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<p>Leaf Area Index (LAI) is a fundamental parameter linking vegetation structure with surface energy and carbon exchange in Earth system models. However, the underestimation of LAI caused by snow cover remains a persistent limitation of existing satellite products. Here, we develop a global snow-free LAI dataset covering the years 1985–2020 at 500 m resolution. The method compiles over 2700 leaf lifespan records from the TRY plant trait database, spanning observations for numerous plant species, and aggregates them into plant functional type (PFT)-specific values. It then identifies snow-affected regions using MODIS data and applies a leaf-lifespan-based correction to PFT-specific LAI values. The physiologically constrained snow-free LAI effectively corrects the underestimation of LAI in snow-affected regions. Simulations with the Common Land Model indicate that snow-free LAI improves albedo simulations over snow-covered regions by better representing vegetation masking effects and reducing positive albedo bias. Additionally, the snow-free LAI increases net radiation and gross primary productivity, and reduces snow depth. This snow-free LAI dataset provides a reliable input for modeling vegetation-snow interactions in Earth system models, supporting more accurate simulations of surface energy, water and carbon dynamics.</p>

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Global Snow-free Leaf Area Index Dataset 1985–2020 for Earth System Modeling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月29日

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  • Wanyi Lin,
  • Hua Yuan,
  • Wenzong Dong,
  • Zhuo Liu,
  • Jiayi Xiang,
  • Yongjiu Dai

摘要

Leaf Area Index (LAI) is a fundamental parameter linking vegetation structure with surface energy and carbon exchange in Earth system models. However, the underestimation of LAI caused by snow cover remains a persistent limitation of existing satellite products. Here, we develop a global snow-free LAI dataset covering the years 1985–2020 at 500 m resolution. The method compiles over 2700 leaf lifespan records from the TRY plant trait database, spanning observations for numerous plant species, and aggregates them into plant functional type (PFT)-specific values. It then identifies snow-affected regions using MODIS data and applies a leaf-lifespan-based correction to PFT-specific LAI values. The physiologically constrained snow-free LAI effectively corrects the underestimation of LAI in snow-affected regions. Simulations with the Common Land Model indicate that snow-free LAI improves albedo simulations over snow-covered regions by better representing vegetation masking effects and reducing positive albedo bias. Additionally, the snow-free LAI increases net radiation and gross primary productivity, and reduces snow depth. This snow-free LAI dataset provides a reliable input for modeling vegetation-snow interactions in Earth system models, supporting more accurate simulations of surface energy, water and carbon dynamics.