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-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)
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
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-S211	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
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

 

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	kg/m²	Fuel consumption	TUD-S4F
dmb_branches	dry matter burned from consumption of tree branches biomass	kg/m²	Fuel consumption	TUD-S4F
dmb_leaf	dry matter burned from consumption of tree leaf biomass	kg/m²	Fuel consumption	TUD-S4F
dmb_herb	dry matter burned from consumption of herbaceous biomass	kg/m²	Fuel consumption	TUD-S4F
dmb_fwd	dry matter burned emissions from consumption of fine woody debris	kg/m²	Fuel consumption	TUD-S4F
dmb_cwd	dry matter burned emissions from consumption of coarse woody debris	kg/m²	Fuel consumption	TUD-S4F
dmb_litter	dry matter burned emissions from consumption of leaf and herbaceous litter	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

 

Sense4Fire provides two bottom-up fire emission approaches (GFA-S4F and TUD-S4F) and a set of approaches (summarised as KNMI-S5p) to provide a top-down benchmark of estimated fire emissions: 

• The GFA-S4F approach is based on the Global Fire Atlas (GFA) algorithm  and uses observations of active fires from the VIIRS instrument and of Sentinel-2-derived burnt area maps to provide near-real time estimates of burnt area and performs a mapping of fire types to estimate fire emissions (Andela et al., 2019, 2022a)
• The TUD-S4F approach is a data-model fusion approach that combines several Earth observation products from the Sentinels, from ESA’s Climate Change Initiative (CCI) and from the Copernicus Global Land Service (CGLS) to estimate fuel loads, fuel moisture, fuel consumption, and fire emissions  (Forkel et al., 2025)
• KNMI-S5p is a set of approaches that uses observations of atmospheric CO and NOx with Integrated Forecasting Model (IFS) to evaluate bottom-up fire emission estimates. The approaches have been used in Forkel et al. (2025) to evaluate emissions estimates from GFA-S4F and TUD-S4F, among others.

The development of the three Sense4Fire approaches along with their versioning, application to different study regions, and the related deliverables and publications are shown in the Table.

The three approaches are described in scientific publications (Andela et al., 2022a; Forkel et al., 2025; de Laat et al., 2026) and all most recent updates of each approach are described in the Algorithm Theoretical Baseline Document version 4 (ATBDv4)

Link to Publications

The GFA-S4F and TUD-S4F approaches are developed to be capable of providing 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

Table: Development of the Sense4Fire approaches with the related versions of Algorithm Theoretical Baseline Documents (ATBD), Product Validation Reports (PVR), Impact Assessment Reports (IAR), Databases (DB)

