## 1 Introduction  
In the past decade, increasing wildfire events have become one of the world's most destructive natural hazards, which has resulted in extensive damage from economical, humanitarian, and environmental perspectives [1]. Due to climate change and rising temperatures, wildfires have become more severe, frequent, and long-lasting, which has led to the release of massive amounts of greenhouse gases, the destruction of forests and their related habitats, and the damage to infrastructure and property [2; 3; 4; 5; 6]. As a result, identifying burned areas is crucial for monitoring wildfire damage. It aids in the growth of vegetation, the nutrient deposition on the forest floor, and the maintenance of a healthy forest ecosystem [7]. Wildfires frequently occur in areas where several causes interact, making early identification by in situ observations challenging. Remote sensing imaging has been useful in detecting wildfire regions due to its ability to identify changes in large areas over long periods of time that are difficult to notice from the ground [8]. Remote sensing has been a method for wildfire monitoring using the Earth observation satellite data [9; 10; 11]. Earth observation systems based on satellites can give consistent and regular readings across large portions of the surface. This provides for quick and cost-effective monitoring of wildfire progression around the world [3]. Satellite sensors like Sentinel-2 and Landsat-8 have multispectral bands sensitive to fire disturbance in the visible, near infrared (NIR), and short-wave infrared (SWIR) [12]. The amount of wildfire-related radiation reflectance can be described in terms of the spectral wavelength [13]. For example, fire-disturbed regions absorb more NIR radiation than unburned areas, whereas burned regions reflect more radiation in the visible and SWIR bands [14]. As a result, several spectral indices for burned area detection have been developed, including the Normalized Difference Vegetation Index (NDVI), the Burned Area Index Modified (BAIM), the Normalized Burn Ratio-SWIR (NBRSWIR), and the Normalized Burn Ratio (NBR) [15; 16; 17; 18; 19; 20; 21]. Among them, NBR, constructed by NIR and SWIR bands, has been frequently employed in burned area mapping by utilizing the optical data (e.g., [22; 23]). The NBR is primarily sensitive to living chlorophyll and water content of vegetation and soil due to the fact that NIR is sensitive to photosynthetic components and SWIR is sensitive to vegetation moisture. To improve the burned area discrimination, the indices such as dNBR and dNDVI can be differently normalized by the relevant bitemporal pair (i.e., pre-fire and post-fire scenes), which also requires cloud-free pre-fire satellite imagery [24]. Optical-based spectral indices for various vegetation types frequently demonstrate strong sensitivity to low, moderate, and high burn severity, with results that are not site-specific [23; 24]. For example, an automatic thresholding chain based on the dNDVI and dNBR was proposed to map the burned areas at a national scale by using Sentinel-2 data [25]. Since the next Earth observation satellites will have onboard data processing and constrained storage for earlier scenes, such bitemporal indices increase preprocessing time and restrict potential uses. When applied to a variety of landscapes, certain automated approaches based on spectral signatures necessitate the time-consuming and difficult fine-tuning of a set of thresholds [26]. Therefore, nonparametric machine learning algorithms have received a lot of attention for outperforming traditional threshold-based approaches using spectral indices in burned area detection [27]. Fortunately, combined with recent remarkable improvements in processing capabilities, the availability of remote sensing data has boosted the ability to use data-centric methods for wildfire detection [27]. Consequently, there has been an increasing interest in using machine learning approaches in wildfire monitoring and management.