As part of the Sense4Fire project, fuel and fire emission products were made available for the Amazon/Cerrado, southern Africa, the eastern Sahel, southern Europe, and a test area in Siberia. The datasets involve the use of different input datasets for burnt area and different configurations of the GFA-S4F, TUD-S4F and KNMI-S5p approaches. Below you find an overview about the Sense4Fire datasets, and a list of all available output variables.
If you want to use the datasets and you are unsure which one to use, please get in contact with Matthias Forkel.
Browse the full folder structure of all datasets.
The Sense4Fire datasets were delivered in different versions of the database:
- Database version 4 (DBv4): Near-real time and high-resolution emissions for Africa. The fourth version of the Sense4Fire database provides advanced methodologies for two study regions in Africa, namely southern Africa and the Sahel. DBv4 provides refined setups of GFA-S4F and TUD-S4F to provide fire emission estimates with low latency, i.e. as provisional (PRV) analysis up to the previous year or as near-real time (NRT), here defined as providing fire emission estimates with a latency of <1 month. In addition, DBv4 includes an unprecedented high resolution fire emission dataset based on the TUD-S4F approach with a high spatial resolution (HR, 20 m). The datasets of DBv4 were published during 2026.
- Database version 3 (DBv3): In 2024, the Amazon experienced an exceptional extreme fire season. During this season, the Sense4Fire project provided updates of fire emissions from the GFA-S4F and TUD-S4F approaches. The purpose of DBv3 – published consecutively during the second half of 2024 – was to demonstrate the near-real time capabilities of the Sense4Fire approaches in providing estimates of fire emissions. Results are available for the Amazon/Cerrado study region for the year 2024 and were published in 2024 DBv3 forms the foundation of the results published in de Laat et al. (2026).
- Database version 2 (DBv2): The Sense4Fire baseline. The second version of the Sense4Fire Database was published in October 2023 with results for the Amazon/Cerrado, southern Africa, Europe, and Siberian study regions. For TUD-S4F, the products and factorial experiments in DBv2 are the baseline products for all study regions. The results for the Amazon/Cerrado study region in DBv2 are the results published in Forkel et al. (2025).
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Database version 1 (DBv1): Initial developments. The first version of the Sense4Fire Database was made available in May 2023 and provided the first products for the Amazon, southern Africa, Europe, and Siberian study regions. The KNMI-S5p dataset remains the same in version 02 of the Experimental Database. The GFA-S4F datasets for southern Africa, Siberia and Europe remain the same in version 02 of the database. The highly experimental TUD-S4F datasets in DBv1 are deprecated. Users should refer for TUD-S4F results in DBv2.
Amazon and Cerrado
The region is defined from 40°W - 80°W and 25°S - 10°N.
| Database | Approach and setup | Temporal coverage | Use/description in publication | Description |
| DBv30 | TUD-S4F-vNRT01 | 2024 | de Laat et al. 2026 | Initial near-real time setup based on a machine learning model trained against TUD-S4F-v02-S4Fba |
| DBv30 | GFA-S4F-v02 | 2024 | de Laat et al. 2026 | GFA-S4F with improved parametrization for NOx emissions, near real time update for 2024 |
| DBv20 | TUD-S4F-v02-S4Fba | 2020 | Forkel et al. 2025, ATBDv3, PVRv3 | Default TUD-S4F setup in DBv2 with ESA CCI land cover and GFA-S4F burnt area for the year 2020 (FireCCI51 for the other years), dynamic emission factors |
| DBv20 | TUD-S4F-v02-fixEF | 2020 | Forkel et al. 2025, ATBDv3, PVRv3 | Like TUD-S4F-v02-S4Fba but with fixed emission factors |
| DBv20 | TUD-S4F-v02-F51 | 2014-2021 | Forkel et al. 2025, ATBDv3, PVRv3 | Default TUD-S4F setup in DBv2 with ESA CCI land cover and FireCCI51 burnt area for 2014-2021, dynamic emission factors |
| DBv20 | GFA-S4F-v02 | 2019-2023 | Forkel et al. 2025, ATBDv3, PVRv3 | GFA-S4F with improved parametrization for NOx emissions and prolonged time series (2019-2023) |
| DBv10 | KNMI-S5p-v01 | 2020 | Forkel et al. 2025, ATBDv3, PVRv3 | Top-down estimates of CO and NOx emissions using the beta-method |
| DBv10 | GFA-S4F-v01 | 2020 | Andela et al. 2022, ATBDv3, PVRv3 | Original GFA-S4F approach based on Andela et al. (2022) |
Near-real time analysis of the Amazon 2024 fire season (Database version 3, DBv3)
The Amazon experienced an exceptional extreme fire season in 2024. During this season, the Sense4Fire project provided updates of fire emissions from the GFA-S4F and TUD-S4F approaches. The purpose of DBv3 – published consecutively during the second half of 2024 – was to demonstrate the near-real time capabilities of the Sense4Fire approaches in providing estimates of fire emissions. Results are available for the Amazon/Cerrado study region for the year 2024. DBv3 forms the foundation of the results published in de Laat et al. (2026).
