## 1 Introduction
Besides burnt area mapping, remote sensing is used successfully to detect active fires [37]. The manifestation of forest fires was detected by means of near-daily satellite platforms such as the Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectrometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS) with coarse (>250 m) spatial resolution [38; 39]. Their near-daily revisit cycle is valuable and practical to record fire signatures, but they are unavailable to detect small burned areas, particularly in regions with limited and highly fragmented fires [40; 41]. In such areas, these satellite products also suffer methodological limitations in respect of assigning specific vegetation types to the burned area [42], which are crucial for understanding the ecological effects of fire [43]. Because accurate information on burned areas is a prerequisite for effective fire management [44], fine-scale satellite imagery is required to provide a reliable depiction of burned areas [45]. The Sentinel satellite constellations are uniquely suited to monitor forest fires as they provide unrestricted high-resolution images with near-daily acquisitions [46; 47], the only one of its kind to deliver rich and affordable global observations so far. This facility also includes the monitoring of fire-emitted gases such as carbon monoxide (CO) with the latest Sentinel-5 sensor [48]. However, the lack of processing tools for these datasets is a major constraint on their utilization, particularly in countries with limited resources. The emergence of unrestricted cloud-based computing resources such as Google Earth Engine (GEE) has enabled unprecedented access to petabytes of geospatial data (i.e., complete Sentinels archive) with diverse processing functions, and this has leveled the ground for users in data-poor countries and their counterparts [49]. The GEE's powerful parallel computing infrastructure facilitates on-demand and on-the-fly production of complex geospatial products at varying geographic and time scales without the necessity of supercomputers [50; 51]. Its capabilities were demonstrated in various applications [52], however, few studies have exploited GEE for burned area monitoring over forested landscapes so far [53]. For example, [PERSON] et al. [54] compared the performance of three machine learning approaches for forest fire burned area detection based on Sentinel-2 and Landsat-8 data within the GEE framework. Their results showed superior classification accuracies of 97-100% for classification regression tree (CART) and RF algorithms, while the support vector machine (SVM) achieved slightly lower accuracy. Similarly, [PERSON] et al. [55], used Sentinel-2 and machine learning algorithms within the GEE platform to detect wildfire damage in Australia, and they detected burned areas with accuracies from 82% to 92%. [PERSON] and [PERSON] [53] also compared post-fire burn severity indices from Sentinel-2 and Landsat-8 and achieved comparable results in mapping burn severity for both sensors; the relativized NBR showed high accuracy over heterogeneous landscapes followed by dNBR and the relativized burn ratio performed relatively weaker. [PERSON] et al. [56] successfully mapped Amazon Forests with accuracies from 82% to 98% using Sentinel-2 and Landsat-8 images within the GEE environment. In addition, studies such as [PERSON] et al. [57] exploited machine learning algorithms within GEE to classify Mediterranean forest landscape, and their results successfully achieved accuracies from 80% to 83% based on Sentinel-2 with RF and SVM. These studies have demonstrated high potential for GEE to monitor fire-induced forest damage, and as such, burned area mapping is expected to increase because users have access to these facilities wherever they are located.