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Comparison of fuel consumption (FC) estimates derived by combining FRP and burned-area data. (a) Fuel consumption derived using SEVIRI FRP data. (b) Correlation between fuel consumption estimates based on the SEVIRI and MODIS FRP data. (c) Fuel consumption derived using MODIS FRP data. (d) MODIS-based fuel consumption estimates as a percentage of the SEVIRI-based estimates. For comparison both SEVIRI-and MODIS-based estimates are shown for the same period (2010–2014) and the MODIS FRE data are uncorrected (see Sect. 4.2). Note that on average MODIS-derived FC is about twice as large as SEVIRI-derived FC. Grid cells with dominant land cover " forest " or " bare or sparsely vegetated " were excluded from our analysis and are masked grey, while water and grid cells with less than 50 MODIS FRP detections are shown in white in all figures.  

Comparison of fuel consumption (FC) estimates derived by combining FRP and burned-area data. (a) Fuel consumption derived using SEVIRI FRP data. (b) Correlation between fuel consumption estimates based on the SEVIRI and MODIS FRP data. (c) Fuel consumption derived using MODIS FRP data. (d) MODIS-based fuel consumption estimates as a percentage of the SEVIRI-based estimates. For comparison both SEVIRI-and MODIS-based estimates are shown for the same period (2010–2014) and the MODIS FRE data are uncorrected (see Sect. 4.2). Note that on average MODIS-derived FC is about twice as large as SEVIRI-derived FC. Grid cells with dominant land cover " forest " or " bare or sparsely vegetated " were excluded from our analysis and are masked grey, while water and grid cells with less than 50 MODIS FRP detections are shown in white in all figures.  

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Landscape fires occur on a large scale in (sub)tropical savannas and grasslands, affecting ecosystem dynamics, regional air quality and concentrations of atmospheric trace gasses. Fuel consumption per unit of area burned is an important but poorly constrained parameter in fire emission modelling. We combined satellite-derived burned area with fire...

