the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 31 Mar 2026
Tracking recent extremes and interannual variability of global fire emissions using a near-real-time extension to the Global Fire Emissions Database
Abstract. The Global Fire Emissions Database (GFED) is widely used to quantify spatiotemporal variability and long-term trends in burned area and fire emissions, supporting assessments of fire impacts on ecosystems and atmospheric composition. GFED has historically relied on observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), but orbital drift and planned sensor decommissioning pose challenges for maintaining record continuity and near-real-time (NRT) monitoring. Here we present the GFED5 near-real-time extension (GFED5NRT), a global fire emissions dataset that enables daily NRT analyses using active fire observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). GFED5NRT uses biome- and region-specific lookup tables of effective fire area and fuel consumption, derived from VIIRS observations and standard GFED5 datasets, to estimate burned area and emissions from VIIRS active fire counts in a manner consistent with the GFED5 time series. Comparisons with GFED5 and independent datasets show strong agreement in spatial patterns, seasonal cycles, and interannual variability of fire activity. GFED5NRT captures recent major fire extremes and provides daily global NRT estimates of burned area and emissions for multiple trace gases and aerosols. Together, GFED5 and GFED5NRT provides a coherent framework for long-term analyses and NRT monitoring of evolving fire regimes in a changing climate. The GFED5NRT dataset is publicly available at https://doi.org/10.5281/zenodo.18702700 (Chen et al., 2026).
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Status: open (until 16 Jun 2026)
- RC1: 'Comment on essd-2026-158', Anonymous Referee #1, 26 Apr 2026 reply
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RC2: 'Comment on essd-2026-158', Anonymous Referee #2, 24 May 2026
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Review of “Tracking recent extremes and interannual variability of global fire emissions using a near-real-time extension to the Global Fire Emissions Database”
As global climate, landcover, and human population continue to change it is imperative to monitor global fire activity and associated emissions. Remote sensing from satellites is the best way to do this but continuous monitoring will be perpetually challenged by the relatively short lifetimes of satellites and changes in the quality of the data they provide. This new GFED5NRT product is timely in that it allows for extension of the MODIS-based GFED5, which ended after 2022 due to orbital drift of Terra and Aqua. The authors have done much work to ensure good continuity between the GFED5 and the new VIIRS-based extension. Further, the automated real-time element will allow for unprecedented ability to place new events of interest in a longer-term context, and it’s great that the automated process also loops back and improves on the prior real-time data when improved retrospective data become available. The paper is written clearly and the figures/analyses provided do a nice job of demonstrating the quality and value of the dataset. The conclusions section transparently discusses limitations and places this real-time product in the context of exciting future next steps. I suggest publication with no necessary revisions.
Citation: https://doi.org/10.5194/essd-2026-158-RC2 -
RC3: 'Comment on essd-2026-158', Anonymous Referee #3, 28 May 2026
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General comments
The paper presents and update of GFED5 BA and wildfire emissions for daily monitoring of both variables based on VIIRS active fire information. The paper is well written and documented and the topic is very appropriate for the journal scope.
Major issues:
I have a couple of major issues and a few minor aspects.
My first concern relates to the modelling environment of GFED5NRT, which includes additional uncertainty in the original product. This uncertainty is not deeply analysed in the paper, which greatly limits its practical use. The new version should include this aspect. In the same line, since EFA scalars are derived from GFED5 BA, which is also modelled from HR datasets, the impact of errors distribution may be significant. The VPN BA products estimate similar BA as MODIS, therefore well below the GFED5 BA estimations. I think this new product should be based on direct comparison with those HR datasets used to derive the GFED5 estimations, whenever possible. In addition, this modelling effort may benefit by using FRP instead of AF information, as the actual emissions are not only related to the number of fire events, but also to their intensity-severity.
It is not clear to me how the EFA and FC values of figure 3 were derived and why critical combinations of cover-biome were not considered (I assume this is the meaning of the white colors (but it is not indicated)? In any case, it seems odd that peats are not considered in the BONA or BOAS regions.
Minor issues:
Line 121: "To reduce commission errors associated with the coarse spatial resolution of MODIS", I assume this is a spelling error, since MODIS MCD64 has much higher omission than commission errors.
Line 150: thereby improving fire mapping, fire behavior characterization, and applications in disaster response and land management." References are needed here.
Line 163: "we selected the NRT fire image products (VNP14IMG_NRT and VJ114IMG_NRT), rather than the NRT fire location products", not clear what products you refer to, and what are the processing differences between these two options.
