Changes in Fire Activity in Africa from 2002 to 2016 and Their Potential Drivers

Global biomass fires and infant mortality

Global outdoor biomass burning is a major contributor to air pollution, especially in low- and middle-income countries. Recent years have witnessed substantial changes in the extent of biomass burning, including large declines in Africa. However, direct evidence of the contribution of biomass burning to global health outcomes remains limited. Here, we use georeferenced data on more than 2 million births matched to satellite-derived burned area exposure to estimate the burden of biomass fires on infant mortality. We find that each additional square kilometer of burning is associated with nearly 2% higher infant mortality in nearby downwind locations. The share of infant deaths attributable to biomass fires has increased over time due to the rapid decline in other important causes of infant death. Applying our model estimates across harmonized district-level data covering 98% of global infant deaths, we find that exposure to outdoor biomass burning was associated with nearly 130,000 additional infant deaths per year globally over our 2004 to 2018 study period. Despite the observed decline in biomass burning in Africa, nearly 75% of global infant deaths due to burning still occur in Africa. While fully eliminating biomass burning is unlikely, we estimate that even achievable reductions-equivalent to the lowest observed annual burning in each location during our study period-could have avoided more than 70,000 infant deaths per year globally since 2004.

Historical spatiotemporal changes in fire danger potential across biomes

This study 1) identifies the seasons and biomes that exhibit significant (1980-2019) changes in fire danger potential, as quantified by the Canadian Fire Weather Index (FWI); 2) explores what types of fire behavior potentials may be contributing to changes in fire danger potential, as quantified by the United States Energy Release Component (ERC) and the Ignition Component (IC); 3) provides spatiotemporal insight on how fire danger potential and fire behavior potential are responding in relation to changes in seasonal precipitation totals and seasonal mean air temperature across biomes. Time series of these fire potentials, as well as seasonal mean temperature, and seasonal precipitation totals are generated using data from the 0.25° ECMWF spatial resolution Reanalysis 5th Generation (ERA5) and the Climatic Research Unit gridded Time Series (CRU TS). The Mann-Kendall test is then applied to identify significant spatiotemporal trends across each biome. Results indicate that the September-November season (SON) exhibits the greatest rate of increase in fire danger potential, followed by the June-August season (JJA), December, January-February season (DJF), and March-May season (MAM), and this is predominant over the Tropical and Subtropical Moist Broadleaf Forest Biome, as well as all vegetation types of the temperate biomes. Similarly, the temperate biomes experience the greatest rate of increase in fire intensity potential and ignition potential, but prevalent during the DJF and MAM seasons. Furthermore, there is a significant positive correlation between fire danger potential and seasonal mean air temperature during JJA in the Northern Hemisphere for the temperate biomes in North America and Europe, as well as the Tropical and Subtropical biomes in Africa. Our analysis provides quantitative insight as to how fire danger potential and fire behavior potential have been responding to changes in seasonal mean temperature and seasonal precipitation totals across different ecoregions around the world.

Climate and socioeconomic drivers of biomass burning and carbon emissions from fires in tropical dry forests: A Pantropical analysis

Global burned area has declined by nearly one quarter between 1998 and 2015. Drylands contain a large proportion of these global fires but there are important differences within the drylands, for example, savannas and tropical dry forests (TDF). Savannas, a biome fire-prone and fire-adapted, have reduced the burned area, while the fire in the TDF is one of the most critical factors impacting biodiversity and carbon emissions. Moreover, under climate change scenarios TDF is expected to increase its current extent and raise the risk of fires. Despite regional and global scale effects, and the influence of this ecosystem on the global carbon cycle, little effort has been dedicated to studying the influence of climate (seasonality and extreme events) and socioeconomic conditions of fire regimen in TDF. Here we use the Global Fire Emissions Database and, climate and socioeconomic metrics to better understand long-term factors explaining the variation in burned area and biomass in TDF at Pantropical scale. On average, fires affected 1.4% of the total TDF' area (60,208 km² ) and burned 24.4% (259.6 Tg) of the global burned biomass annually at Pantropical scales. Climate modulators largely influence local and regional fire regimes. Inter-annual variation in fire regime is shaped by El Niño and La Niña. During the El Niño and the forthcoming year of La Niña, there is an increment in extension (35.2% and 10.3%) and carbon emissions (42.9% and 10.6%). Socioeconomic indicators such as land-management and population were modulators of the size of both, burned area and carbon emissions. Moreover, fires may reduce the capability to reach the target of "half protected species" in the globe, that is, high-severity fires are recorded in ecoregions classified as nature could reach half protected. These observations may contribute to improving fire-management.