Approach and version	Short description	Related ATBDs, PVRs and IARs	Study region and temporal coverage Publication	Related Database	Data publication date
GFA-S4F-v0.3	Based on GFA-S4F-v0.1 but with updated estimation of NRT burnt area based on Sentinel-2 for Africa	ATBDv4, PVRv4	S-Africa 2019-2025	DBv4	Apr 2026
			Sahel 2019-2025	DBv4	Sep 2026
GFA-S4F-v0.2	Based on GFA-S4F-v0.1 but with improved parametrization for NOx emissions and prolonged time series (2019-2024)	ATBDv2.1, ATBDv3, IARv2.1	Amazon/Cerrado 2018-2025	DBv4	Mai 2026
			Amazon/Cerrado 2024 (de Laat et al., 2026)	DBv3	Dec 2024
			Amazon/Cerrado 2019-2023 (Forkel et al., 2025)	DBv2	Oct 2023
GFA-S4F-v0.1	Original description of the GFA-S4F approach based on Andela et al. (2022)	ATBDv1, PVRv3	Amazon/Cerrado, Europe, S-Africa, and Siberia 2020	DBv1	Nov 2021
TUD-S4F-v0.3	Based on TUD-S4F-v0.2 but with new input datasets and representation of surface live woody vegetation	ATBDv4, PVRv4	S-Africa 2014-2024/2025	DBv4	Apr 2026
			Sahel 2014-2024/2025	DBv4	Sep 2026
TUD-S4F-vNRT01	Near-real time setup based on a machine learning model trained against TUD-S4F-v0.2	IARv2.1	Amazon/Cerrado 2024 (de Laat et al., 2026)	DBv3	Dec 2024
TUD-S4F-v0.2	Default setup of TUD-S4F for all study regions	ATBDv2.1, ATBDv3, PVRv3, IARv2.1	Amazon/Cerrado (Forkel et al., 2025), Europe, S-Africa, and Siberia 2014-2021	DBv2	Oct 2023
TUD-S4F-v0.1 (deprecated)	Initial version of the TUD-S4F approach, replaced by TUD-S4F-v0.2	ATBDv1, PVRv2.1	Amazon/Cerrado, Europe, S-Africa, and Siberia 2014-2021	DBv1	Nov 2021
KNMI-S5p-v0.2	Revised setup of the IFS model	ATBDv4, PVRv4	S-Africa, Sahel	-- (use for validation in DBv4.0/ 4.1)	Sep 2026
KNMI-S5p-v0.1	Initial description of approach	ATBDv2.1, ATBDv3, PVRv3	Amazon/Cerrado   (Forkel et al., 2025; de Laat et al., 2026), Europe, S-Africa, and Siberia 2020	DBv1/v2	Nov 2021

 

GFA-S4F: near real time fire types and emissions

The GFA-S4F approach introduces an object-based methodology, building on the Global Fire Atlas and Amazon Dashboard  (Andela et al., 2019, 2022b), to track individual fires and their characteristics. GFA-S4F makes use of active fire observations from VIIRS. Active fire detections are clustered into fire events using the Fire Atlas algorithm. The size of fire events is then scaled with burnt area from Sentinel-2 imagery to provide realistic near-real time estimates of burnt area. Fire Radiative Energy (FRE) is derived from Fire Radiative Power (FRP) by computing the average FRP over fire duration (i.e. FRP density) and by multiplying with fire duration, providing detailed insights into fire duration and energy release.

Fire types are then estimates by combining properties of indivdiuals fires with land cover information to estimate fire emissions. Fires are clustered into categories like cropland, grassland, savannah, and forest fires.

 

TUD-S4F: fuels, dry matter burnt and emissions

TUD-S4F is a data-model fusion approach that integrates different Earth observation products such as land cover, leaf area index (LAI), soil water index (SWI), and burnt area within a simple ecosystem model to estimate the spatial patterns and temporal dynamics of biomass, fuel loads and fuel moisture, combustion completeness, fuel consumption, combustion efficiency, emission factors, and fire emissions (Figure). The approach has been introduced and applied in Forkel et al. (2025), which assesses the role of woody debris on fire emissions in the Amazon and Cerrado.

The TUD-S4F core model represents different biomass and fuel components such as tree leaves, branches and stems, herbaceous vegetation, surface live woody biomass (SLW, i.e. shrubs and understorey), surface litter, and fine and coarse woody debris (FWD, CWD). Biomass in trees and SLW vegetation is estimated from canopy height, LAI and land cover using allometric equations that are calibrated based on satellite products of canopy height and above-ground biomass. The accumulation of surface fuels is then estimated from changes in land cover and from seasonal and long-term temporal changes in LAI, which affect the turnover of herbaceous and leaf biomass to litter, and of branches and stems to FWD and CWD, respectively. Fuel moisture content (FMC) is estimated from SWI and LAI for leaves and herbaceous biomass (i.e., live-fuel moisture content, LFMC) and from SWI for live woody biomass. Additionally, SWI is used as a proxy for the dead fuel moisture content (DFMC) of surface fuels. The estimated FMC is then used to estimate combustion completeness. 

