## 4 Simulation of the Meteosat Third Generation Imager Fire Channel Potential
The overwhelming part of the total FRP, summarized for the whole scene, is contributed by large fires that are also detectable in an MTG-I/FCI fire application MIR band pixel;
2. Due to the BIRD MIR band pixel aggregation conducted for the MTG-I/FCI MIR band simulation, a part of the small fires in the BIRD MIR band data is not omitted during this pixel aggregation but rather \"collected\" in larger hot clusters providing enough signal in the simulated MTG-I/FCI MIR band scene to retrieve an FRP estimate. This way, even such \"small fire ensembles\" in the initial BIRD scene can still contribute to the total FRP derived and summarized for the whole scene during the MTG-I/FCI simulation. The retrieval errors are defined--as described in [3]--in relation to the TIR background clutter, which is the principal effect introducing error to the bi-spectral retrieval for hot clusters with a fire proportion less than 0.001, i.e., in pixels occupied by fire at less than 0.1%. This is the case for BIRD data and for the simulated MTG-I/FCI observations, too. The simulated MTG-I/FCI data (i) of the Benin scene and (ii) of a fire scene in the Castelo Branco region of Portugal allowed the retrieval of the effective fire temperature and area for a much smaller number of hot clusters than in the respective original BIRD scenes. To secure a quantitative comparison of the simulation results, the following comparable accuracy conditions were applied for the estimation of the three attributes fire temperature, fire area and FRP:  
* Accuracy of 100 K for the fire temperatures;
* An accuracy \"factor of 2\" for the fire areas (an accuracy \"factor of 2\" for the fire areas means that the area is estimated with an uncertainty, i.e., the exact fire area may be lower by a factor of 2 or higher by a factor of 2 than the estimated fire area);
* Thirty percent FRP accuracy (a \"30% FRP estimation accuracy condition\" means that the FRP is estimated with an uncertainty, i.e., the exact value of the FRP may be 30% lower or 30% higher than the estimated FRP value). These conditions are actually not the requirements set up by the user (who would probably prefer much better accuracies), but rather the retrieval accuracies that can reasonably be expected from the satellite data-based bi-spectral retrievals [3]. The total number of hot clusters with the three fire attributes--as presented in the three right columns of Table 9 for the BIRD data, the simulated MTG-I/FCI data and the MODIS data--decrease for the simulated MTG-I/FCI data and MODIS data. Meanwhile, the BIRD data meet the retrieval accuracy conditions for 16-19% of the detected hot clusters; the retrieval accuracy conditions decrease to 10% in the 1 km sampling mode simulation for MTG-I/FCI; and all detected hot clusters do not meet the \"factor-2\" fire area accuracy conditions in the 2 km sampling mode simulation for MTG-I/FCI. Nevertheless, the simulation shows that MTG/FCI will meet the applied \"30% FRP estimation accuracy condition\" for two out of every three hot clusters in both sampling modes. A second BIRD data example selected for this MTG-I/FCI simulation was a large fire scenario in the Castelo Branco region of Portugal observed on 4 August 2003. Figure 10 shows two MIR band image fragments of a fire scene in Portugal obtained on 4 August 2003 by MODIS \"Terra\" (left) and BIRD (right) with ground sampling distances of 1 km and 180 m, respectively, and with color-coded FRPs of the fire clusters. The MODIS image was obtained 34 min earlier than the BIRD image that was used for the MTG-I/FCI image simulation. Though the general distribution of burning in both images of Figure 10 is similar, one can also notice differences in the location and intensity of some hot spots due to the time interval between the images. The proportion of big fires in this scene was much higher than in the Benin case.