Building a small fire database for Sub-Saharan Africa from Sentinel-2 high-resolution images

Coarse resolution sensors are not very sensitive at detecting small fire patches, making current estimations of global burned areas (BA) very conservative. Using medium or high-resolution sensors to generate BA products becomes then a priority, particularly in areas where fires tend to be small and frequent. Building on previous work that developed a small fire dataset (SFD) for Sub-Saharan Africa for 2016, this paper presents a new version of the dataset for 2019 using the two Sentinel-2 satellites (A and B) and VIIRS active fires. Total estimated BA was 4.8 Mkm². This value was much higher than estimations from two global, coarser-spatial resolution BA products based on MODIS data for the same area and period: 80 % greater than estimates from FireCCI51 (based on MODIS 250 m bands) and 120 % larger than MCD64A1 (based on MODIS 500 m bands). The main differences were observed in those months with higher fire occurrence (November to January for the Northern Hemisphere regions and June to September for the Southern Hemisphere ones). Accuracy assessment of the SFD product was based on a novel sampling strategy designed to obtain independent fire reference perimeters. Validation results showed remarkable high accuracy values comparing to existing global BA products. Overall omission errors (OE) were estimated as 8.5 %, commission errors (CE) as 15.0 %, with a Dice Coefficient of 87.7 %. All of these estimations implied significant improvements over the global, coarser spatial resolution BA products (OE > 50 % and CE > 20 % for the same area and period), as well as over the previous SFD product for 2016 of the same area, generated from a single Sentinel-2 satellite and MODIS active fires (OE = 26.5 % and CE = 19.3 %). Temporal accuracies greatly increased as well with the new product, with 92.5 % of fires detected within the first 10 days of occurrence.

Air Pollution Dispersion over Durban, South Africa

Air pollution dispersion over Durban is studied using satellite, reanalysis and in situ measurements. This coastal city of 4 million people located on the east coast of South Africa contributes 29 million T/yr of trace gases, mostly from transport and industry. Terrestrial and agricultural particulates derive from the Kalahari Desert, Zambezi Valley and Mozambique. Surface air pollutants accumulate during winter (May–August) and provide a focus for statistical analysis of monthly, daily and hourly time series since 2001. The mean diurnal cycle has wind speed minima during the land−sea breeze transitions that follow morning and evening traffic emissions. Daily air pollution concentrations (CO, NO2, O3, PM2.5 and SO2) vary inversely with dewpoint temperature and tend to peak during winter prefrontal weather conditions. Descending airflow from the interior highlands induces warming, drying and poor air quality, bringing dust and smoke plumes from distant sources. Spatial regression patterns indicate that winters with less dispersion are preceded by warm sea surface temperatures in the tropical West Indian Ocean that promote a standing trough near Durban. Statistical outcomes enable the short- and long-range prediction of atmospheric dispersion and risk of exposure to unhealthy trace gases and particulates. The rapid inland decrease of mean wind speed from 8 to 2 m/s suggests that emissions near the coast will disperse readily compared with in interior valleys.

Characterizing Global Fire Regimes from Satellite-Derived Products

We identified four global fire regimes based on a k-means algorithm using five variables covering the spatial, temporal and magnitude dimensions of fires, derived from 19-year long satellite burned area and active fire products. Additionally, we assessed the relation of fire regimes to forest fuels distribution. The most extensive fire regime (35% of cells having fire activity) was characterized by a long fire season, medium size fire events, small burned area, high intensity and medium variability. The next most extensive fire regime (25.6%) presented a long fire season, large fire events and the highest mean burned area, yet it showed the lowest intensity and the least variability. The third group (22.07%) presented a short fire season, the lowest burned area, with medium-low intensity, the smallest fire patches and large variability. The fourth group (17.3%) showed the largest burned area with large fire patches of moderate intensity and low variability. Fire regimes and fuel types showed a statistically significant relation (CC = 0.58 and CC’ = 0.67, p < 0.001), with most fuel types sustaining all fire regimes, although a clear prevalence was observed in some fuel types. Further efforts should be directed towards the standardization of the variables in order to facilitate comparison, analysis and monitoring of fire regimes and evaluate whether fire regimes are effectively changing and the possible drivers.