Unlike in other fire emission inventories, TUD-S4F estimates emission factors dynamically dependent on the chemical composition of biomass and fuel components and fuel moisture by using a chemical-based combustion model. The combustion model is calibrated against field and laboratory databases of emission factors and against satellite observations of fire radiative energy.

 

KNMI-S5p: top down benchmarking of fire emissions

KNMI-S5p validation approach makes use of Sentinel-5p satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI) of atmospheric composition and air pollution to provide top-down constraints on estimated fire emissions. The advance of TROPOMI has been so important for wildfire emission monitoring and for the first time allows for spatiotemporally detailed a continuous monitoring of wildfire emissions.

The KNMI-S5p approach to use an atmospheric chemistry-transport model as an interface between the wildfire emission estimates on the one hand and the satellite measurements of air pollution on the other hand. Bottom-up wildfire emission estimates – like from GFA-S4F and TUD-S4F – are into the model whose results are collocated with the satellite observations of air pollution. This provides an extensive dataset that can be used to explore how well the model reproduces the observations. Model simulations can be tweaked with other fire emission databases or modifications of those emission databases to bring model results and observations in closer agreement. For the short live trace gas nitrogen dioxide (NO₂) the model simulations also allow for applying a post-hoc NO₂ emission correction based on the modelled local sensitivity of tropospheric NO₂ columns to local NO₂ emissions, which has been applied – among others – in Forkel et al. (2025). Assuming linearity between both, that sensitivity – denoted as β-factor – can be multiplied with the differences between model simulated and observed tropospheric NO₂ to a local multiplication factor which to first order brings model simulations and observations of tropospheric NO₂ in better agreement.

Results in Sense4Fire indicated that CO was a useful addition to NO₂. Both traces are emitted by wildfires but have different sensitivities to fire temperatures. Whereas NO₂ emissions increase with increasing fire temperature – and thus indicate flaming fires with nearly complete combustion, CO emissions increase with decreasing fire temperatures. CO thus reflects incomplete combustion. The first results of the exploration of the use of TROPOMI measurements for wildfire emission monitoring have focused on short time periods of several months in single year with a focus Amazonia (Forkel et al., 2025; de Laat et al., 2026; other regions were sub-equatorial Africa, Mediterranean Europe and in Siberia).

 

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/doi:10.1126/sciadv.abd2713.

de Laat, A.T.J., Andela, N., Forkel, M., Huijnen, V., Kinalczyk, D., van Wees, D. (2026).
Sentinel-5p Reveals Unexplained Large Wildfire Carbon Emissions in the Amazon in 2024
Geophysical Research Letters 53, e2025GL115123. https://doi.org/10.1029/2025GL115123

de Laat, A., Huijnen, V., Andela, N., and Forkel, M. (2024).
Assessment of satellite observation-based wildfire emissions inventories using TROPOMI data and IFS-COMPO model simulations
EGUsphere, 1–81, https://doi.org/10.5194/egusphere-2024-732

Forkel, M., Wessollek, C., Huijnen, V., Andela, N., de Laat, A., Kinalczyk, D., Marrs, C., van Wees, D., Bastos, A., Ciais, P., Fawcett, D., Kaiser, J.W., Klauberg, C., Kutchartt, E., Leite, R., Li, W., Silva, C., Sitch, S., Goncalves De Souza, J., Zaehle, S., and Plummer, S. (2025).
Burning of woody debris dominates fire emissions in the Amazon and Cerrado.
Nature Geoscience, 18, 140–147, https://doi.org/10.1038/s41561-024-01637-5

 

 

 

Journal publications

 

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/doi:10.1126/sciadv.abd2713.

This publication describes the first version of the GFA-S4F approach. 

