## 1 Introduction  
Fire severity refers to the extent of impact and damage caused by fire disturbances on forest ecosystem vegetation, soil nutrients, and soil physical and chemical properties [1, 2]. It serves as a crucial indicator for assessing the severity of fire disturbances [3]. Accurately quantifying forest fire severity is essential for understanding the response mechanisms of terrestrial ecosystems to fire disturbances. This is particularly significant for post-firedamage assessment, ecological restoration, and nutrient cycling studies. As a result, fire severity has become an emerging focus in fire ecology research [4; 5; 6]. Fire severity estimation is commonly performed using ground surveys and remote sensing monitoring techniques. Ground-based methods, such as the Composite Burn Index (CBI), have become a widely accepted standard for field surveys and loss assessments conducted by the U.S. Forest Service [7]. To improve the applicability and accuracy of such methods, other scholars have proposed variations, such as Geometrically Structured Composite Burn Index (GeoCBI), Weighted Composite Burn Index (WCBI), and Burn Severity Index (BSI) [8; 9]. Additionally, Fire Severity Index (FSI) based on field data, quantifies fire severity based on the biomass of different components of burned areas [10]. In terms of remote sensing, common indices are predominantly derived from shortwave infrared (SWIR) and near-infrared (NIR) bands due to their distinct biophysical sensitivities. SWIR reflects canopy water content, while NIR is indicative of leaf area and photosynthetic pigment concentrations--both of which undergo significant alterations following fire events [11; 12]. Indices that incorporate both spectral ranges, such as the Normalized Burn Ratio (NBR) and its derivatives--the delta Normalized Burn Ratio (dNBR), the relative delta Normalized Burn Ratio (RdNBR), and the Relativized Burn Ratio (RBR)--have been extensively applied to evaluate fire severity, particularly for quantitative assessments at landscape and regional scales [11; 13; 14; 15]. Indices constructed using visible-near-infrared (VNIR) bands, such as the Normalized Difference Vegetation Index (NDVI), the Global Environmental Monitoring Index (GEMI), and the Excess Green Index (EGI) have also been employed in fire impact assessments across different global regions [13; 16; 17]. However, their effectiveness is generally inferior to that of the NBR series [18; 19]. Moreover, mid-infrared (MIR) bands, which are capable of penetrating smoke and capturing thermal anomalies, have been incorporated into indices such as the SWIR-MIR index (SMI) for real-time fire intensity assessments during active burning [11]. Long-wave infrared (LWIR) bands, which provide direct measurements of surface temperature dynamics, have further been utilized in composite indices such as the pre- and post-fire land surface temperature and enhanced vegetation index (deltaLST/EVI), enhancing fire severity estimation across varying vegetation cover types and densities [20; 21; 22]. Nonetheless, the applicability of these thermally derived indices remains context-dependent and may exhibit limitations under certain conditions [18; 23]. While ground-based methods can provide relatively accurate assessments by establishing relevant standards, they are time-consuming and labor-intensive, making them inefficient for large-scale applications [24; 25]. Additionally, the absence of pre-fire baseline comparisons renders fire severity classification highly susceptible to investigator subjectivity [26; 27; 28]. In contrast, remote sensing methods have gradually become a key direction of development in fire severity research [4; 6; 29]. Numerous studies have demonstrated the effectiveness of using medium-high resolution multispectral satellite images combined with spectral indices for fire severity estimation [14; 18; 30], including images from Landsat-7/8 [31; 32; 33], Sentinel-2 [34; 35], and GF-1 [36].