Summary Historical time series (2018-2025) from the Amazon fire monitoring dashboard, including minor updates to the methods. The Amazon dashboard data product uses satellite active fire detections to identify and track individual fire events in near-real time across most of South America (between 10°N–25°S and 85°W–30°W). The model classifies fires into four fire types (deforestation, forest, small clearing and agricultural, and savanna and grassland fires) and provides estimates of individual fire carbon emissions. Methods are described in Andela et al. (2022). Near-real time estimates are available at https://amzfire.servirglobal.net/, while this archive provides the historical time series. Methods The data archived here (v1.2) are identical to v1.1, with the addition of data for 2025. Versions 1.1 and 1.2 include several methodological updates relative to v1.0. Two updates relate to the use of VIIRS active-fire detections. First, VIIRS detections were updated from Collection 1 to Collection 2. Second, any full day of missing observations from either the NOAA-20 or Suomi NPP VIIRS instruments is now replaced using observations from the other instrument. This gap-filling approach reduces the impact of temporary instrument outages, such as those affecting Suomi NPP VIIRS during the 2024 fire season. Two additional updates relate to the emissions calculations. First, dry matter burned is now converted to carbon emissions using fire-type-specific emission factors instead of assuming a uniform 50% carbon content across all fire types. Second, as part of the Sense4Fire project (https://sense4fire.eu/), we provide daily gridded emissions estimates of dry matter (DM), C, CO₂, CO, and NOₓ at 0.1° spatial resolution. Emissions factors from Andrea (2019) were used for savanna and grassland fires as well as small clearing and agricultural fires. For forest and deforestation fires, emission factors were selected based on a review of the literature (Table 1). A recent study by de Laat et al. (2026) demonstrated good agreement between atmospheric CO concentrations simulated using the GFA-S4F emissions provided here and Sentinel-5P/TROPOMI CO observations. Table 1: Emissions factors (gram species per kg dry matter burned) used to calculate C, CO2, CO, and NOx emissions. Fire type / trace gas emissions C CO2 CO NOx Savanna and grassland 480 1656 69.2 2.5 Small clearing and agricultural 430 1431 76.2 2.4 Forest 480 1561 104.0 2.0 Deforestation 490 1641 95.5 1.7 Dataset description For full methodological details, see Andela et al. (2022). Tables 2–4 describe the contents of the fire event (polygon), active-fire detection (point), and gridded emissions datasets. Active-fire detections and associated dry matter burned estimates can be combined with the emission factors provided in Table 1 to derive daily trace gas emissions time series for species and regions of interest. Table 2: Description of fire event shapefile attributes. Attribute class Attribute Explanation / units Fire type classification Fire type (1) savanna and grassland, (2) small clearing andagriculture, (3) forest, and (4) deforestation fires Confidence (1) low, (2) moderate, and (3) high Fire Atlas Size Fire size in km2 Start day Day of new fire start as day of year (1-366) Duration Fire duration in days C Emissions Fire carbon emissions (ton C) Fire characterization Tree cover Average tree cover fraction within perimeter (%) Biomass Average biomass within fire perimeter (ton ha-1) Deforestation Fraction of 550 m grid cells with historicdeforestation (five years prior to fire) within fire perimeter (%) FRP Average fire radiative power (FRP) for all firedetections within fire perimeter (MW) Persistence Average fire persistence across 550 m grid cellswithin fire perimeter (days) Progression Average fire progression fraction across 550 mgrid cells within perimeter (%) Daytime Fraction of 1:30 pm detections (%) for all firedetections within fire perimeter Detections Total active fire detections within fire perimeter Table 3: Description of attributes in the active-fire detection shapefile. Attribute class Attribute Explanation / units VIIRS active fire detections FRP Fire radiative power (MW) DOY Day of year (1-366) Fire type classification Fire type (1) savanna and grassland, (2) small clearing and agriculture, (3) forest, and (4) deforestation fires Confidence (1) low, (2) moderate, and (3) high Emissions C Emissions Fire carbon emissions (ton C) associated with each active fire detection DM Emissions Dry matter burned (ton) associated with each active fire detection Table 4: Description of variables in the daily gridded (0.1° resolution) emissions NetCDF files. The daily emissions product provides estimates of dry matter (DM), C, CO₂, CO, and NOₓ emissions. For DM and CO, emissions are additionally partitioned by fire type. For the other species, fire-type-specific emissions can be derived by combining dry matter burned estimates with the trace-gas-specific emission factors provided in Table 1. Grid-cell values can be converted from units of kg species m⁻² s⁻¹ to total daily emissions per grid cell (kg species day⁻¹) by multiplying by the number of seconds per day and the grid-cell area provided in the NetCDF file. /ancill grid_cell_area /partitioned_DM_emissions Deforestation emissions Forest emissions Savanna and grassland emissions Small clearing and agricultural emissions /partitioned_CO_emissions Deforestation emissions Forest emissions Savanna and grassland emissions Small clearing and agricultural emissions /total_emissions DM emissions C emissions CO2 emissions CO emissions NOx emissions Results Despite the methodological updates outlined above, the updated dataset remains largely consistent with the original 2019–2020 dataset (Table 5). Table 5: Comparison of model versions (the original version from Andela et al. (2022) and v1.2 published here) for April–December 2019 over the region 0–25°S and 85°W–30°W. Note that v1.2 provides complete coverage for 2019, whereas the original dataset contained missing data due to incomplete NOAA-20 VIIRS active-fire detections at that time. Dataset Fire type Fire detections (x1,000) Mean fire radiative power (MW) Number of events (x1,000) Emissions (Tg C) Original Deforestation 756.65 15.15 24.24 99.18 Original Forest 637.58 12.73 5.28 85.46 Original Small clearing and agricultural 348.49 10.91 154.68 10.55 Original Savanna and grassland 1935.06 12.11 296.42 71.75 v1.2 Deforestation 742.64 14.81 24.02 97.18 v1.2 Forest 626.92 12.06 5.16 77.84 v1.2 Small clearing and agricultural 350.7 10.89 155.56 9.27 v1.2 Savanna and grassland 1877.16 11.89 299.17 70.55 Acknowledgements The Sense4Fire project is funded by ESA under ESA Contract Number: 4000134840/21/I-NB. References Andela, N., Morton, D.C., Schroeder, W., Chen, Y., Brando, P.M. and Randerson, J.T., 2022. Tracking and classifying Amazon fire events in near real time. Science advances, 8, eabd2713. https://doi.org/10.1126/sciadv.abd2713. Andreae, M.O., 2019. Emission of trace gases and aerosols from biomass burning–an updated assessment. Atmospheric Chemistry and Physics, 19, 8523-8546. https://doi.org/10.5194/acp-19-8523-2019. De Laat, A.T.J., Andela, N., Forkel, M., Huijnen, V., Kinalczyk, D. and Van Wees, D., 2026. Sentinel‐5p reveals unexplained large wildfire carbon emissions in the Amazon in 2024. Geophysical Research Letters, 53(5), p.e2025GL115123. https://doi.org/10.1029/2025GL115123