 

de Laat, A.T.J., Andela, N., Forkel, M., Huijnen, V., Kinalczyk, D., van Wees, D. (2026).
Sentinel-5p Reveals Unexplained Large Wildfire Carbon Emissions in the Amazon in 2024
Geophysical Research Letters 53, e2025GL115123. https://doi.org/10.1029/2025GL115123

This publication presents the application of the Sense4Fire approaches in near-real time for the extreme fire season in the Amazon region in 2024. 

 

de Laat, A., Huijnen, V., Andela, N., and Forkel, M. (2024).
Assessment of satellite observation-based wildfire emissions inventories using TROPOMI data and IFS-COMPO model simulations
EGUsphere, 1–81, https://doi.org/10.5194/egusphere-2024-732

This preprint presents the first evaluation of fire emission estimates using the KNMI-S5p approach. 

 

Forkel, M., Wessollek, C., Huijnen, V., Andela, N., de Laat, A., Kinalczyk, D., Marrs, C., van Wees, D., Bastos, A., Ciais, P., Fawcett, D., Kaiser, J.W., Klauberg, C., Kutchartt, E., Leite, R., Li, W., Silva, C., Sitch, S., Goncalves De Souza, J., Zaehle, S., and Plummer, S. (2025).
Burning of woody debris dominates fire emissions in the Amazon and Cerrado.
Nature Geoscience, 18, 140–147, https://doi.org/10.1038/s41561-024-01637-5

This paper compares the three Sense4Fire approaches (TUD-S4F, GFA-S4F and KNMI-S5p) with other fire emission datasets and focusses specifically on the role of fuels for fire emissions. A full description of the TUD-S4F approach is given in the Methods and Supplementary Information. 

 

Technical documents

 

A series of deliverables such as Algorithm Theoretical Baseline Documents (ATBD), Product Validation Reports (PVR) and Impact Assessment Reports (IAR) have been produced in Sense4Fire and published along with the Databases. In order to keep the list of deliverables concise for the potential user of our data products, we list and publish the documents that relate to a certain version of the database.

The fourth version of the Algorithm Theoretical Baseline Document (ATBDv4) provides an updated and complete description of the most recent versions (GFA-S4F-v0.3, TUD-S4F-v0.3, KNMI-S5p-v0.2) of three approaches used in Sense4Fire. The three approaches were previously described in ATBDv2.1 and ATBDv3 (Forkel et al., 2023b, 2024) as well as in the corresponding scientific publications (Andela et al., 2022; Forkel et al., 2025; de Laat et al., 2026). ATBDv4 documents advancements since ATBDv3, reflecting the integration of new Earth observation datasets and methodological developments in the approaches.

Table. Overview about key technical deliverbales in Sense4Fire.

Technical document	Content / Relation to Sense4Fire Database	Date	Link to document
Algorithm Theoretical Baseline Document version 4 (ATBDv4)	Complete description of all approaches used in Sense4Fire, including all updates for Database version 2 (DBv4)	23.04.2026	ATBDv4
Product Validation Report version 3 (PVRv3)	Validation results for products published in Database version 2 (DBv2)	03.05.2024	PVRv3
Algorithm Theoretical Baseline Document version 3 (ATBDv3)	Update of ATBDv2.1 with description of setups and experiments for all study regions as presented in PVRv3 and for products published in Database version 2 (DBv2)	03.05.2024	ATBDv3
Algorithm Theoretical Baseline Document version 2 (ATBDv2.1)	Description of all approaches used in Sense4ire	05.05.2023	ATBDv2.1

 

