## 1 Introduction  
In terrestrial ecosystems, fire serves as a key ecological driver [1, 2, 3]. Grassland fires, a natural phenomenon within ecosystems, drive community evolution and facilitate vegetation recovery [4]. These fires promote community succession, support rapid vegetation recovery, and drive nutrient cycling and species renewal [5], all contributing to grassland ecosystem health. However, grassland fires are unpredictable, spread rapidly, and are difficult to control, posing a significant threat to life and property in pastoral areas with limited preventive capacities [6]. Frequent grassland fires weaken livestock carrying capacity [7], intensify desertification, and degrade ecosystems by depleting grassland resources [8]. They also reduce air visibility in affected regions [9] and pose serious health risks to local populations [10]. Furthermore, these fires diminish the carbon sink functions of grassland systems and may trigger chain reactions, leading to secondary ecological disasters like landslides and mudslides [11]. Grassland fires, as typical combustion phenomena, can be categorized into open fires and smoldering combustion [12; 13]. Open fires primarily consist of typical surface grassland fires. Smoldering combustion, often referred to as \"zombie fires\", mainly occurs in tropical rainforests, vegetation humus, and organic peat layers below the surface of temperate and cold zones [12; 14]. \"Zombie fires\" primarily occur in the peat layers of tropical rainforests and temperate to boreal regions [14]. Once ignited, these fires can spread underground and continue burning for months or even years. Due to challenges in accurately detecting underground fire locations and assessing suppression effectiveness, \"zombie fires\" are exceedingly difficult to extinguish completely [15]. Wildfires near the Arctic Circle have garnered significant attention [16; 17; 18; 19], as these fires can persist beneath Arctic peat, reemerging in early spring as temperatures rise [20]. Additionally, these fires can release significant amounts of carbon into the atmosphere, alter soil hydrophilicity, and lead to severe grassland erosion [21; 22]. Most research has focused on forest fires, while studies on grassland fires remain relatively limited. Since the 1960s, researchers have explored techniques for collecting and managing data on grassland fires. For instance, [PERSON] [23] and [PERSON] [24] examined the spatiotemporal patterns of grassland fires and assessed fire risk distribution based on historical fire data. Their findings laid the groundwork for early Chinese grassland fire prevention efforts, aiding decision-making in fire management and disaster mitigation. Due to the complexity of grassland fire dynamics, fire risk cannot be precisely quantified using a single indicator, nor can historical data alone adequately capture the status of influencing factors. With technological advancements, machine learning techniques and remote sensing have been extensively applied to fire spatial pattern analysis, prediction, and forecasting. Scholars globally have made substantial contributions to understanding the spatiotemporal characteristics of wildfires through remote sensing data [25; 26; 27]. Their research has developed fire occurrence risk models and examined the spatiotemporal distribution of wildfires, incorporating factors such as fire evolution [6], occurrence probability [28], and fire density [29]. Grassland fires result from multiple factors, including meteorological conditions, fuel availability, topography, and human activities. Their spatial and temporal distribution exhibits significant heterogeneity, with key influencing factors differing across regions [30; 31; 32]. For instance, [PERSON]'s [27] study on grassland fires in Hulun Buir found that meteorological factors play the most significant role in fire occurrence, while combustible loads are correlated with burned area.