# Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa
## 4 Results
### MOD17 Productivity Assessment
Despite constraining the fire clusters to those which are best observed (i.e., \(\geq\)10 observations on average per-pixel), the FRE-derived fuel consumption estimates appear underestimated. [PERSON] et al., [26] compared MODIS and SEVIRI FRE-derived fuel consumption estimates (g DM m\({}^{-2}\)) and found those from SEVIRI were around three times lower than those from spatially coincident ground measurements. Applying this adjustment to fire cluster fuel consumption estimates for savanna, grassland and cropland in southern hemisphere Africa increases it to 0.33 (kg DM m\({}^{-2}\)) at the 25 th percentile. This is closer to the range of fuel consumption estimates found in African grassland savanna (0.21-0.65 kg DM m\({}^{-2}\)) and woody savanna (0.29-0.73 kg DM m\({}^{-2}\); reference [36]), though the slope characterising the relationship  
Figure 9: MCD64A1 burned area derived fire occurrence (2004–2016) at 0.25\({}^{\circ}\) resolution using MCD64 A1 burned area product between 2004 and 2016.  
between FRE-derived fuel consumption and accumulated PSN for fire clusters in southern hemisphere Africa in savanna, grassland and cropland remains far below unity (at 0.23, 0.23 and 0.15 respectively). A number of factors contribute to the underestimation of FRE-derived fuel consumption including FRP retrieval, fire detection bias and fuel characteristics. SEVIRI FRP has been compared to spatially and temporally coincident MODIS FRP for fire clusters >50 MW where over half (57%) were within 20% of the corresponding MODIS FRP [42]. However, whilst good agreement is found on sufficiently intense fires, SEVIRI underestimates fire activity over a wider area due to the omission of small and/or low intensity fire which fall below its detection limit [41; 42], or are omitted due to cloud mask over efficiency for example [69]. Sensor imaging characteristics can also impact the retrieved FRP. For example, the point spread function (PSF) distributes fire emitted radiance into neighbouring pixels, and may result in these not being detected or falling outside of the MODIS burned area [78; 79]. Vegetation structure can also reduce the retrieved FRP through preventing the detection of actively burning pixels [65; 69] and intercepting the fire emitted radiance from fires beneath an upper canopy [34]. Fuel moisture content also has an impact since energy is lost to vapourisation which reduces the potential for combustion and, should combustion occur, the retrieved FRP [59; 80]. In this study, the majority of the fires were detected in the dry season, and account for 91% and 88% of all fire clusters in the northern and southern hemisphere respectively. The fuel moisture content of herbaceous fuels is typically low in the dry season but does vary over time and with the proportion of green grass. These factors are typically at a minimum at the peak of the dry season [73; 81]. Recent studies have also found different FRE to fuel consumption coefficient values to that found by reference [20] which could be caused by differences in sensor characteristics, fuel moisture, fuel type and fire size [35; 79; 82; 83; 84]. Figure 10: FRE-derived fuel consumption (**a**,**c**; kg DM m\({}^{-2}\)) and integrated MODIS PSN (**b**,**d**; kg m\({}^{-2}\)) estimates for five land cover types found in northern (a,b) and southern (c,d) hemisphere Africa. The upper and lower bounds of the bars are the 75 th and 25 th percentiles and the black horizontal line is the 50 th percentile.