Southern Africa
The region is defined from 10°E – 30°E and 5°S – 25°S.
| Database | Approach and setup | Temporal coverage | Use/description in publication | Description |
| DBv40 | TUD-S4F-v03-S311 | 2014–2024 | ATBDv4, PVRv4 | Default TUD-S4F setup in DBv4 with new representation of surface live woody vegetation, ESA WorldCover as land cover and burnt area from medium-resolution MODIS (FireCCI51 2014–2018) and Sentinel-3 (FireCCIS311 2019–2024) data |
| DBv40 | TUD-S4F-v03-S211 | 2017–2025 | ATBDv4, PVRv4 | Default TUD-S4F setup in DBv4 with new representation of surface live woody vegetation, ESA WorldCover as land cover and burnt area from high-resolution Sentinel-2 data (Sense4Fire-FireCCIS211 burnt area dataset 2017–2025) |
| DBv20 | TUD-S4F-v02-S4Fba | 2020 | ATBDv3, PVRv3 | Default TUD-S4F setup in DBv2 with ESA CCI land cover and GFA-S4F burnt area for the year 2020 (FireCCI51 for the other years), dynamic emission factors |
| DBv20 | TUD-S4F-v02-F51 | 2014–2021 | ATBDv3, PVRv3 | Default TUD-S4F setup in DBv2 with ESA CCI land cover and FireCCI51 burnt area for 2014-2021, dynamic emission factors |
| DBv10 | KNMI-S5p-v01 | 2020 | ATBDv3, PVRv3 | Top-down estimates of CO and NOx emissions using the beta-method |
| DBv10 | GFA-S4F-v01 | 2020 | ATBDv3, PVRv3 | Original GFA-S4F approach based on Andela et al. (2022) |
Sahel
The region is defined from 23°E – 43°E and 3° – 15°N.
| Database | Approach and setup | Temporal coverage | Use/description in publication | Description |
| DBv40 | TUD-S4F-v03-211 | 2017-2025 | ATBDv4 | Default TUD-S4F setup in DBv4 with new representation of surface live woody vegetation, ESA WorldCover as land cover and burnt area from high-resolution Sentinel-2 data (Sense4Fire-FireCCIS211 burnt area dataset 2017–2025 |
Southern Europe
The region is defined from 10°W – 29.5°E and 34.5°N – 49°N.
| Database | Approach and setup | Temporal coverage | Use/description in publication | Description |
| DBv20 | TUD-S4F-v02-S4Fba | 2020 | ATBDv3, PVRv3 | Default TUD-S4F setup in DBv2 with ESA CCI land cover and GFA-S4F burnt area for the year 2020 (FireCCI51 for the other years), dynamic emission factors |
| DBv20 | TUD-S4F-v02-F51 | 2014–2021 | ATBDv3, PVRv3 | Default TUD-S4F setup in DBv2 with ESA CCI land cover and FireCCI51 burnt area for 2014-2021, dynamic emission factors |
| DBv10 | GFA-S4F-v01 | 2020 | ATBDv3, PVRv3 | Original GFA-S4F approach based on Andela et al. (2022) |
| DBv10 | KNMI-S5p-v01 | 2020 | ATBDv3, PVRv3 | Top-down estimates of CO and NOx emissions using the beta-method |
Siberia
The region is defined from 132°E - 138°E and 60°N - 71°N
| Database | Approach and setup | Temporal coverage | Use/description in publication | Description |
| DBv20 | TUD-S4F-v02-S4Fba | 2020 | ATBDv3, PVRv3 | Default TUD-S4F setup in DBv2 with ESA CCI land cover and GFA-S4F burnt area for the year 2020 (FireCCI51 for the other years), dynamic emission factors |
| DBv10 | GFA-S4F-v01 | 2020 | ATBDv3, PVRv3 | Original GFA-S4F approach based on Andela et al. (2022) |
| DBv10 | KNMI-S5p-v01 | 2020 | ATBDv3, PVRv3 | Top-down estimates of CO and NOx emissions using the beta-method |
Definition of output variables
The following variables are available from Sense4Fire:
|
Variable |
Description |
Unit |
Type |
Approach |
|---|---|---|---|---|
