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<p>Boreal forest and Arctic tundra environments collectively hold the largest percentage of global terrestrial seasonal snow cover. Тhe <i>in-situ</i> snow measurement network is sparse and costly in these remote northern regions. Here, we complement existing snow depth monitoring in Arctic tundra and boreal forest by presenting an extensive (64°N–70°N) snow depth dataset and description of ground-based snow depth measurements collected during the NASA SnowEx Alaska intensive field campaign, March 7–16, 2023. We also report the accuracy of snow depth measurements in shallow boreal forest and Arctic tundra snowpack and share considerations in developing the consistent and repeatable snow depth data collection procedures. Snow depth measurements and technical validation described in this paper can serve as a robust product for testing snow remote sensing techniques, and for providing a reference dataset for climatological and hydrological studies.</p>

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Snow depth measurements from Arctic tundra and boreal forest collected during NASA SnowEx Alaska campaign

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月31日

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  • Svetlana L. Stuefer,
  • Katherine Hale,
  • Lora D. May,
  • Megan Mason,
  • Carrie Vuyovich,
  • Hans-Peter Marshall,
  • Dragos Vas,
  • Kelly Elder

摘要

Boreal forest and Arctic tundra environments collectively hold the largest percentage of global terrestrial seasonal snow cover. Тhe in-situ snow measurement network is sparse and costly in these remote northern regions. Here, we complement existing snow depth monitoring in Arctic tundra and boreal forest by presenting an extensive (64°N–70°N) snow depth dataset and description of ground-based snow depth measurements collected during the NASA SnowEx Alaska intensive field campaign, March 7–16, 2023. We also report the accuracy of snow depth measurements in shallow boreal forest and Arctic tundra snowpack and share considerations in developing the consistent and repeatable snow depth data collection procedures. Snow depth measurements and technical validation described in this paper can serve as a robust product for testing snow remote sensing techniques, and for providing a reference dataset for climatological and hydrological studies.