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<p>Frequent, large-scale wildfires threaten ecosystems and human livelihoods globally. To effectively quantify and attribute the antecedent conditions for wildfires, a thorough understanding of Earth system dynamics is imperative. In response, we introduce the SeasFire datacube, a meticulously curated spatiotemporal dataset tailored for global sub-seasonal to seasonal wildfire modeling via Earth observation. The SeasFire datacube consists of 59 variables including climate, vegetation, oceanic indices, and human factors. It offers 8-day temporal resolution, 0.25° spatial resolution, and covers the period from 2001 to 2021. We showcase the versatility of SeasFire for exploring the variability and seasonality of wildfire drivers, modeling causal links between ocean-climate teleconnections and wildfires, and predicting sub-seasonal wildfire patterns across multiple timescales with a Deep Learning model. We have publicly released the SeasFire datacube and appeal to Earth system scientists and Machine Learning practitioners to use it for an improved understanding and anticipation of wildfires.</p>

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SeasFire cube - a multivariate dataset for global wildfire modeling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月3日

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  • Ilektra Karasante,
  • Lazaro Alonso,
  • Ioannis Prapas,
  • Akanksha Ahuja,
  • Nuno Carvalhais,
  • Ioannis Papoutsis

摘要

Frequent, large-scale wildfires threaten ecosystems and human livelihoods globally. To effectively quantify and attribute the antecedent conditions for wildfires, a thorough understanding of Earth system dynamics is imperative. In response, we introduce the SeasFire datacube, a meticulously curated spatiotemporal dataset tailored for global sub-seasonal to seasonal wildfire modeling via Earth observation. The SeasFire datacube consists of 59 variables including climate, vegetation, oceanic indices, and human factors. It offers 8-day temporal resolution, 0.25° spatial resolution, and covers the period from 2001 to 2021. We showcase the versatility of SeasFire for exploring the variability and seasonality of wildfire drivers, modeling causal links between ocean-climate teleconnections and wildfires, and predicting sub-seasonal wildfire patterns across multiple timescales with a Deep Learning model. We have publicly released the SeasFire datacube and appeal to Earth system scientists and Machine Learning practitioners to use it for an improved understanding and anticipation of wildfires.