Letter to the editor on "Nonlinear dynamics of fires in Africa over recent decades controlled by precipitation"

Time series of burned area versus cropland land cover type in sub-Saharan Africa do not demonstrate an inverse relationship between fire activity and cropland expansion in the most fire-prone continent, as was suggested by several authors.

Alternative biome states of African terrestrial vegetation and the potential drivers: A continental-scale study

The alternative stable state (ASS) theory provides a plausible framework to explain the spatial distribution of biomes and their dynamics. Existing studies to test the alternative biome states (ABSs) mainly focused on tree-dominated biomes. It is still uncertain whether ABSs are present in a wide range of terrestrial biomes. This study was to examine the ABSs in the terrestrial vegetated areas of Africa and the maintaining factors. The potential landscapes were reconstructed separately for forest, grassland and shrubland using the MODIS Vegetation Continuous Field data along the gradients of temperature, precipitation and aridity index (AI, the ratio of precipitation to potential evapotranspiration). The differences of soil organic carbon density (SOCD), fire count and grazing intensity were compared to test the feedback hypothesis to maintain the ABSs. The results showed that AI performed well in detecting the ABSs at the continental scale of Africa. Forest (at the wetter end) and shrubland (at the drier end) were well separated along the AI axis. Forest had three stable states (i.e. closed forest, woody savanna, and savanna) and shrubland had two stable states (i.e. closed shrubland and open shrubland). Grassland had two stable states (i.e. dense grassland and sparse grassland) distributing in a large AI range. The stable states that shared a specific AI range were regarded as the ABSs. Climate aridity greatly determined the distribution of the ABSs but the positive feedbacks between vegetation and SOCD, fire count, and livestock density played potential roles in driving the shifts between the ABSs. Our study indicated that the ABSs commonly existed in varied biomes (both tree-dominated and non-tree-dominated) in the African continent, which provided an enlarged picture of the ABSs of the terrestrial biomes. The findings contribute to a deeper understanding of large scale vegetation patterns and their dynamics and facilitate to macro management of the terrestrial ecosystems in facing the possible regime shifts of the biomes.

Increasing forest fire emissions despite the decline in global burned area

Satellites have detected a global decline in burned area of grassland, coincident with a small increase in burned forest area. These contrasting trends have been reported in earlier literature; however, less is known of their impacts on global fire emission trends due to the scarcity of direct observations. We use an atmospheric inversion system to show that global fire emissions have been stable or slightly decreasing despite the substantial decline in global burned area over the past two decades caused by the carbon dioxide emission increase from forest fires offsetting the decreasing emissions from grass and shrubland fires. Forest fires are larger carbon dioxide sources per unit area burned than grassland fires, with a slow or incomplete follow-up recovery—sometimes no recovery due to degradation and deforestation. With fires expanding over forest areas, the slow recovery of carbon dioxide uptake over burned forest lands weakens land sink capacity, implying positive feedback on climate change.

Seasonal variability and trends in global type-segregated aerosol optical depth as revealed by MISR satellite observations

