The Sense4Fire project provides estimates of fire emissions, emission factors, fuel loads, dry matter emissions and live fuel moisture from three complementary approaches for four study regions. Each approach aims to estimate fire emissions by considering properties of individual fires:
The Sense4Fire project provides estimates of fire emissions, emission factors, fuel loads, dry matter emissions and live fuel moisture from three complementary approaches for four study regions.
- GFA-S4F is based on the Global Fire Atlas (GFA) algorithm (Andela et al. 2022) and uses observations of active fires from the VIIRS sensors with a new fire type map to estimate fire emissions.
- TUD-S4F is a new data-model fusion approach that combines several datasets from Sentinel-3 and other Earth observation products to estimate fuel loads, fuel moisture, fuel consumption, and fire emissions. Emission factors are computed dynamically depending on fuel type and fuel composition.
- KNMI-S5p is based on observations from Sentinel-5p, whereby fire emissions of CO and NOx are estimated using a top-down approach. The approach serves as a constraint on regional total fire emissions.
A full list of all available products from each approach is listed at the end of this page.
The datasets cover four regions, namely South America (Amazon and Cerrado), southern Europe, southern Africa, and a region in eastern Siberia. All approaches were applied to the fire season 2020 to compare all approaches. Fire activity can vary considerably from year to year and 2020 was found to be of particular interest for the selected regions (e.g., extensive drought driven understorey fires in Brazil, and large forest fires in Eastern Russia). Individual approaches in Sense4Fire are additionally applied to other years.
Example of the Sense4Fire data: Emissions in the Amazon 2020
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Total fire CO emissions in 2020
The map shows total carbon monoxide emissions over the Amazon and Cerrado in the year 2020 as estimated with the TUD.S4F approach from the Sense4Fire project. The data is produced at a spatial resolution of 333 x 333 m (High-res) and also provided at a coarser resolution (aggregated to 0.1° x 0.1°).
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Comparison of emission approaches
The total emissions of total dry matter (DM), carbon monoxide (CO), and nitrogen oxide (NOx) in the Amazon and Cerrado region in 2020 from the three approaches in Sense4Fire are compared in this figure. As a comparison, we here also show the GFED500m product, which is the Global Fire Emissions Database 500 meter model by van Wees et al. (2022)
Sense4Fire Experimental Database v02 - most recent release
The second version of the Sense4Fire Experimental Database was pubished in October 2023 with results for the Amazon/Cerrado, southern Africa, and Siberian study regions and extended in May 2024 with results for southern Europe.
Experimental database v02 - browse the full file structure
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TUD-S4F v0.2
TUD-S4F provides fuel loads, fuel consumption and fire emissions. The data is provided at a spatial resolution of 333 x 333 m and aggregated to 0.1 x 0.1° for Amazon, Europe, S-Africa, and Siberia. It covers the period 2014-01-01 to 2021-10-20 at 10-daily time step.
References: Algorithm Theoretical Baseline Document v3 Ch. 4, Product Validation Report v3, Forkel et al. (in review)
Datasets
- S4F.CCILC_S4Fba_dynEFThe TUD-S4F default experiment computes dynamic emission factors and uses as input burned area data for the year 2020 from the GFA-S4F approach and for the other years from the ESA CCI fire (v5.1) dataset .
- S4F.CCILC_S4Fba_fixEFThis experiment differs from S4F.CCILC_S4Fba_dynEF in the estimated emissions because it is based on fixed emission factors for forests and grasslands. Estimated fuel loads and fuel consumption are identical to S4F.CCILC_S4Fba_dynEF. The experiment is available for the Amazon region only.
- S4F.CCILC_FireCCI51This experiment uses burned area from ESA's CCI fire (v5.1) dataset only and dynamic emission factors.
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KNMI-S5p v0.1
KNMI-S5p provides fire emission for carbon monoxide (CO) and nitrogen oxides (NO) derived from Sentinel-5p observations. The data is provided for all regions at 0.1 x 0.1° and at daily time step for the year 2020.
References: Algorithm Theoretical Baseline Document v3 Ch. 5, Product Validation Report v3, de Laat et al. (2024)
Datasets
- KNMI-S5pKNMI-S5p provides CO and NO emissions for all regions for 2020.
The dataset is identical to the dataset in the Experimental Database version 01.
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GFA-S4F v0.1 and v0.2
GFA-S4F provides fire emissions and fuel consumption derived from VIIRS Fire Radiative Power observations based on a fire type classification. The method is based on Andela et al. (2022). The data is provided at a spatial resolution of 333 x 333 m and aggregated to 0.1 x 0.1° for all regions and covers the year 2020. Fire emission are provided daily. The fire type map is an annual map.
References: Algorithm Theoretical Baseline Document v3 Ch. 3, Product Validation Report v3, Andela et al. (2022)
Datasets
- GFA-S4F v0.1Results for all regions for 2020. For the Amazon/Cerrado region, please use v0.2.
- GFA-S4F v0.2Like GFA-S4F-FRP v0.1 but with improved parametrisation for NOx emissions and prolonged time series (2019-2023). Only for the Amazon/Cerrado study region.
Sense4Fire Experimental Database v01
The first version of the Sense4Fire Experimental Database was made available in May 2023 and provided the first products for the Amazon, southern Africa, 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 GFA-S4F results for the Amazon/Cerrado have been updated for version 02 (see above).
The highly experimental TUD-S4F datasets in version 01 of the Experimental Database are deprecated and are kept here for documentary purposes only. Users should refer for TUD-S4F results to version 02 of the Experimental Database.
Experimental database v01 - browse the full file structure
List of output variables
The following products 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/shrubs |
kg/m² |
Fuel load |
TUD-S4F |
bm_leaf |
leaf biomass of trees/shrubs |
kg/m² |
Fuel load |
TUD-S4F |
bm_herb |
herbaceous biomass (incl. crops) |
kg/m² |
Fuel load |
TUD-S4F |
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 |
fc_total |
total dry matter burned emissions |
kg/m² |
Fuel consumption |
GFA-S4F, TUD-S4F |
fc_stem |
dry matter burned emissions from consumption of tree stem biomass |
kg/m² |
Fuel consumption |
TUD-S4F |
fc_branches |
dry matter burned emissions from consumption of tree branches biomass |
kg/m² |
Fuel consumption |
TUD-S4F |
fc_leaf |
dry matter burned emissions from consumption of tree leaf biomass |
kg/m² |
Fuel consumption |
TUD-S4F |
fc_herb |
dry matter burned emissions from consumption of herbaceous biomass |
kg/m² |
Fuel consumption |
TUD-S4F |
fc_fwd |
dry matter burned emissions from consumption of fine woody debris |
kg/m² |
Fuel consumption |
TUD-S4F |
fc_cwd |
dry matter burned emissions from consumption of coarse woody debris |
kg/m² |
Fuel consumption |
TUD-S4F |
fc_litter |
dry matter burned emissions from consumption of leaf and herbaceous litter |
kg/m² |
Fuel consumption |
TUD-S4F |
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 |