the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Oct 2025
Direct Estimation of Wildfire Emissions at High Latitudes from Combined Polar Orbiter FRP and Sentinel-5P CO Data
Abstract. High Latitude (HL) landscape fires are an important source of greenhouse gases and aerosols, with growing significance under rapid anthropogenic climate change-induced warming. Current fire emission inventories are mostly ‘bottom-up’ in nature; combining, or relying on linear regressions between, satellite remote sensing data and process-based model outputs. However, these methods rely on uncertainties surrounding fuel load and combustion completeness. Here, we adapt the ‘top-down’ Fire Radiative Energy Emission (FREM) approach for HL fires (HLFREM), linking Fire Radiative Energy (FRE) directly to emissions via coefficients derived solely from satellite observations. We derive biome-specific emission coefficients by combining Fire Radiative Power (FRP) from GFAS v1.4 with TROPOMI Total Column Carbon Monoxide plume observations, for the HL’s four most fire-prone biomes; Deciduous and Evergreen Needleleaf Forests, Grasslands, and Shrublands. By applying these coefficients to daily GFAS v1.2 FRE totals (2003–2024), we estimate CO and total carbon emissions across the HL using HLFREM. HLFREM-derived CO emissions generally agree with other widely used inventories (GFAS v1.2, FEERv1.0-GFASv1.2, and GFEDv4.1s) in forested biomes, with annual average differences of -32 % to -43 % for Deciduous Needleleaf Forests, and -28 % to -43 % for Evergreen Needleleaf forests. For Shrublands and Grassland biomes, HLFREM estimates are 31–43 % and 61–80 % lower respectively. Total carbon emissions, using Emission Factors, were found to show consistent patterns with CO across all biomes. Our results represent the first HL fire emissions dataset based only on satellite data of a major carbon containing gas (CO) emitted by fires and the rate of fire radiative energy release.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-4486', Samuel Villarreal, 13 Jan 2026
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RC2: 'Comment on egusphere-2025-4486', Anonymous Referee #2, 03 Jul 2026
Overview
The manuscript by Maslanka et al. “Direct Estimation of Wildfire Emissions at High Latitudes from Combined Polar Orbiter FRP and Sentinel-5P CO Data “ presents a methodology to obtain CO emission coefficients from satellite CO columns and hourly GFAS FRP. The authors derive emission coefficients for four different broad vegetation classes that are common in Northern latitudes. Total CO emissions are estimated from this and compared to three other common wildfire inventories: GFAS, FEER and GFED. The manuscript s well written and scientifically sound. I believe that the manuscript fits well in the scope of Biogeosciences and should be of interest to the readers of this journal. I suggest publication after the suggested revisions are addressed (see details below).
General comments
- Uncertainty analysis
Overall, the uncertainties, limitations of this method, and assumptions need to be expanded in this manuscript. The approach used to estimate the background CO concentration also requires further justification and uncertainty analysis (sensitivity tests). Using the minimum CO value within the study area may not be the most appropriate choice, given the inherent noise in satellite observations; an upwind background concentration may provide a more robust estimate or at least a lowest percentile of CO. Since the excess CO calculation depends directly on the background concentration, this is likely one of the largest sources of uncertainty in the emission coefficient estimates.
Additionally, the manuscript would be strengthened by a more comprehensive discussion of the uncertainties associated with the methodology, including the uncertainty in the excess CO estimates as illustrated in Figure 6.
A discussion on the influence of combustion phase (flaming versus smoldering) on the emission coefficients is also missing, I assume this has been neglected because TROPOMI does not have too many overpasses per day. But a discussion of this needs to be included, as well as the assumptions underlying the approach.
- Description on TROPOMI CO
The manuscript would benefit from either a separate section on TROPOMI CO or including this as part of section 3.4, describing TROPOMI CO in more details including data version used, quality filters used, uncertainties and validation of the CO data product (meaning citation and discussion of appropriate studies for this, I don’t mean validating this product as part of this study).
This is a good reference for TROPOMI CO in smoke:
Jake P. Rowe, Kyle J. Zarzana, Natalie Kille, Tobias Borsdorff, Manu Goudar, Christopher F. Lee, Theodore K. Koenig, Johana Romero-Alvarez, Teresa Campos, Christoph Knote, Nicolas Theys, Jochen Landgraf, and Rainer Volkamer, ACS Earth and Space Chemistry 2022 6 (7), 1799-1812, DOI: 10.1021/acsearthspacechem.2c00048
- Comparison to previous studies
The claim in the abstract (Lines 23–24) that this is the first study of its kind should be reconsidered or better justified. Previous studies have derived emission coefficients for these biomes, and emission coefficients have been reported using different methodologies. It would therefore be valuable to discuss how the emission coefficients presented in this study compare with those reported in previous work (e.g., Adams et al., 2019; Hayden et al., 2022; Griffin et al., 2024). In addition, I suggest considering the following references and determine whether they are relevant to cite and discuss in the context of the current study:
Adams, C., McLinden, C. A., Shephard, M. W., Dickson, N., Dammers, E., Chen, J., Makar, P., Cady-Pereira, K. E., Tam, N., Kharol, S. K., Lamsal, L. N., and Krotkov, N. A.: Satellite-derived emissions of carbon monoxide, ammonia, and nitrogen dioxide from the 2016 Horse River wildfire in the Fort McMurray area, Atmos. Chem. Phys., 19, 2577–2599, https://doi.org/10.5194/acp-19-2577-2019, 2019.
