###### Abstract
Among the different models, statistical models usually show accuracy limitations due to their simple basis and lack of mechanism [13], and process models typically suffer from a complex model structure and numerous parameters, despite being developed based on a strong theoretical foundation and clear mechanism [14]. As a result, LUE models have become the most widely applied models, due to their clear LUE principles, computational simplicity, and suitability for regional- and global-scale estimation when combined with remote sensing data [15]. Based on these methods, a large number of large-scale GPP estimation and analysis studies have been conducted to understand the changes and driving factors for regional or global vegetation photosynthesis. Not only have the spatial distribution and temporal variation trends of GPP been well analyzed, but the response of GPP to different climatic factors, extreme climatic events, and various disturbances has been extensively studied. There have also been many studies of specific sensitive areas, which can help provide recommendations for conservation policies [16; 17; 18; 19; 20]. However, these studies have mainly focused on large regions, and the specific conditions and characteristics in complex mountainous areas have still not been well investigated. Compared to plain regions, mountainous regions exhibit diverse slopes, aspects, and elevation changes, even over small areas, which makes the climate and vegetation distribution much more complex. On the one hand, due to the vertical zonation, vegetation shows strong spatial heterogeneity, with the rich species showing different variation trends and responses to climate change, even in a small region [21]. On the other hand, climatic factors also vary significantly with elevation; for example, temperature decreases with increasing elevation, and the moisture from precipitation may not be easily retained in high-altitude areas, leading to pronounced climatic differentiation [22; 23; 24]. Consequently, the response of photosynthesis to environmental conditions is more complex in mountainous regions [25], and data with a finer spatiotemporal resolution are also required to represent the sharply varied GPP and climatic conditions. Although there are many sensors that can obtain observations supporting GPP estimation at various resolutions, they all have their unique strengths and limitations, which results in it being difficult to fulfill the demand for GPP studies in mountainous regions. For example, Moderate-Resolution Imaging Spectroradiometer (MODIS) products feature spatial resolutions of 500 m or 1000 m with daily observations, enabling consistent monitoring. However, their coarser resolution often misses fine-scale details, especially in complex terrain such as mountainous regions, where subtle changes can be difficult to detect [23]. Conversely, Landsat sensors feature a 30 m spatial resolution, capturing detailed landscape variations. However, their 16-day revisit cycle and cloud cover issues limit the generation of continuous, cloud-free time series. Similarly, the Sentinel series of satellites, with resolutions as fine as 10 m and diverse multi-sensor capabilities, supports many applications across various fields. Nonetheless, Sentinel data faced the problems of cloud contamination and insufficient temporal resolution, hindering the generation of seamless time series. To address these issues, integrating the complementary strengths of the different sensors is vital. Multi-source remote sensing fusion combines the high temporal resolution of sensors such as MODIS with the fine spatial detail of Landsat. Studies have shown that this approach can improve data accuracy, enhance coverage, and provide robust datasets for monitoring complex, climatically variable regions [26; 27; 28; 29]. Multi-source remote sensing fusion excels in applications needing precise temporal sequences and fine-scale change detection, such as land use monitoring.