As part of the Sense4Fire project, several approaches have been or are being developed, tested and used in different study regions around the world and for different years, involving the use of different input dataset for burnt area and different configurations of the GFA-S4F, TUD-S4F and KNMI-S5p approaches. Below you find the definition of the study regions used in Sense4Fire, an overview about the development of the Sense4Fire database and all datasets, and a list of all available output variables.
Study regions
|
Study region or test area |
East/West extent |
North/South extent |
Included in Database Version |
| Amazon study region, including the Cerrado biome | 40°W - 80°W | 25°S - 10°N | DBv1, DBv2, DBv3 |
| S-Africa: southern Africa study region | 10°E - 30°E | 5°S - 25°S | DBv1, DBv2, DBv4 |
| Sahel study region | 10°E – 43.3°E | 0° - 18.1°N | DBv4 (in development) |
| Europe study region, covering mainly the Mediterranean | 10°W – 29.5°E | 34.5°N - 49°N | DBv1, DBv2 |
| Siberia test area | 132°E - 138°E | 60°N - 71°N | DBv1, DBv2 |
Overview about all datasets
Browse the full folder structure of all datasets.
|
Product / Approach |
Latency / Maturity |
Database Study region and temporal coverage |
Publication |
Product latency/maturity level and description |
|
TUD-S4F-v0.3-S311 |
default |
DBv4, S-Africa, 2014-2019/2024 DBv4, Sahel 2014-2019/2024 (upcoming) |
Default TUD-S4F setup in DBv4 with new representation of surface live woody vegetation, ESA WorldCover as land cover and prolonged burnt area time series (FireCCI51 2014-2018 + FireCCIS311 2019-2024) |
|
| TUD-S4F-v0.3-GFA | provisional |
DBv4, S-Africa, 2014-2019/2025 DBv4, Sahel 2014-2019/2024 (upcoming) |
ATBDv4 | TUD-S4F with ESA Worldcover land cover and GFA-S4F burnt area (2019-2025) |
| TUD-S4F-v0.3-NRT | near-real time | DBv4, regional case study (upcoming) | ATBDv4 | Like TUD-S4F-v0.3-S311 but with NRT LAI and GFA-S4F burnt area (2019-2025) |
| TUD-S4F-vNRT01 | experimental | DBv3, Amazon/Cerrado, 2024 | (de Laat et al. 2026) | Initial near-real time setup based on a machine learning model trained against TUD-S4F-v0.2-S4Fba |
| TUD-S4F-v0.2-F51 | default | Default TUD-S4F setup in DBv2 with ESA CCI land cover and FireCCI51 burnt area for the year 2020, dynamic emission factors | ||
| TUD-S4F-v0.2-S4Fba | default | (Forkel et al. 2025) ATBDv3 |
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 |
|
| TUD-S4F-v0.2-fixEF | experimental | DBv2, Amazon/Cerrado, 2020 | (Forkel et al. 2025) | Like TUD-S4F-v0.2-S4Fba but with fixed emission factors |
| TUD-S4F-v0.1 | deprecated | DBv1, all regions, 2014-2021 | ATBDv1 | Initial version of the TUD-S4F approach, replaced by TUD-S4F-v0.2 |
| GFA-S4F-v0.3 | provisional |
DBv4, S-Africa, 2019-2025 (upcoming) DBv4, Sahel, 2019-2025 (upcoming) |
ATBDv4 | GFA-S4F with updated estimation of NRT burnt area based on Sentinel-2 for Africa |
| GFA-S4F-v0.2 | default |
DBv4, Amazon/Cerrado, 2018-2025 |
|
GFA-S4F with improved parametrization for NOx emissions and prolonged time series (2019-2024) |
| GFA-S4F-v0.1 | default | Original GFA-S4F approach based on Andela et al. (2022) | ||
|
KNMI-S5p-v0.1 |
default |
Top-down estimates of CO and NOx emissions using the beta-method |
Database development
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, the southern Africa study region 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 first datasets of DBv4 were published in April 2026 and further datasets are added during 2026.
Database version 3 (DBv3): Near-real time analysis of the Amazon 2024 fire season
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).
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 for the results published in Forkel et al. (2025).
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.
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 (>= v0.3) |
| bm_slw_lead | Leaf biomass of surface live woody vegetation | kg/m² | Fuel load | TUD-S4F (>= v0.3) |
| 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 (>= v0.3) |
| dmb_slw_wood | dry matter burned emissions from consumption of surface live woody vegetation wood | kg/m² | Fuel consumption | TUD-S4F (>= v0.3) |
| 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 |