| e_co | fire emissions of carbon monoxide | g/m² | Emission | GFA-S4F, KNMI-S5p, TUD-S4F |
| e_co2 | fire emissions of carbon dioxide | g/m² | Emission | GFA-S4F, TUD-S4F |
| e_ch4 | fire emissions of methane | g/m² | Emission | GFA-S4F, TUD-S4F |
| e_pm25 | fire emissions of particulate matter 2.5 micron | g/m² | Emission | GFA-S4F, TUD-S4F |
| e_nox | fire emissions of nitrogen oxides | g/m² | Emission | GFA-S4F, KNMI-S5p, TUD-S4F |
| ef_co | emission factor carbon monoxide | g/kg | Emission factor | TUD-S4F |
| ef_co2 | emission factor carbon dioxide | g/kg | Emission factor | TUD-S4F |
| ef_ch4 | emission factor methane | g/kg | Emission factor | TUD-S4F |
| ef_pm25 | emission factor particulate matter 2.5 micron | g/kg | Emission factor | TUD-S4F |
| ef_nox | emission factor nitrogen oxides | g/kg | Emission factor | TUD-S4F |
| mce | modified combustion efficiency | unitless | Combustion efficiency | TUD-S4F |
| bm_wood | woody biomass of trees | kg/m² | Fuel load | TUD-S4F |
| bm_leaf | leaf biomass of trees | kg/m² | Fuel load | TUD-S4F |
| bm_herb | herbaceous biomass (incl. crops) | kg/m² | Fuel load | TUD-S4F |
| bm_slw_wood | woody biomass of surface live woody vegetation | kg/m² | Fuel load | TUD-S4F (>= v03) |
| bm_slw_lead | Leaf biomass of surface live woody vegetation | kg/m² | Fuel load | TUD-S4F (>= v03) |
| fwd | fine woody debris (diameter < 7.62 cm) | kg/m² | Fuel load | TUD-S4F |
| cwd | coarse woody debris (diameter > 7.62 cm) | kg/m² | Fuel load | TUD-S4F |
| litter | litter (dead herbaceous and leaf material) | kg/m² | Fuel load | TUD-S4F |
| dmb_total | total dry matter burned (named fc_total before DBv4) | kg/m² | Fuel consumption | GFA-S4F, TUD-S4F |
| dmb_stem | dry matter burned consumption of tree stem biomass (named fc_stem before DBv4) | kg/m² | Fuel consumption | TUD-S4F |
| dmb_branches | dry matter burned from consumption of tree branches biomass (named fc_branches before DBv4) | kg/m² | Fuel consumption | TUD-S4F |
| dmb_leaf | dry matter burned from consumption of tree leaf biomass (named fc_leaf before DBv4) | kg/m² | Fuel consumption | TUD-S4F |
| dmb_herb | dry matter burned from consumption of herbaceous biomass (named fc_herb before DBv4) | kg/m² | Fuel consumption | TUD-S4F |
| dmb_fwd | dry matter burned emissions from consumption of fine woody debris (named fc_fwd before DBv4) | kg/m² | Fuel consumption | TUD-S4F |
| dmb_cwd | dry matter burned emissions from consumption of coarse woody debris (named fc_cwd before DBv4) | kg/m² | Fuel consumption | TUD-S4F |
| dmb_litter | dry matter burned emissions from consumption of leaf and herbaceous litter (named fc_litter before DBv4) | kg/m² | Fuel consumption | TUD-S4F |
| dmb_slw_leaf | dry matter burned emissions from consumption of surface live woody vegetation leaves | kg/m² | Fuel consumption | TUD-S4F (>= v03) |
| dmb_slw_wood | dry matter burned emissions from consumption of surface live woody vegetation wood | kg/m² | Fuel consumption | TUD-S4F (>= v03) |
| fmc_live | live fuel moisture content of leaves and herbaceous vegetation | % | Fuel moisture | TUD-S4F |
| fre | fire radiative energy | MJ/m² | Fire | GFA-S4F |
| fire_type | fire types | classes | Fire | GFA-S4F |
| ba_scale | burned area scaling factor | unitless | Fire | GFA-S4F |
