Approaches

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 the 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) and in de Laat et al. (2026) 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 Table 2.

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 in near-real time (NRT), here defined as providing fire emission estimates with a latency of <1 month. In addition, the TUD-S4F approach is currently developed to provide estimates of fuel loads and fire emissions at high spatial resolution (HR, 20 m).

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 1). 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., Giglio, L., Paugam, R., Chen, Y., Hantson, S., Van Der Werf, G. R., and Randerson, J. T.: The Global Fire Atlas of individual fire size, duration, speed and direction, Earth Syst. Sci. Data, 11, 529–552, 2019.

Andela, N., Morton, D. C., Schroeder, W., Chen, Y., Brando, P. M., and Randerson, J. T.: Tracking and classifying Amazon fire events in near real time, Sci. Adv., 8, eabd2713, https://doi.org/doi:10.1126/sciadv.abd2713, 2022a.

Andela, N., Morton, D. C., Schroeder, W., Chen, Y., Brando, P. M., and Randerson, J. T.: Tracking and classifying Amazon fire events in near real time, Sci. Adv., 8, eabd2713, https://doi.org/10.1126/sciadv.abd2713, 2022b.

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.: Burning of woody debris dominates fire emissions in the Amazon and Cerrado, Nat. Geosci., 18, 140–147, https://doi.org/10.1038/s41561-024-01637-5, 2025.

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