## 1 Introduction  
Vegetation burning is a global-scale process that affects the global distribution and structure of vegetation, major biogeochemical cycles, and the climate system [1]. The relation of fire with vegetation is contradictory: as an ecological factor, it contributes to maintaining ecosystem dynamics, productivity and biodiversity and, as a land management tool, it is extensively employed in croplands, rangelands, and forests throughout the world. However, wildfires are a socio-natural hazard that annually affect millions of hectares of forests, woodlands, and other vegetation, endangering human populations, and causing substantial economic losses, both in terms of assets destroyed and in the form of prevention and suppression costs [2]. During the last decades, the Brazilian savanna has been increasingly affected by deforestation due to cropland and pasture expansion, consequently increasing and altering the natural fire regime in the region [3; 4]. Attempts to characterize these anthropogenic impacts presuppose understanding of spatial and temporal fire patterns [1]. Despite the high frequency of human induced-fire and significant disturbance caused to the Cerrado biome, fire dynamics are not yet well characterized. Over the last few decades, the use of remote sensing has allowed unprecedented advances in mapping fire dynamics, especially for locating fire occurrence in time and space and quantifying the total extent of area burned. Several studies relied on the use of remote sensing to map burned areas at a global/regional scale [5; 6; 7; 8; 9; 10; 11]. However, the variable persistence of burn scars within different vegetation types, and the spectral confusion with other phenomena (e.g., cloud shadowing) are some of the problems that still hamper accurate burned area mapping [12]. Accordingly, users of burned area maps have stressed the need to improve product accuracy, namely in order to refine current estimates of burned areas, thus providing input to global analysis of ecological impacts of fires to better understand the relations between fire occurrence and biodiversity, and to improve the assessment of atmospheric emissions derived from vegetation fires [13; 14]. The detection of small burned areas is one of the main limitations in burned area mapping that uses low-resolution sensors, as previously reported [7; 11; 15; 16; 17]. It is possible to improve the detection of small burned areas using existing fire products. [PERSON] [5], using 300-m MEdium Resolution Imaging Spectrometer (MERIS) global satellite data, developed an algorithm based on the time series of surface reflectance to identify abrupt changes in near-infrared reflectance (NIR), and regional growing techniques using MODIS active fire as spatial seeds, in a two-phase algorithm. The use of this sensor has improved small fire detection due to better spatial resolution, however, the results were not considered competitive in comparison with MODIS burned area products [17]. Hybrid approaches that combine active fire information with reflectance data have been widely used in burned area mapping to add new evidence for burned area classification [5; 7; 11]. In such approaches, active fires are used to derive statistics for burn classes [18; 19], or used as seed points in regional growing techniques. However, active fire detection products often omit burned area patches [20], leading to underestimation of the area burned. Omission errors from active fire detection products may be due to the spatial and the temporal coverage of satellite overpasses, sensor saturation, or obscuration by clouds and smoke [7; 11; 21]. The presence of thick clouds and heavy smoke layers is a major drawback in operational applications, preventing hot spot detection due to the spectral signal attenuation in the atmosphere [21; 22]. This is especially true in the tropics during the dry season [23; 24], where the probability of cloud-free observations is, on average, less than 30% [25].