Presentations at conferences and workshops

Date and event	Presenter	Title and link
23-27 May 2022 | ESA Living Planet Symposium 2022, Bonn, Germany	Jos de Laat	On the use of daily Sentinel-5p trace gas and aerosol observations for characterising small-scale localised wildfires (Presentation)
	Vincent Huijnen	Using Sentinel-5p trace gas and aerosol observations of fire plumes to constrain a global composition model: a critical assessment (Poster)
	Matthias Forkel	Integrating the Sentinels for novel fuel, fire and emissions products to constrain the changing role of vegetation fires in the global carbon cycle (Presentation) (Poster)
	Christine Wessollek	Estimating vegetation fuel loads for the quantification of fire emissions by integrating various Earth observation data (Poster)
	Niels Andela	Understanding Earth Systems - Tracking Amazon fires in near-real time (Presentation)
23-28 April 2023 | EGU General Assembly 2023, Vienna, Austria	Matthias Forkel	Effects of land use, fuel loads and fuel moisture on fire intensity and fire emissions in South America derived by reconciling bottom-up and top-down satellite observations (Abstract) (Presentation)
	Christine Wessollek	Estimating biomass compartments and surface fuel loads by integrating various satellite products with a data-model fusion approach (Abstract) (Presentation)
14-19 April 2024 | EGU General Assembly 2024, Vienna, Austria	Matthias Forkel	Multiple approaches for quantifying fuels, combustion dynamics, and regional fire emissions in the Amazon and Cerrado (Abstract) (Presentation)
	Niels Andela	New insights on global fire extremes from object-based fire inventories (Abstract) (Presentation)
	Dave van Wees	Comparison and validation of state-of-the-art fire emissions models for the Amazon (Abstract) (Presentation)
16-18 Sep 2024 | Fire Modelling Workshop; Leverhulme Centre for Wildfire, Science and Society; Dartington Hall, UK	Matthias Forkel	Vegetation-fire interactions: Role of dead wood
19-20 Sep 2024 | 13th EARSeL Forest Fires Special Interest Group Workshop, Milano, Italy	Matthias Forkel	Complementary Earth Observation Approaches to Advance Fire Emission Estimation (Book of abstracts)
26-28 Nov 2024 | Future Focus Wildfires. Community forum; EUMETSAT; Darmstadt, Germany	Matthias Forkel	Sense4Fire – novel fuel, fire and emission products (Presentation)
23-27 Jun 2025 | ESA Living Planet Symposium 2025, Bonn, Germany	Matthias Forkel	Novel Earth observation data-model fusion approaches reveal dominant role of woody debris in fire emissions in the Amazon and Cerrado
21 Jul 2025 | ESA International Workshop on Fire from Space, Harwell, UK	Matthias Forkel	Sense4Fire – Sentinel-based fuel, fire and emissions products to constrain the changing role of vegetation fires in the global carbon cycle
25-26 Jun 2026 | 14th EARSeL Workshop on Forest Fires, Lisbon, Portugal	Matthias Forkel	Estimating Fuels and Fire Emissions at 20 m Spatial Resolution in African Savannahs using the S4F Satellite Data-Model Fusion Approach (Poster)
	Daniel Kinalczyk	Estimating Fuels and Fire Emissions in Near Real Time (NRT) in African Savannahs
21 Sep 2026 | CBC 2026 Connecting Biodiversity, Climate, and Human Behaviour Conference, Leipzig, Germany	Matthias Forkel	Fire, Fuels, and Feedbacks: Uncovering the Role of Vegetation Structure in Amplifying Climate-Driven Wildfire Emissions

 

The Sense4Fire project, funded by the European Space Agency (ESA), aims to increase the scientific understanding of dynamics and emissions of landscape fires and their role in the carbon cycle by integrating observations from the Sentinels into new Earth observation products.

Sense4Fire develops novel Earth observation approaches and datasets about fire dynamics and emissions. Fire dynamics encompass a broad range of processes, including pre-fire conditions of the land surface (i.e. fuel loads and fuel moisture), fire behaviour (fire ignitions, spread, speed, size, burnt area, fire type, and radiative power), combustion and production of fire emissions (combustion completeness, dry matter burnt, combustion efficiency, and composition of emissions) and the effect of fire emissions on atmospheric composition.

The Sense4Fire project is part of ESA’s Carbon Science Cluster. The project has been originally funded from August 2021 to July 2023 and has been prolonged twice until December 2026.

Outreach