promise; particularly because it can easily be scaled up in order to estimate snow thickness on seasonal sea ice on a regional basis. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 124877636
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Snow thickness estimation on first-year sea ice using microwave and optical remote sensing with melt modelling.
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  Data: <searchLink fieldCode="AR" term="%22Zheng%2C+Jiacheng%22">Zheng, Jiacheng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Geldsetzer%2C+Torsten%22">Geldsetzer, Torsten</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yackel%2C+John%22">Yackel, John</searchLink><relatesTo>1</relatesTo><i> yackel@ucalgary.ca</i>
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing+of+Environment%22">Remote Sensing of Environment</searchLink>. Sep2017, Vol. 199, p321-332. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Snow+cover%22">Snow cover</searchLink><br /><searchLink fieldCode="DE" term="%22Oceanography%22">Oceanography</searchLink><br /><searchLink fieldCode="DE" term="%22Aerospace+telemetry%22">Aerospace telemetry</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22Geophysical+prospecting%22">Geophysical prospecting</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: The snow cover on first-year sea ice plays a paramount role in thermodynamics by modulating sea ice ablation and accretion processes. However, meteoric accumulation and redistribution of snow on first-year sea ice is highly stochastic over space and time, which makes it a poorly understood parameter. In this study, a region of late-winter snow thickness on first-year sea ice in the Canadian Arctic Archipelago is estimated using time series spaceborne C-band microwave scatterometer and optical MODIS data and a simple snow melt model. Results show good correspondence between the modeled snow thickness and remotely sensed dates of melt onset and pond onset. The mean snowmelt duration for 20 study sites is 24.6 ± 1.2 days, and the estimated mean snow thickness is 14.7 ± 3.0 cm. The overall performance of the model reveals a RMSE of 4.0 cm and a mean bias of 0.3 cm. The methodology shows promise; particularly because it can easily be scaled up in order to estimate snow thickness on seasonal sea ice on a regional basis. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.rse.2017.06.038
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      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 321
    Subjects:
      – SubjectFull: Snow cover
        Type: general
      – SubjectFull: Oceanography
        Type: general
      – SubjectFull: Aerospace telemetry
        Type: general
      – SubjectFull: Remote-sensing images
        Type: general
      – SubjectFull: Geophysical prospecting
        Type: general
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      – TitleFull: Snow thickness estimation on first-year sea ice using microwave and optical remote sensing with melt modelling.
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              M: 09
              Text: Sep2017
              Type: published
              Y: 2017
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              Value: 199
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