A Harmonized Landsat–Sentinel‐2 Burned‐Area Product for the Pantanal (2014–2024), Based on Multitemporal Composites

Burned‐area information is essential for documenting fire activity and supporting environmental management in dynamic ecosystems such as the Pantanal wetland. This study presents AQM‐HLS, an 11‐year burned‐area dataset (2014–2024) derived from multisensor surface‐reflectance imagery from Landsat‐8/9 and Sentinel‐2. The dataset is generated using multitemporal NBR‐based compositing and an automated sampling procedure that integrates VIIRS active‐fire detections with morphological filtering to produce consistent training samples for Random Forest classification. The classification achieved high performance, with overall accuracy above 99.6% and Dice and CSI values exceeding 0.99. Spectral diagnostics indicate a clear separation between burned and unburned surfaces, particularly in the NIR and SWIR regions. The resulting 30‐m resolution annual and monthly maps capture the spatial and temporal patterns of fire occurrence across the Pantanal and provide a consistent time series suitable for climate, ecological and risk‐management applications. The dataset is openly available and can support the intercomparison of burned‐area products with lower spatial resolution, contribute to the development and improvement of algorithms based on medium‐ and low‐resolution sensors, support cross‐sensor calibration across different orbital platforms and inform regional studies on fire dynamics.