## 1 Introduction  
Forest fire occurs globally on various scales every year, causing economic, social, ecological, and environmental damage [1]. It is caused by natural phenomena as well as anthropogenic activities. It results in severe damage to wildlife, fertile forest floors, timber, human property, and certain rare plant species [2]. As a result of the pervasive harmful effects due to forest fires, they have received increasing recognition in past recent years. Therefore, precise mapping and monitoring of the location and temporal distribution of wildfires are important. In addition, forest fire as one of the most significant natural disturbance processes would also modify the structure and the composition of the vegetation [3, 4]. It is closely related to the carbon cycling and greenhouse gas emissions [5, 6]. The forest fire can burn away dead or decaying vegetation, facilitating the growth of new trees and burned trees or the soil surface, releasing large amounts of carbon. Forest fire disturbances have an important impact on ecosystem stability and renewal, and succession of forest ecosystems. [7, 8]. The effects of forest fires on ecosystems are not clear for a long time. Long-time burned area mapping is a critical factor to investigate the causes of the forest fire and the impacts of forest fires on Gross Primary Productivity (GPP). This research provides theoretical support for intelligent management and prediction of forest fires to achieve carbon neutrality in China. Traditionally, a field survey was used to monitor forest fires. But this method is time-consuming and laborious and lacks spatial information. To overcome this problem, remote sensing is one of the most efficient and cost-effective techniques for fire detection and mapping. At present, there are so many forest fire remote sensing monitoring products that provide fire data sources, but these data have their limitations in terms of both spatial and temporal resolution. [PERSON] et al. (2017) compared four common fire products such as MCD45A1, MCD64A1, MCD14 ML, and Fire_CCI [9]. Each product has its limitations in terms of accuracy in different fire ranges. Therefore, the timely and accurate mapping of burned areas is essential for fire management and climate change. The remote sensing technique provides a labor-efficient method to quickly locate the burned area distribution and detect the impacts and causes of forest fires [10; 11]. In the past decades, extensive studies have been carried out on the detection of burned areas by remote sensing [3; 10; 12]. Medium-resolution satellites such as the Landsat series are widely utilized to identify the burned area. However, they cannot accurately depict the burned area change because of the limitations of the temporal resolution. Combining information from different sensors can fill the gaps between high temporal resolution and medium spatial resolution [13]. High temporal resolution satellite images such as MODIS and AVHRR greatly improve the capability to detect the long-time burned area [14; 15; 16]. Accurate forest fire distribution provides the basis for subsequent studies. It is significant to acquire the long-time fire distribution for studying its causes and effects. Forest fire occurs every year in China, especially during the dry season. These fires are due to various factors such as dry weather, flammable materials, and human action [13]. The natural factor is the main determinant of natural forest fire [17]. Climate change affects the occurrences and dynamics of fire by changing meteorological factors such as air temperature, precipitation, and humidity [18]. The two possible main factors associated with climatic change are temperature and precipitation [19]. [PERSON] et al. (2004) found that the temperature and precipitation were significantly correlated with the burned areas [20]. [PERSON] et al. (2006) found that the forest fire was caused by reduced winter precipitation together with earlier spring snowmelt [21]. [PERSON] et al.