Mebust, A. K. and Cohen, R. C.: Space-based observations of fire NOx emission coefficients: a global biome-scale comparison, Atmos. Chem. Phys., 14, 2509–2524, https://doi.org/10.5194/acp-14-2509-2014, 2014.
Mebust, A. K., Russell, A. R., Hudman, R. C., Valin, L. C., and Cohen, R. C.: Characterization of wildfire NOx emissions using MODIS fire radiative power and OMI tropospheric NO2 columns, Atmos. Chem. Phys., 11, 5839–5851, https://doi.org/10.5194/acp-11-5839-2011, 2011.
Voshtani, S., Jones, D. B. A., Wunch, D., Pendergrass, D. C., Wennberg, P. O., Pollard, D. F., Morino, I., Ohyama, H., Deutscher, N. M., Hase, F., Sussmann, R., Weidmann, D., Kivi, R., García, O., Té, Y., Chen, J., Anderson, K., Stevens, R., Kondragunta, S., Zhu, A., Worthy, D., Racki, S., McKain, K., Makarova, M. V., Jones, N., Mahieu, E., Cadena-Caicedo, A., Cristofanelli, P., Labuschagne, C., Kozlova, E., Seitz, T., Steinbacher, M., Mahdi, R., and Murata, I.: Quantifying CO emissions from boreal wildfires by assimilating TROPOMI and TCCON observations, Atmos. Chem. Phys., 25, 15527–15565, https://doi.org/10.5194/acp-25-15527-2025, 2025.
Chelsea E. Stockwell, Megan M. Bela, Matthew M. Coggon, Georgios I. Gkatzelis, Elizabeth Wiggins, Emily M. Gargulinski, Taylor Shingler, Marta Fenn, Debora Griffin, Christopher D. Holmes, Xinxin Ye, Pablo E. Saide, Ilann Bourgeois, Jeff Peischl, Caroline C. Womack, Rebecca A. Washenfelder, Patrick R. Veres, J. Andrew Neuman, Jessica B. Gilman, Aaron Lamplugh, Rebecca H. Schwantes, Stuart A. McKeen, Armin Wisthaler, Felix Piel, Hongyu Guo, Pedro Campuzano-Jost, Jose L. Jimenez, Alan Fried, Thomas F. Hanisco, Lewis Gregory Huey, Anne Perring, Joseph M. Katich, Glenn S. Diskin, John B. Nowak, T. Paul Bui, Hannah S. Halliday, Joshua P. DiGangi, Gabriel Pereira, Eric P. James, Ravan Ahmadov, Chris A. McLinden, Amber J. Soja, Richard H. Moore, Johnathan W. Hair, and Carsten Warneke, Environmental Science & Technology 2022 56 (12), 7564-7577, DOI: 10.1021/acs.est.1c07121
Griffin, D., Chen, J., Anderson, K., Makar, P., McLinden, C. A., Dammers, E., and Fogal, A.: Biomass burning CO emissions: exploring insights through TROPOMI-derived emissions and emission coefficients, Atmos. Chem. Phys., 24, 10159–10186, https://doi.org/10.5194/acp-24-10159-2024, 2024.
Hayden, K. L., Li, S.-M., Liggio, J., Wheeler, M. J., Wentzell, J. J. B., Leithead, A., Brickell, P., Mittermeier, R. L., Oldham, Z., Mihele, C. M., Staebler, R. M., Moussa, S. G., Darlington, A., Wolde, M., Thompson, D., Chen, J., Griffin, D., Eckert, E., Ditto, J. C., He, M., and Gentner, D. R.: Reconciling the total carbon budget for boreal forest wildfire emissions using airborne observations, Atmos. Chem. Phys., 22, 12493–12523, https://doi.org/10.5194/acp-22-12493-2022, 2022. – particularly section 3.5.2
- Limiting this method to high latitude fires
Hourly GFAS FRP is available globally, as are TROPOMI CO observations. Therefore, I do not fully understand the rationale for restricting this analysis to high-latitude fires. While higher latitudes benefit from multiple TROPOMI overpasses per day, the manuscript does not clearly explain how these additional overpasses are utilized. For example, Figure 4 describing the methodology appears to include only a single TROPOMI overpass.
The manuscript mentions that northern latitudes can have "up to three" TROPOMI overpasses per day. Does this simply mean that up to three independent emission coefficient estimates are included in the analysis (i.e., one for each overpass) multiple points on Fig. 6?, or are the multiple overpasses incorporated in a more sophisticated way for which multiple overpasses per day are needed. The methodology should clearly describe how multiple overpasses improve the analysis (e.g., by accounting for temporal variability in emissions throughout the day).
