[10] used biomass maps derived from remote sensing data to calculate the forest carbon sinks in China's Dongting Lake basin, observing an increase from 64.64 TgC in 2000 to 382.56 TgC in 2020. Similarly, [PERSON] et al. [11] integrated Normalized Difference Vegetation Index(NDVI) and Leaf Area Index(LAI) remote sensing data with ground inventory records to estimate biomass distribution in Tripura, India, at 32-94 Mg ha\({}^{-1}\). Current remote sensing satellites estimate forest biophysical and chemical parameters, such as canopy closure, tree species, structural attributes, and associated indices [12]. While these approaches generally satisfy the requirements for detecting forest carbon sinks, they are hindered by challenges including limited model generalizability, sensitivity to weather conditions, and restricted applicability [13]. Additionally, these methods are insensitive to small or negligible changes in reflectance linked to photosynthetic activity, making them inadequate as indicators of vegetation's real-time photosynthetic status. They also exhibit limited sensitivity in capturing the complexity of vegetation's photosynthetic processes [14]. Therefore, it is imperative to develop advanced remote sensing methods that are both robust and efficient, enabling easier observation while accurately reflecting the intensity of vegetation photosynthesis to enhance the precision of forest carbon sinks assessments. Sun-Induced Chlorophyll Fluorescence (SIF) has emerged as a promising method for forest carbon sinks estimation. SIF is a phenomenon in which plants emit fluorescence when exposed to light, with emissions primarily concentrated in the red light range (approximately 690-750 nm) and the near-infrared range (approximately 725-800 nm). As a byproduct of photosynthesis, SIF provides a more direct representation of vegetation photosynthetic dynamics compared to traditional vegetation indices. This makes it highly effective for applications such as estimating Gross Primary Productivity (GPP) [15] and investigating vegetation phenology [16], crop health and diseases [17], and other related fields. SIF is intrinsically linked to vegetation photosynthesis, a critical component of the ecological carbon sequestration process in forest ecosystems. This connection makes SIF data a valuable tool for estimating forest carbon sinks. However, due to the influence of physical models and environmental factors, SIF data can exhibit instability, and directly using SIF for forest carbon sinks estimation may lead to significant errors. Previous research has shown a strong linear correlation between SIF and GPP [18; 19; 20], although this relationship may be influenced by climatic conditions and forest types [21; 22]. Despite these influencing factors, the correlation provides a solid foundation for model development. Accordingly, this study adopts GPP as an intermediate variable, using SIF data to estimate GPP. As a key component in calculating forest carbon sinks, GPP, when combined with ecosystem respiration, forms the basis for estimating forest carbon sequestration. For instance, [PERSON] et al. [23] utilized satellite remote sensing to monitor plant SIF and directly measure GPP in agricultural and grassland ecosystems, achieving results consistent with ground-based observations. [PERSON] and [PERSON] [24] applied SIF data to estimate forest carbon sinks in northeastern China and validated the method using flux tower data. Similarly, [PERSON] [25] reviewed the application of SIF in monitoring vegetation dynamics and carbon uptake in Arctic-Boreal regions, finding the method promising. While previous studies have explored methods for estimating carbon fluxes using SIF and highlighted the linear relationship between SIF and GPP, their findings are largely confined to semi-arid regions globally, northeastern China, and the Arctic-Boreal zones.