Small fires, big gap: High-resolution VIIRS data reveal widespread underestimation of emissions in sub-Saharan Africa
Bibliographic record
Abstract
Fires across sub-Saharan Africa (SSA) are a dominant source of global carbon emissions, yet their true magnitude remains uncertain due to the limitations of coarse-resolution satellite products. In this study, we developed a high-resolution fire emission inventory prototype for SSA using active fire detections from the VIIRS sensor (375 m) and a top-down approach based on fire radiative power (FRP). Emissions were estimated through the integration of FRP to fire radiative energy (FRE), conversion to dry matter burned using biome-specific combustion coefficients, and application of emission factors for carbon dioxide. A parallel MODIS-based dataset was also produced using the same methodology to isolate sensor-specific effects. To evaluate detection and modelling differences, the VIIRS-based product (VIIRS-EM) was compared against six widely used global fire emission inventories. In addition, a subset of emissions from small fires (defined as FRP < 10 MW) was derived and assessed separately. Over the period 2013–2022, VIIRS-EM estimated average annual carbon emissions of 3.0 Pg C, which is 50% to 75% higher than most MODIS-based inventories. Emission hotspots were identified in agricultural and savanna regions, particularly in West and Central Africa. Small fires contributed significantly to early and late fire-season emissions and revealed widespread underestimation in existing products. Our findings underscore the importance of high-resolution detection and FRP-based modelling for capturing the full extent of African fire activity. The VIIRS-EM inventory provides improved spatial and temporal resolution, with implications for atmospheric composition modelling, greenhouse gas accounting, and regional fire policy development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".