Figures 4 and 7 have basically the same information. If you keep the latter, include at least the uncertainty of the estimations, which is now not covered.
You used as a cross-comparison data from Canada and Brazil (although Mapbiomas does not include daily estimations, but I monthly with a high uncertainty as Landsat has low temporal resolution). Why not US and European data? It may be convenient to take a look to the compilation of BA datasets published by Gincheva et al. (2024).
Gincheva, A., Pausas, J.G., Edwards, A., Provenzale, A., Cerdà, A., Hanes, C., Royé, D., Chuvieco, E., Mouillot, F., Vissio, G., Rodrigo, J., Bedía, J., Abatzoglou, J.T., Senciales González, J.M., Short, K.C., Baudena, M., Llasat, M.C., Magnani, M., Boer, M.M., González, M.E., Torres-Vázquez, M.Á., Fiorucci, P., Jacklyn, P., Libonati, R., Trigo, R.M., Herrera, S., Jerez, S., Wang, X. y Turco, M. (2024). A monthly gridded burned area database of national wildland fire data. Scientific Data, 11, 352.
Citation: https://doi.org/10.5194/essd-2026-158-RC3
Data sets
GFED5NRT: Global Fire Emissions Database Near-Real-Time Extension Yang Chen, Guido R. van der Werf, Mingquan Mu, Dave van Wees, Joanne V. Hall, Louis Giglio, Roland Vernooij, Douglas C. Morton, Li Xu, Tianjia Liu, Rebecca C. Scholten, Shane R. Coffield, Melanie B. Follette-Cook, Lesley Ott, and James T. Randerson https://doi.org/10.5281/zenodo.18702700
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This manuscript presents a near-real-time extension of the Global Fire Emissions Database (GFED5NRT), which aims to provide timely estimates of burned area and fire emissions by integrating VIIRS active fire detections within the established GFED framework. The development of a near-real-time product is timely and potentially valuable for monitoring recent fire activity and extreme events, and the effort to maintain consistency with the long-term GFED record is particularly appreciated.
While the manuscript demonstrates overall temporal consistency between GFED5 and GFED5NRT, the quantification and interpretation of uncertainties remain limited. For example, the reported burned area uncertainties (12–45%, Line 433) are not clearly reflected in the analyses presented (e.g., Fig. S3), and it is unclear how these uncertainties vary across regions. In addition, uncertainty in emissions is not explicitly assessed. Emissions are estimated from burned area and fuel consumption, where fuel consumption depends on combustion completeness and fuel load—both of which vary in space and time. The use of static fuel consumption parameters, even when region- and biome-specific, may further amplify uncertainties, particularly given the already existing uncertainty in burned area estimates. While I appreciate the trade-off between timeliness and accuracy inherent in near-real-time products, a more explicit discussion or quantification of uncertainty—both for burned area and emissions—would greatly improve the robustness and interpretability of the dataset and help guide appropriate use by the community.
Regarding extreme events, GFED5NRT appears capable of capturing major fire hotspots. However, the manuscript would benefit from a more explicit evaluation of its performance during extreme fire events within the overlapping period with GFED5 (2012–2022). While comparisons of 2023 emissions with other datasets are useful, additional evaluation against GFED5, particularly for well-documented extreme events during the overlap period, would help demonstrate how the GFED5NRT methodology performs and provide greater confidence in its application to recent extremes.
The evaluation of the dataset focuses primarily on regional and global time series, with limited direct assessment of spatial performance. Spatial consistency is evaluated only indirectly, for example through grid-cell scatter plots (Fig. S3) and latitudinally averaged anomaly patterns (Z-score maps; Fig. 5). While these analyses provide useful information on overall agreement and large-scale patterns, they do not fully capture spatial discrepancies in absolute burned area or emissions. Additional diagnostics, such as direct spatial comparisons in absolute values, would help more clearly reveal GFED5NRT performance. This would substantially improve the assessment of spatial performance and enhance confidence in the dataset for regional-scale applications.
The dataset claims to include multi-species emissions (trace gases and aerosols), but the evaluation is largely limited to CO and total carbon emissions. Additional assessment of other species (e.g., CH₄, N₂O, and aerosols) would strengthen the study.
Some additional minor suggestions:
Table S5: It would be helpful to indicate whether the reported trends are statistically significant.
Figure captions: Please clarify that “fire emissions” refers to total fire carbon emissions where applicable.
Overall, the dataset has clear potential value, but the manuscript would benefit from improved clarity in uncertainty estimates, clearer articulation of dataset limitations, and more explicit guidance on appropriate use.