## 5 Discussion  
Two aspects can be considered in the analysis of omission and commission errors: the first is related to errors along the edges of burn scars, due to the differences in spatial resolution of the datasets, possibly combined with georeferencing errors. The second is related to fires that occurred prior to the date of the first Landsat image, but were detected in the PROBA-V composite after this date, probably due to shortage of cloud-free data. This type of error was also identified in the results of MCD64A1. The uncertainty in detection dates can lead to this type of error in the accuracy assessment [52]. Although the AQM-PROBA and MCD64A1 products are based on different data derived from different sensors, the divergences in the results presented may reflect the different methodologies applied. Both products use active fire for spectral characterization of burned pixels, but they are different in design. The AQM-PROBA is based on the spectral space of the NIR T2 and NIR T1-T2 values in PROBA-V images, with samples collected by VIIRS active fire, which is used to train the OC-SVM classification model. On the other hand, MCD64A1 uses spectral index data constructed in the NIR-SWIR spectral space (1.6 and 2.1 \(\upmu\)m) and cumulative active fire maps to guide the selection of burned and unburned samples and to specify probabilities. In addition, the better spatial resolution of PROBA-V compared to MODIS data and the use of OC-SVDD may have an influence on the detection of smaller fire scars. In the AQM-PROBA product, isolated pixels are excluded from the final result, such that the minimum mapping area is 22 ha. Due to the 500 m spatial resolution of MODIS, the theoretical minimum area mapped by the MCD64A1 would be 50 ha. However, according to previous studies, it is hard to detect burned areas smaller than about 4 to 10 pixels in size [64]. For instance, the minimum resolution at which MCD64A1 is reliable is 120 ha [66]. Both products showed the same trend towards larger omission errors and lower correlation with the reference data when small burns dominate the fire size distribution. This is common when comparing images and reference data collected at different spatial resolutions, where the size and irregularity of the targets are determinant for higher omission errors [5; 7; 15; 52]. The largest omission errors in both burned area products were observed where scars are typically small. [PERSON] et al. [15] using Satellite Pour I'Observation de la Terre (SPOT) Vegetation data over Africa, mentioned two atypical situations: small burned areas that were sufficiently dark to be detected (low values of NIR reflectance after the fire) and large burned areas that were frequently missed due to a small decrease in NIR reflectance values after the fire. The former case may explain the results obtained with scene 219/072 evaluated in our study. This scene contained small burned areas but yielded a Kendall coefficient of 0.71 for AQM-PROBA, similar to those obtained for areas with larger burns. Besides the presence of small burned areas, scene 218/72 covers a peculiar area where a rugged topography and extensive rock outcrops further complicated burned area detection with low-spatial resolution sensors. Thus, we show that mapping accuracy is likely to vary significantly in space due to the observed contrast between the distribution of fire size number and area. The extent and fragmentation pattern of burning, namely on the relative importance of small versus large burns, introduces limitations in BA estimation when using coarse spatial resolution sensors. Since the study area is characterized by a high frequency of very small scars (<25 ha), a large number of omission errors was observed due to the coarse resolution of the BA products (500 m for MCD64A1 and 350 m for AQM-PROBA), in some cases leading to a substantial underestimation of total area burned. On the order hand, in regions with predominance of large fire scars, the BA coarse products are quite accurate, and may even lead to slight overestimation.