A Global 30 m Landsat-based Dataset of Forest Fire Patches (GlobMap FFP v1.0) from 1984 to 2022

Abstract. Forest fires exert profound ecological impacts globally. Characterizing their long-term effects and evolving regimes requires consistent, high-resolution fire records over extended periods. The Landsat archive provides a unique foundation for such efforts, offering fine spatial detail with globally coherent, multi-decadal observations. Yet, it remains challenging to generate a globally consistent, Landsat-based fire product with event-level characterization. Here we present a 30 m global forest fire patch dataset spanning 1984–2022, developed from the full Landsat archive to ensure comprehensive fire characterization. We first condensed multi-temporal burned signals from Landsat archive on Google Earth Engine (GEE) using a pixel-based image compositing approach. This approach also reduces noise from clouds and shadows while ensuring high computational efficiency using GEE. We then mapped burned area using artificial neural network modeling across global forests. Finally, we delineated individual fire patches through spatial–temporal clustering and extracted their key attributes. Across global forests, we identified a total of 11.97 million individual fire patches burning 7.3 Mha yr⁻¹over 1984–2022. Validation indicated omission errors ranging from 12.2 % to 36.8 % and commission errors ranging from 6.4 % to 23.2 % across diverse forest types. Intercomparison with existing Landsat products revealed strong agreement in annual burned area estimates and fire patch detection, with discrepancies mainly arising from within-fire delineation and small fire detection. This dataset offers a valuable resource for quantifying fire impacts and advancing the understanding of contemporary and future fire regimes in global forests.