Reconstructed global monthly burned area maps from 1901 to 2020

作者：Zhixuan Guo, Wei Li, Philippe Ciais, Stephen Sitch, Guido R. van der Werf, Simon Bowring, Ana Bastos, Florent Mouillot, Jiaying He, Minxuan Sun, Lei Zhu, Xiaomeng Du, Nan Wang, Xiaomeng Huang · 发表于：Earth system science data · 年份：2025 · DOI：10.5194/essd-17-3599-2025 · 被引用次数：8 · 研究领域：Fire effects on ecosystems、Fire Detection and Safety Systems、Fire dynamics and safety research

Abstract. Fire is a key Earth system process, driving variability in the global carbon cycle through CO2 emissions into the atmosphere and subsequent CO2 uptake through vegetation recovery after fires. Global spatiotemporally consistent datasets on burned area have been available since the beginning of the satellite era in the 1980s, but they are sparse prior to that date. In this study, we reconstructed global monthly burned area at a resolution of 0.5° × 0.5° from 1901 to 2020 using machine learning models trained on satellite-based observations of burned area between 2003 and 2020, with the goal of reconstructing long-term burned area information to constrain historical fire simulations. We first conducted a classification model to separate grid cells with extreme (burned area ≥ the 90th percentile in a given region) or regular fires. We then trained separate regression models for grid cells with extreme or regular fires. Both the classification and regression models were trained on a satellite-based burned area product (FireCCI51), using explanatory variables related to climate, vegetation and human activities. The trained models can well reproduce the long-term spatial patterns (slopes = 0.70–1.28 and R2 = 0.69–0.98 spatially), inter-annual variability and seasonality of the satellite-based burned area observations. After applying the trained model to the historical period, the predicted annual global total burned area ranges from 3.46×106 to 4.58×106 km2 yr−1 over 1901–...