# Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa
## 4 Results
### MOD17 Productivity Assessment
The matching FRE estimates were calculated via temporal integration of the SEVIRI FRP data between the first and last day the pixel was detected as being burned in the current year, with a 10-day buffer added to account for any offset between the burned area and active fire observations (e.g., due to cloud cover or differences in fire detectability using the burned area and active fire approaches; references [67; 68; 26]). FRE-derived fuel consumption for each fire cluster was then estimated using the conversion coefficient of 0.37 kg MJ\({}^{-1}\) found by reference [20], which provided good relations between fuel availability and FRE-derived fuel consumption metrics in reference [39]. A timeseries of SEVIRI FRP retrievals and the mean MODIS PSN and GPP estimates from a single 57.7 km\({}^{2}\) fire which burned in an area of woody savanna are shown in Figure 4 (a and b respectively). This fire affected an area of 18 discrete SEVIRI pixels over its lifetime (with a mean of 22 FRP observations per pixel), and the accumulated PSN and total fuel consumed over the 57.7 km\({}^{2}\) burned area were calculated as 96,950 and 13,700 tonnes respectively. The FRE-derived fuel consumption per unit area is 0.23 kg DM m\({}^{-2}\) which is somewhat lower than field measurements made in African savannas which ranges between 0.29-0.45 kg DM m\({}^{-2}\)[36]. Figure 4: **(a)** Daily fire radiative power (FRP, MW) timeseries from a fire cluster that first burned in 2007 and then again in 2008 in an area of woody savanna in southern hemisphere Africa. The red vertical lines indicate the time period between which FRP retrievals are integrated to estimate the FRE and fuel consumption on the basis of the burned area detection dates. Note that only the time periods where fire activity was detected in each year are shown. **(b)** Mean fire cluster productivity (MODIS Gross Primary Productivity (GPP) and PSN; kg C m\({}^{-2}\) day\({}^{-1}\)). The dashed grey lines indicate the DOY where the fire cluster was last detected as burning in 2007 and first detected as burning in 2008 (according to the MCD64 product), between which the PSN were accumulated. Active fire detection and FRP retrievals are influenced by a number of factors including sensor characteristics, obscuration due to cloud/smoke or an upper canopy, fire size, fuel moisture content and flame emissivity [59; 69; 70; 71]. One approach to mitigate for certain of these issues is to focus analyses on \"well-observed\" active fire clusters. Using data between 2005 and 2016, Figure 5 illustrates the relationship between FRE-derived fuel consumption (Tg DM) and accumulated MODIS PSN (Tg) for fire clusters in the northern (Figure 5a) and southern (Figure 5b) African hemispheres as a function of the mean number of per-pixel SEVIRI FRP retrievals in each cluster. Clusters with, on average, fewer than 10 FRP retrievals per-pixel exhibit greater variability whilst fires observed with an average of \(>\)10 observations display a much stronger relationship. This is clear in the northern hemisphere which displays a weaker relationship and greater variability than found in the southern hemisphere. For the remainder of the analyses presented, only fire clusters where pixels are observed on average \(\geq\)10 times are retained, omitting 46% and 48% of the fire clusters in the northern and southern hemispheres respectively. Figure 5: _Cont._  
Figure 6 shows scatterplots between FRE-derived fuel consumption and integrated PSN in the northern and southern African hemispheres for the remaining fire clusters for each year (2005-2015). In the northern and southern hemispheres, the average annual number of fire clusters per year is 2040 and 2588 respectively. Overall, there is little interannual variation in terms of changes in the correlation and slope of the relation between FRE-derived fuel consumption and accumulated PSN.