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).
  • 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:

Table. Overview about the output variables produced in 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