Expanding this approach to fires globally would substantially increase the impact and applicability of the study. If such an analysis is beyond the scope of the current work, I recommend including a discussion of its feasibility as future work. Alternatively, the authors should provide stronger justification for why a single TROPOMI overpass, together with hourly GFAS FRP, is insufficient for applying the proposed methodology.
- Figure Quality
Quality of figures could be improved, making label fonts larger, making the figures higher resolution, etc. see detailed suggestions in specific comments.
Specific comments
l.133: “FRE over the time it took the plume to form”: how do you find this time?
l.283/284: this is not surprising as the HL-FREM relies on GFAS FRE just a different scaling factor.
Fig. 1: increase the font size
Fig. 7: the lines could be thicker it is hard to see the different colours.
Fig. 8 it would be helpful to have the map in the same projection as Fig.2b perhaps having them side by side would make it easier as well.
Fig. 8b-e: the label on the bottom figures is cut off
Fig. 9: it’s quite hard to see any details in the map – perhaps removing the background land colors would make it clearer. It’s also hard to see where it is– could it be zoomed out a more or showing a similar projection as Fig. 2b. Instead of each emissions, showing the difference to FREM for e-j could make it clearer
Fig.10: why not combine GFED into the other figures (a,c,e,g) as done before?
Fig. A2,A3: should be higher resolution or the axes labels should be in a larger font, it is hard to read
Technical corrections:
l.19: GFAS listed twice is that correct?
l.20: which one is higher? HLFREM or other inventories – this should be made clearer here
l.237: Only 26 and 22 fire are identified for grassland and shrubland, respectively. Are these enough to estimate a biome specific emission coefficient? Also please specify the time period that was used here.
l.273/274: do you mean FRP or FRE? Before it looks like you derived the relationship between FRE and mass
Citation: https://doi.org/10.5194/egusphere-2025-4486-RC2 -
RC3: 'Comment on egusphere-2025-4486', Anonymous Referee #3, 03 Jul 2026
The manuscript presents a novel approach for estimating landscape fire emissions at high latitudes using an emissions inventory developed using satellite FRE estimates and emissions coefficients derived through comparison between GFAS FRE and Sentinel-5P TCCO data over a large number of manually identified fires and smoke plumes.The manuscript is clearly written and suitable for publication. Below are some comments and minor corrections.CommentsIt would be useful to include a section that describes the GFAS v1.2 and 1.4 and S5P datasets used in developing the emissions dataset to better understand their benefits and limitations for this application.Two versions of the GFAS dataset are used in the production of HLFREM. GFAS v1.4 is used to derive the emissions coefficients as it provides hourly FRP retrievals whilst GFAS v1.2 is used to estimate emissions as it provides FRP over a longer time period. Is the FRP in GFAS v1.4 derived using the GFAS v1.2 FRP but diurnally distributed (i.e. the same FRP but with a different temporal distribution)? The FRP in GFAS is cloud cover adjusted – could this contribute to some of the variability in Figure 6 or impact the relationship between energy and emissions?Minor comments#205 – a fire affected area polygon could cover a large area. Under this scenario, is the fire FRP\FRE that from all GFAS 0.1 grid cells that intersect the polygon?#220 – at higher latitudes S5P can have >1 observation. In calculating the excess CO, is the data from all overpasses used\accumulated?#237 – “sparce” - sparseFigure 1: Could add a line showing the limits of the HL region (either on both a and b or just b)Figure 2a – perhaps plot the EURO region FRP on a 2nd y-axis given its low magnitudeTable 4 – the EFs from FEER for deciduous forest and grassland are the same. Is this correct?Figures – in general the figures would benefit from being higher resolution – the tick labels, legends and annotations etc could be larger \ clearer.Citation: https://doi.org/
10.5194/egusphere-2025-4486-RC3
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This manuscript presents a methodologically innovative and scientifically significant advancement in the assessment of high-latitude wildfires (≥ 60° N), achieved through a robust integration of multiple satellite-derived data sources within a well-conceptualized methodological framework. The study is well aligned with the scope of Biogeosciences and represents a genuine advance over existing wildfire emission inventories.
I recommend minor revisions, not due to flaws in the core methodology, but because several conceptual, statistical, and reproducibility aspects should be strengthened.
The manuscript would benefit from explicitly stated research questions and testable hypotheses in the Introduction. This would improve conceptual clarity and narrative structure.
The manual digitization of matchup fires may introduce subjectivity and selection bias. A quantitative comparison between matchup fires and the full fire population is recommended.
Biome-specific emission coefficients are derived using zero-intercept OLS regression. The physical justification for this assumption should be clarified, and sensitivity analyses or alternative regression approaches should be considered.
The framework does not explicitly distinguish soil carbon combustion, which may lead to underestimation in peat-rich regions. This limitation should be clearly discussed.
Uncertainties are not fully propagated into long-term emission totals. A clearer description of uncertainty propagation is encouraged.