# Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa
## Materials
### Gross and Net Primary Productivity (MODIS MOD17A2H/MOD17A3)  
A variety of MODIS primary productivity datasets were considered for derivation of pre-fire fuel availability. The MODIS MOD17A2 and A3 products quantify gross primary productivity (GPP; kg C m-2 day-1), whilst the MODIS MOD17A3 product describes the net primary productivity (NPP; kg C m-2 yr-1) which is the fraction of the GPP remaining after accounting for autotrophic respiration [46].  
The MODIS GPP data are delivered via the MOD17A2 algorithm on an 8-day basis at 0.5 km, using the MODIS-derived leaf area index (LAI), fPAR (MCD15A2H; [47]) and land cover (MOD12; reference [48]) data products as inputs. Meteorological information and biome-specific physiological parameters are also used, and estimates of NPP and net photosynthesis (PSN; kg C m-2 day-1) are also provided.  
\[PSN=GPP-R_{ml}-R_{mr} \tag{1}\]where \(GPP\) is gross primary productivity (kg C m\({}^{-2}\) day\({}^{-1}\)), and \(R_{ml}\)  and \(R_{mr}\) are the maintenance respiration by leaves and fine roots respectively (kg C kg C\({}^{-1}\) day\({}^{-1}\)). The PSN product does not include live wood maintenance or growth respiration costs, which are removed when calculating the annual NPP (MOD17A3) using:  
\[NPP=\sum_{i=1}^{365}PSN-\left(R_{mo}+R_{g}\right) \tag{2}\]  
where \(R_{mo}\) is the maintenance respiration of all living matter except leaves and fine roots (kg C kg C\({}^{-1}\) day\({}^{-1}\)) and \(R_{g}\) is the growth respiration (kg C kg C\({}^{-1}\) day\({}^{-1}\)).  
In contrast to the relatively high temporal resolution GPP data, the MODIS NPP products delivered via the MOD17 algorithm are produced only on an annual basis (at 0.5 km resolution). They are based on the light use efficiency (LUE) approach originally developed in reference [49], where NPP is expressed as a function of the plants LUE (\(\varepsilon\)) and the amount of absorbed photosynthetically active radiation (aPAR) [46; 50]. Due to its very limited temporal information, we chose to focus on the 8-day MODIS GPP product and the derived parameter of PSN.  
Processing refinements applied to mitigate the impact of residual cloud contamination on the MODIS data, along with improved spatial interpolation of the air temperature, vapour pressure deficit, and incoming solar radiation have improved the temporal consistency of the MOD17 GPP product [46]. [PERSON] et al., [51] evaluated these data at ten eddy covariance sites in China, predominantly covering croplands and grasslands, and found that the product captured temporal dynamics very well, but underestimated GPP by around 70% compared to the eddy covariance method. Similar evaluations in Africa also showed significant GPP underestimation, particularly at arid sites in the Sahel [52], and this was attributed to the parameterisation of the LUE term and to residual noise in the MODIS fPAR product. [PERSON] et al., [53] compared MODIS (Collection 4 and 4.5) annual NPP and annually integrated PSN data against field measurements of above ground biomass (AGB) at semi-arid open shrubland and savanna sites in the Sahel, and found integrated PSN to be far better related to AGB (\(r^{2}\) = 0.77; slope of linear best fit = 0.59) than was annual NPP (\(r^{2}\) = 0.37; slope of linear best fit = 0.37). We selected to use the MODIS PSN (MOD17A2H) product because it is available on an 8-day basis, facilitating temporal integration between successive fires. The apparent better performance of the MOD17 product in southern hemisphere Africa relative to that in northern hemisphere Africa, adds a degree of uncertainty to our analysis in the northern hemisphere.