This study utilized a long-term (2001-2018) aerosol optical component dataset retrieved from the Multiangle Imaging Spectroradiometer (MISR), Version 23, to perform comprehensive analyses of the global climatology of seasonal AODs, partitioned by aerosol types (including small-size, medium-size, large-size, spherical, and non-spherical). By dividing eight different AOD bins and performing trend analysis, the seasonal variability and trends in these type-segregated AODs, as well as in the frequency occurrences (FOs) for different AOD bins, globally and over 12 regions of interest, were also investigated. In terms of particle size, small-size aerosol particles (diameter < 0.7 μm) contribute the largest to global extinction in all three seasons except winter. A similar globally dominant role is exhibited by spherical aerosols, which contribute 68.5%, 73.3%, 71.6% and 70.2% to the global total AOD (TAOD) in spring, summer, autumn and winter, respectively, on a global average scale. FOs with different aerosol loading levels suggested that the seasonal FOs tend to decrease progressively with increasing aerosol loading, except for Level 1 (TAOD< 0.05). Examination of the seasonal distribution of FOs revealed that the FO at Level 1 (Level 2, 0.05 < TAOD< 0.15) is much larger in summer/winter (winter/autumn) than in spring/autumn (spring/summer) over most areas of the world. However, the FOs for Level 3 (0.15 < TAOD< 0.25) to Level 8 (TAOD> 1.0) generally exhibit greater intensity in spring/summer than in autumn/winter. Temporal trend analyses showed that the seasonal TAOD experiences a significant decline during 2001-2018 in most regions globally, except in South Asia, the Middle East, and North Africa. Opposite seasonal trends in the above regions are closely related to the increase in FOs in the range of 0.4 < TAOD< 1.0. The global average TAOD shows the most pronounced decline in spring, falling by -10.4% (P < 0.05). Examination of the trends in type-segregated AODs further revealed that the decreases in size-segregated (shape-segregated) AODs all contribute to the decrease in seasonal TAOD, with small-size AOD (spherical AOD) contributing most significantly.

Human and climate drivers of global biomass burning variability

Biomass burning is one of the most critical factors impacting vegetation and atmospheric trends, with important societal implications, particularly when extreme weather conditions occur. Trends and factors of burned area (BA) have been analysed at regional and global scales, but little effort has been dedicated to study the interannual variability. This paper aimed to better understand factors explaining this variation, under the assumption that the more human control of fires the more frequently they occur, as burnings will be less dependent of weather cycles. Interannual variability of BA was estimated from the coefficient of variation of the annual BA (BA_CV) estimated from satellite data at 250 m, covering the period from 2001 to 2018. These data and the explanatory variables were resampled at 0.25-degree resolution for global analysis. Relations between this variable and explanatory factors, including human and climate drivers, were estimated using Random Forest (RF) and generalized additive models (GAM). BA_CV was negatively related to BA_Mean, implying that areas with higher average BA have lower variability as well. Interannual BA variability decreased when maximum temperature (TMAX) and actual and potential evapotranspiration (AET, PET) increased, cropland and livestock density increased and the human development index (HDI) values decreased. GAM models indicated interesting links with AET, PET and precipitation, with negative relation with BA_CV for the lower ranges and positive for the higher ones, the former indicating fuel limitations of fire activity, and the latter climate constrains. For the global RF model, TMAX, AET and HDI were the main drivers of interannual variability. As originally hypothesised, BA_CV was more dependent on human factors (HDI) in those areas with medium to large BA occurrence, particularly in tropical Africa and Central Asia, while climatic factors were more important in boreal regions, but also in the tropical regions of Australia and South America.

Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation

Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land-surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large-scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in several regions by 14%-44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%-27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large-scale circulation patterns featuring less active upper-level ridge-trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US.

Nutrient content and stoichiometry of pelagic Sargassum reflects increasing nitrogen availability in the Atlantic Basin

The pelagic brown macroalgae Sargassum spp. have grown for centuries in oligotrophic waters of the North Atlantic Ocean supported by natural nutrient sources, such as excretions from associated fishes and invertebrates, upwelling, and N2 fixation. Using a unique historical baseline, we show that since the 1980s the tissue %N of Sargassum spp. has increased by 35%, while %P has decreased by 44%, resulting in a 111% increase in the N:P ratio (13:1 to 28:1) and increased P limitation. The highest %N and δ15N values occurred in coastal waters influenced by N-rich terrestrial runoff, while lower C:N and C:P ratios occurred in winter and spring during peak river discharges. These findings suggest that increased N availability is supporting blooms of Sargassum and turning a critical nursery habitat into harmful algal blooms with catastrophic impacts on coastal ecosystems, economies, and human health.

