Biomass burning emission estimation in the MODIS era: State-of-the-art and future directions
Bibliographic record
Abstract
Accurate estimates of biomass burning (BB) emissions are of great importance worldwide due to the impacts of these emissions on human health, ecosystems, air quality, and climate. Atmospheric modeling efforts to represent these impacts require BB emissions as a key input. This paper is presented by the Biomass Burning Uncertainty: Reactions, Emissions and Dynamics (BBURNED) activity of the International Global Atmospheric Chemistry project and largely based on a workshop held in November 2023. The paper reviews 9 of the BB emissions datasets widely used by the atmospheric chemistry community, all of which rely heavily on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations of fires scheduled to be discontinued at the end of 2025. In this time of transition away from MODIS to new fire observations, such as those from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite instruments, we summarize the contemporary status of BB emissions estimation and provide recommendations on future developments. Development of global BB emissions datasets depends on vegetation datasets, emission factors, and assumptions of fire persistence and phase, all of which are highly uncertain with high degrees of variability and complexity and are continually evolving areas of research. As a result, BB emissions datasets can have differences on the order of factor 2–3, and no single dataset stands out as the best for all regions, species, and times. We summarize the methodologies and differences between BB emissions datasets. The workshop identified 5 key recommendations for future research directions for estimating BB emissions and quantifying the associated uncertainties: development and uptake of satellite burned area products from VIIRS and other instruments; mapping of fine scale heterogeneity in fuel type and condition; identification of spurious signal detections and information gaps in satellite fire radiative power products; regional modeling studies and comparison against existing datasets; and representation of the diurnal cycle and plume rise in BB emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".