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

 

Table. Overview about the study regions used in Sense4Fire.

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.

 

Table. Overview about all datasets produced in Sense4Fire.

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)

ATBDv4

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

DBv2, Amazon/Cerrado, 2014-2021

DBv2, Europe, 2014-2021

DBv2, S-Africa, 2014-2021

(Forkel et al. 2025)

ATBDv3

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

DBv2, Amazon/Cerrado, 2020

DBv2, Europe, 2020

DBv2, S-Africa, 2020

DBv2, Siberia, 2020

(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

DBv3, Amazon/Cerrado, 2024

DBv2, Amazon/Cerrado, 2019-2023

 

(de Laat et al. 2026)

(Forkel et al. 2025)

GFA-S4F with improved parametrization for NOx emissions and prolonged time series (2019-2024)
 GFA-S4F-v0.1 default

DBv1, Amazon/Cerrado, 2020

DBv1, Europe, 2020

DBv1, S-Africa, 2020 

DBv1, Siberia, 2020

PVRv3

(Andela et al. 2022)

Original GFA-S4F approach based on Andela et al. (2022)

 KNMI-S5p-v0.1

default

DBv1, Amazon/Cerrado, 2020

DBv1, Europe, 2020

DBv1, S-Africa, 2020

DBv1, Siberia, 2020

(Forkel et al. 2025), ATBDv3

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:

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 (>= 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