Tropical Indo-Pacific SST influences on vegetation variability in eastern Africa

Mechanisms by which tropical Pacific and Indian Ocean sea surface temperatures (SST) influence vegetation in eastern Africa have not been fully explored. Here, we use a suite of idealized Earth system model simulations to elucidate the governing processes for eastern African interannual vegetation changes. Our analysis focuses on Tanzania. In the absence of ENSO-induced sea surface temperature anomalies in the Tropical Indian Ocean (TIO), El Niño causes during its peak phase negative precipitation anomalies over Tanzania due to a weakening of the tropical-wide Walker circulation and anomalous descending motion over the Indian Ocean and southeastern Africa. Resulting drought conditions increase the occurrence of wildfires, which leads to a marked decrease in vegetation cover. Subsequent wetter La Niña conditions in boreal winter reverse the phase in vegetation anomalies, causing a gradual 1-year-long recovery phase. The 2-year-long vegetation decline in Tanzania during an ENSO cycle can be explained as a double-integration of the local rainfall anomalies, which originate from the seasonally-modulated ENSO Pacific-SST forcing (Combination mode). In the presence of interannual TIO SST forcing, the southeast African precipitation and vegetation responses to ENSO are muted due to Indian Ocean warming and the resulting anomalous upward motion in the atmosphere.

Reductions in NO2 burden over north equatorial Africa from decline in biomass burning in spite of growing fossil fuel use, 2005 to 2017

Socioeconomic development in low- and middle-income countries has been accompanied by increased emissions of air pollutants, such as nitrogen oxides [NO_x: nitrogen dioxide (NO₂) + nitric oxide (NO)], which affect human health. In sub-Saharan Africa, fossil fuel combustion has nearly doubled since 2000. At the same time, landscape biomass burning-another important NO_x source-has declined in north equatorial Africa, attributed to changes in climate and anthropogenic fire management. Here, we use satellite observations of tropospheric NO₂ vertical column densities (VCDs) and burned area to identify NO₂ trends and drivers over Africa. Across the northern ecosystems where biomass burning occurs-home to hundreds of millions of people-mean annual tropospheric NO₂ VCDs decreased by 4.5% from 2005 through 2017 during the dry season of November through February. Reductions in burned area explained the majority of variation in NO₂ VCDs, though changes in fossil fuel emissions also explained some variation. Over Africa's biomass burning regions, raising mean GDP density (USD⋅km^-2) above its lowest levels is associated with lower NO₂ VCDs during the dry season, suggesting that economic development mitigates net NO₂ emissions during these highly polluted months. In contrast to the traditional notion that socioeconomic development increases air pollutant concentrations in low- and middle-income nations, our results suggest that countries in Africa's northern biomass-burning region are following a different pathway during the fire season, resulting in potential air quality benefits. However, these benefits may be lost with increasing fossil fuel use and are absent during the rainy season.

Improving prediction and assessment of global fires using multilayer neural networks

Fires determine vegetation patterns, impact human societies, and are a part of complex feedbacks into the global climate system. Empirical and process-based models differ in their scale and mechanistic assumptions, giving divergent predictions of fire drivers and extent. Although humans have historically used and managed fires, the current role of anthropogenic drivers of fires remains less quantified. Whereas patterns in fire-climate interactions are consistent across the globe, fire-human-vegetation relationships vary strongly by region. Taking a data-driven approach, we use an artificial neural network to learn region-specific relationships between fire and its socio-environmental drivers across the globe. As a result, our models achieve higher predictability as compared to many state-of-the-art fire models, with global spatial correlation of 0.92, monthly temporal correlation of 0.76, interannual correlation of 0.69, and grid-cell level correlation of 0.60, between predicted and observed burned area. Given the current socio-anthropogenic conditions, Equatorial Asia, southern Africa, and Australia show a strong sensitivity of burned area to temperature whereas northern Africa shows a strong negative sensitivity. Overall, forests and shrublands show a stronger sensitivity of burned area to temperature compared to savannas, potentially weakening their status as carbon sinks under future climate-change scenarios.

