## 3 Methods
### Accuracy Assessment  
The accuracy of AQM-PROBA-V burned area maps was assessed using reference fire perimeters extracted from each L8 scene and compared with the results from the MCD64A1 burned area product. We evaluated the AQM-PROBA-V and MCD64A1 in two different ways: (1) pixel-based and (2) grid-based. The pixel-based analysis was based on a contingency table (Table 2), from which different verification measures were calculated, namely overall accuracy (OA), omission (OE), and commission error (CE), bias (BIAS), Dice coefficients (DICE), and critical success index (CSI) (Table 3). The OA is the fraction of correctly classified pixels, either as burned or unburned. Accordingly, the OA reflects the agreement between the burned area (BA) product and the reference map (i.e., the accuracy of the classification) and satisfies the principle of equivalence of events, since it credits correct burned and unburned pixels equally [62]. This is not always a desirable attribute, particularly when the number of burned pixels is much smaller than the unburned one. An alternative to the OA is the CSI, which is useful when the event (burned pixels) occurs much less frequently than the nonevent (unburned pixels). It quantifies the proportion of correctly classified burned pixels, after removing correctly classified unburned pixels from consideration. The worst possible values for OA or CSI are zero, and the best is one. The OE and the CE provide respectively information about the reliability and discrimination power of the developed BA product, while the DICE is a measure of the overlap between the BA product and the reference map, in terms of the number of common burned pixels. The OE and CE range between zero and one, and have a reverse scale, such that smaller values are best. Conversely, the DICE has a direct scale, varying from zero (worst) to one (best). Finally, BIAS just indicates if the BA product overestimates (BIAS > 1) or underestimates (BIAS < 1) the burned area, and should not be considered an accuracy measure, since it does not provide information about the correspondence between classification and reference. Unbiased BA products exhibit BIAS equal to 1, indicating that the burned event is classified the same number of times that it is observed. Most accuracy metrics available in the literature for the validation of thematic maps are derived from the contingency table. However, the traditional use of the contingency table assumes that reference data and classified data have the same spatial resolution, which is often not the case when evaluating coarse spatial resolution classifications, e.g., BA product acquired by sensors such as PROBA and MODIS. However, the influence of the sensor spatial resolution on the accuracy of the final burned area coarse product is well documented in the literature and is called low-resolution bias [49]. In order to circumvent this limitation, the contingency table was calculated using a fuzzy approach [63], which evaluates the proportion of reference (30 m) burned area contained within each pixel of the \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Reference**} \\ \hline  & & **Burned** & **Unburned** & **Total** \\
**BA Products** & **Burned** & A & B & A + B \\
**Unburned** & C & D & C + D \\
**Total** & A + C & B + D & A + B + C + D \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Generic contingency table between the reference and burned area (BA) products. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Verification Measures** & **Acronym** & **Equation** \\ \hline Overall Accuracy & OA & (A + D)/(A + B + C + D) \\ Omission Error & OE & C/(A + C) \\ Commission Error & CE & B/(A + C) \\ Bias & BIAS & (A + B)/(A + C) \\ Dice Coefficient & DICE & 2A/(2A + B + C) \\ Critical Success Index & CSI & A/(A + B + C) \\ \hline \hline \end{tabular}
\end{table}