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

Forest fires exert profound ecological and socioeconomic impacts globally. Characterizing their long-term effects and evolvingregimes requires consistent, high-resolution event-level 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. This dataset offers a valuable resource for quantifying fire impacts and advancing the understanding of contemporary and future fire regimes in global forests. The dataset is organized into seven “.zip” packages corresponding to compositing periods and is provide in 5° x 5°Sinusoidal grid tiles (666 tiles in global forests). Each 30 m tile (18,533 x 18,533 pixels) includes three raster-format GeoTIFF files: (1) fire patch ID, (2) burned year, and (3) QA level. Files follow the naming convention: “ScarID_ScarTM3DV03_GEEScarTMSinv02_Clean.A3000001.h[HH]v[VV].[FILE_TYPE].[PERIOD].tif”, where [HH] and [VV] denote the horizontal and vertical tile indices. [FILE_TYPE] corresponds to one of the three file types: “ScarID”, “ScarYear”, and “QualityFlag”, representing fire patch ID, burned year, and QA level, respectively. The actual year of fire is obtained by adding 1980 to “ScarYear”. Only QA levels 1 and 2 are included in the distributed dataset.