## 6 Conclusions  
This work has used the MODIS active fire product to quantitatively asses the performance of the FTA active fire detection algorithm used to generate the Meteosat SEVIRI FRP-PIXEL fire product. Though comparisons were performed across a strong gradient of fire activity in the Central African  
Figure 11: Comparison between the fire radiative power (FRP) emitted from well-observed fire pixel clusters simultaneously detected by SEVIRI and MODIS. All clusters are completely surrounded by SEVIRI “non-potential fire pixels”. In (**a**) is the direct relationship, and in (**b**) is the ratio of the FRP measured by SEVIRI to that measured by MODIS, \(\phi_{\text{FRP}}\), on a cluster basis and sorted by the number of SEVIRI active fire pixels detected in each cluster. Note: SEVIRI clusters with \(>\)5 active fire pixels are not included in (b). Republic (CAR), such gradients are not uncommon in northern Africa, e.g., in Senegal [68]. Therefore rather than selecting validation sites across global variations in fire regimes, we suggest that steep geographical gradients in fire behavior may offer more localized opportunities to evaluate the active fire detection performance of SEVIRI and/or other satellite-based sensors. Many tests are used by the FTA algorithm to discriminate SEVIRI pixels containing active fires whilst minimizing false alarms [38]. Mistakenly classifying sunlight as an active fire is an important issue that active fire detection algorithms must avoid by judicious use of multispectral tests (e.g., [38, 69]). Although it is possible for such tests to also occasionally remove \"true\" fire pixels, we found sunlight to have a minor impact on SEVIRI's active fire detection performance. Instead, the FTA detection algorithms performance was more influenced by the absence of and/or uncertainties in the estimated background temperature surrounding potential fire pixels, a key parameter required for establishing the contextual thresholds used when confirming an active fire pixel as a true fire pixel [33]. Although it may be possible to design regional contextual detection thresholds (e.g., [70]), this practice potentially results in higher commission (_i.e._, false alarm) rates. Moreover, the current FTA detection algorithm is already capable of identifying active fire pixels with MIR brightness temperatures only a few Kelvin above that of the background, so the detection criteria are likely already being pushed to their limits. Thus the scope for further refining the contextual detection criteria in the FTA algorithm with the aim of detecting a greater number of active fires appears rather limited. Results here also indicate that the performance of the SEVIRI FTA detection algorithm is strongly affected by the cloud mask, accounting for no less than 30% of SEVIRI's active fire omission rate when compared to MODIS. The cloud mask used during the processing of the SEVIRI FRP-PIXEL product is generated every 15 min by the SAF to support NoWcasting and Very Short-Range Forecasting, SAFNWC [71]. The SAFNWC cloud mask explicitly aims to detect all types of clouds, with the goal of confidently identifying SEVIRI observations of the land surface uncontaminated by cloud [71]. In contrast, the tailored cloud detection algorithm used during the generation of the MODIS fire products is designed specifically for the active fire application to distinguish only larger, cooler, and optically thick clouds, and sacrifices the ability to discern thin cirrus and cloud edges in favour of reducing the possibility of misclassifying fire-affected pixels as cloudy [38, 39]. Other SEVIRI active fire detection algorithms have implemented different cloud detection strategies (e.g., [48, 72]), and refinement of the cloud mask used for in the FRP-PIXEL product maybe warranted, though it should not be inferred that SEVIRI's omission rate relative to MODIS would decrease if an alternative cloud mask were used.