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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".