Fire decline in dry tropical ecosystems enhances decadal land carbon sink

The terrestrial carbon sink has significantly increased in the past decades, but the underlying mechanisms are still unclear. The current synthesis of process-based estimates of land and ocean sinks requires an additional sink of 0.6 PgC yr-1 in the last decade to explain the observed airborne fraction. A concurrent global fire decline was observed in association with tropical agriculture expansion and landscape fragmentation. Here we show that a decline of 0.2 ± 0.1 PgC yr-1 in fire emissions during 2008-2014 relative to 2001-2007 also induced an additional carbon sink enhancement of 0.4 ± 0.2 PgC yr-1 attributable to carbon cycle feedbacks, amounting to a combined sink increase comparable to the 0.6 PgC yr-1 budget imbalance. Our results suggest that the indirect effects of fire, in addition to the direct emissions, is an overlooked mechanism for explaining decadal-scale changes in the land carbon sink and highlight the importance of fire management in climate mitigation.

Predicting the influence of climate on grassland area burned in Xilingol, China with dynamic simulations of autoregressive distributed lag models

The influence of climate change on wildland fire has received considerable attention, but few studies have examined the potential effects of climate variability on grassland area burned within the extensive steppe land of Eurasia. We used a novel statistical approach borrowed from the social science literature—dynamic simulations of autoregressive distributed lag (ARDL) models—to explore the relationship between temperature, relative humidity, precipitation, wind speed, sunlight, and carbon emissions on grassland area burned in Xilingol, a large grassland-dominated landscape of Inner Mongolia in northern China. We used an ARDL model to describe the influence of these variables on observed area burned between 2001 and 2018 and used dynamic simulations of the model to project the influence of climate on area burned over the next twenty years. Our analysis demonstrates that area burned was most sensitive to wind speed and temperature. A 1% increase in wind speed was associated with a 20.8% and 22.8% increase in observed and predicted area burned respectively, while a 1% increase in maximum temperature was associated with an 8.7% and 9.7% increase in observed and predicted future area burned. Dynamic simulations of ARDL models provide insights into the variability of area burned across Inner Mongolia grasslands in the context of anthropogenic climate change.

Global validation of the collection 6 MODIS burned area product

This paper presents a Stage 3 validation of the recently released Collection 6 NASA MCD64A1 500 m global burned area product. The product is validated by comparison with Landsat 8 Operational Land Imager (OLI) image pairs acquired 16 days apart that were visually interpreted. These independent reference data were selected using a stratified random sampling approach that allows for probability sampling of Landsat data in both time and in space. A total of 558 Landsat 8 OLI image pairs (1116 images), acquired between March 1^st, 2014 and March 19^th , 2015, were selected and used to validate the MCD64A1 product. The areal accuracy of the MCD64A1 product was characterized at the 30 m resolution of the Landsat independent reference data using standard accuracy metrics derived from global and from biome specific confusion matrices. Because a probability based Stage 3 sampling protocol was followed, unbiased estimators of the accuracy metrics and associated standard errors could be used. Globally, the MCD64A1 product had an estimated 40.2% commission error and 72.6% omission error; the prevalence of omission errors is reflected by a negative estimated bias of the mapped global area burned relative to the Landsat independent reference data (-54.1%). Globally, the standard errors of the accuracy metrics were less than 6%. The lowest errors were observed in the boreal forest biome (27.0% omission and 23.9% estimated commission errors) where burned areas tend to be large and distinct, and remain on the landscape for long periods, and the highest errors were in the Tropical Forest, Temperate Forest, and Mediterranean biomes (estimated > 90% omission error and > 50% commission error). The product accuracy was also characterized at coarser scale using metrics derived from the regression between the proportion of coarse resolution grid cells detected as burned by MCD64A1 and the proportion mapped in the Landsat 8 interpreted maps. The errors of omission and commission observed at 30 m resolution compensate to a considerable extent at coarser resolution, as indicated by the coefficient of determination (r² > 0.70), slope (> 0.79) and intercept (-0.0030) of the regression between the MCD64A1 product and the Landsat independent reference data in 3 km, 4 km, 5 km, and 6 km coarse resolution cells. The Boreal Forest, Desert and Xeric Shrublands, Temperate Savannah and Tropical Savannah biomes had higher r ² and slopes closer to unity than the Temperate Forest, Mediterranean, and Tropical Forest biomes. The analysis of the deviations between the proportion of area burned mapped by the MCD64A1 product and by the independent reference data, performed using 3 km × 3 km and 6 km × 6 km coarse resolution cells, indicates that the large negative bias in global area burned is primarily due to the systematic underestimation of smaller burned areas in the MCD64A1 product.