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... provide new insights into the specific qualities and lim- itations of polar-orbiting and geostationary-based FRP data, we compared the mean fuel consumption (kg m −2 ) estimates based on our approach using SEVIRI FRP data ( Fig. 2a) with our approach using MODIS FRP data (Fig. 2c). Al- though later on the MODIS-based FRE estimates are cali- brated against field measurements, here we use the uncor- rected FRE estimates to provide insights into the effect of sensor characteristics and our methods on absolute FRE es- timates. We used linear regression fitted through ...
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... provide new insights into the specific qualities and lim- itations of polar-orbiting and geostationary-based FRP data, we compared the mean fuel consumption (kg m −2 ) estimates based on our approach using SEVIRI FRP data ( Fig. 2a) with our approach using MODIS FRP data (Fig. 2c). Al- though later on the MODIS-based FRE estimates are cali- brated against field measurements, here we use the uncor- rected FRE estimates to provide insights into the effect of sensor characteristics and our methods on absolute FRE es- timates. We used linear regression fitted through the origin (Fig. 2b) in order to compare the ...
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... with our approach using MODIS FRP data (Fig. 2c). Al- though later on the MODIS-based FRE estimates are cali- brated against field measurements, here we use the uncor- rected FRE estimates to provide insights into the effect of sensor characteristics and our methods on absolute FRE es- timates. We used linear regression fitted through the origin (Fig. 2b) in order to compare the results. Total estimated FRE, and thus fuel consumption, based on the MODIS in- struments was roughly two times larger than SEVIRI-derived fuel consumption. On top of these absolute differences, the spatial patterns were not uniform ( Fig. 2b and d), for which we identified two main causes: first, the ...
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... absolute FRE es- timates. We used linear regression fitted through the origin (Fig. 2b) in order to compare the results. Total estimated FRE, and thus fuel consumption, based on the MODIS in- struments was roughly two times larger than SEVIRI-derived fuel consumption. On top of these absolute differences, the spatial patterns were not uniform ( Fig. 2b and d), for which we identified two main causes: first, the MODIS-based fuel consumption was consistently higher in south-eastern Africa (e.g. Mozambique and Madagascar), likely because of the decreasing sensitivity of the SEVIRI instrument at the greater off-nadir angle over this region (e.g. Freeborn et al., 2014b), and second, the relative ...
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... the relative fraction of FRE emitted during peri- ods that FRP values were below the SEVIRI detection thresh- old, a function of the absolute FRP values and the shape of the fire diurnal cycle. Fires with high FRP (related to high fire spread rates and/or fuel consumption) are often equally well observed by both instruments (i.e. red colouring in Fig. 2d), while areas with low fuel consumption are often character- ized by a larger differences between the MODIS and SE- VIRI estimates (i.e. green colouring in Fig. 2d). To prevent these differences from affecting our estimated correlation too much, we only included frequently burning grid cells (burned area ≥ 15 % yr −1 ) and those that ...
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... of the fire diurnal cycle. Fires with high FRP (related to high fire spread rates and/or fuel consumption) are often equally well observed by both instruments (i.e. red colouring in Fig. 2d), while areas with low fuel consumption are often character- ized by a larger differences between the MODIS and SE- VIRI estimates (i.e. green colouring in Fig. 2d). To prevent these differences from affecting our estimated correlation too much, we only included frequently burning grid cells (burned area ≥ 15 % yr −1 ) and those that have a surface area of the SEVIRI FRP-PIXEL product grid cells below 12 km 2 (min- imum value is 9 km 2 at nadir) during the linear regression shown in Fig. 2b. This ...
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... colouring in Fig. 2d). To prevent these differences from affecting our estimated correlation too much, we only included frequently burning grid cells (burned area ≥ 15 % yr −1 ) and those that have a surface area of the SEVIRI FRP-PIXEL product grid cells below 12 km 2 (min- imum value is 9 km 2 at nadir) during the linear regression shown in Fig. 2b. This resulted in reasonable correlation (r 2 = 0.42; n = 6569). Partial cloud cover and missing data were also affecting the analysis, and we found that 54 % of the annual burned area occurred during periods of reduced data availability (below 80 % during the 15-day time win- dow). When excluding these events, the absolute difference ...
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... and missing data were also affecting the analysis, and we found that 54 % of the annual burned area occurred during periods of reduced data availability (below 80 % during the 15-day time win- dow). When excluding these events, the absolute difference between MODIS-and SEVIRI-based fuel consumption be- came somewhat smaller (i.e. the slope in Fig. 2b became 0.59), demonstrating that periods of reduced observations were partly responsible for the underestimation in SEVIRI- derived fuel consumption. However, by excluding this 54 % of the data, the correlation between MODIS-and SEVIRI- based fuel consumption was reduced (r 2 = 0.28) due to the heterogeneous nature of fuel ...
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... understanding of the im- plications of these differences, we compared the fuel con- sumption estimates based on both platforms using the FRE- to-DM-burned conversion factor found during laboratory ex- periments ( Wooster et al., 2005). At first sight, very similar spatial patterns were found using polar-orbiting or geosta-tionary data (compare Fig. 2a and c), providing confidence in the spatial distribution of the fuel consumption estimates. However, many differences were also present (Fig. 2d). We found that a large part of the differences could be attributed to the different sensors characteristics and methods used here. The shape of the fire diurnal cycle, for example, affects both ...
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... to-DM-burned conversion factor found during laboratory ex- periments ( Wooster et al., 2005). At first sight, very similar spatial patterns were found using polar-orbiting or geosta-tionary data (compare Fig. 2a and c), providing confidence in the spatial distribution of the fuel consumption estimates. However, many differences were also present (Fig. 2d). We found that a large part of the differences could be attributed to the different sensors characteristics and methods used here. The shape of the fire diurnal cycle, for example, affects both MODIS-based fuel consumption estimates due to the lim- ited number of daily overpasses but also the SEVIRI-derived fuel consumption estimates ...
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... et al., 2009, 2014b, Roberts and Wooster, 2008). In our analysis a small part of the structural difference could also be explained by the fact that we did not correct for cloud cover and/or missing data in the SEVIRI-based FC estimates. Not surprisingly, the best com- parison between both methods was found in areas of high fuel consumption rates (Fig. 2d), for example areas where fires can spread over large areas to form large fire fronts ( Archibald et al., 2013), and areas of high fuel consumption; these fires with high FRP are likely to be well observed by both ...

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... With the growing use of the FRP, it is possible to retrieve FC by reverse calculation of the amount of combusted fuel required to produce the measured energy. Thus, this methodology has been applied at continental [67,68] and global [69] scales despite the fact that the FRP-derived FC has been found to be typically lower than those used in "bottom-up" methodologies such as GFED [67][68][69]. Moreover, differences have been reported in the agreement between FRP-derived FC and GFED estimates across the southern and northern hemispheres of Africa [70]. ...
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