###### Abstract  
Vegetation photosynthesis is a key Earth system process that can fix carbon dioxide in the atmosphere. Mountainous areas usually have high productivity and extensive vegetation cover, but their study requires a higher spatiotemporal resolution due to the complex climate and vegetation variations with altitude. In this study, we analyzed the variations and climatic responses of vegetation gross primary productivity (GPP) in northwestern Hubei, China, at a 30 m spatial resolution from 2001 to 2020, based on the fusion of multi-source remote sensing data. A GPP estimation framework based on the CASA model was applied, and spatiotemporal fusion of Landsat and MODIS data was achieved using the STNLFFM algorithm. The results indicate that GPP exhibits higher values in the mountainous regions of west Shennongjia, compared to the eastern plain regions, with a generally increasing trend with increasing elevation. GPP has shown an overall increasing trend over the past 20 years, with almost 90% of the high-elevation regions showing an increasing trend, and the low-elevation regions showing an opposite trend. The relationship between GPP and climate factors is greatly impacted by the temporal scale, with the most pronounced correlation at a seasonal scale. The impact of temperature has been generally stable over the past 20 years across different altitudes, while the relationship with precipitation has exhibited an overall decreasing trend with the increase of altitude. Precipitation and temperature correlations show opposing variations in different months and elevations, which can be mainly attributed to the varied climatic conditions in the different elevations. and driving factors of vegetation photosynthesis [5]. Gross primary productivity (GPP), which is the total amount of carbon fixed by plants in a given period of time, reflects the ability of ecosystem productivity and carbon sequestration. As a result, it is meaningful to monitor the changes and responses of GPP as it is a key indicator for understanding the global carbon cycle and combating climate change [6; 7; 8]. Recent studies have highlighted that climate variability plays a dominant role in driving changes in GPP [9; 10]. In these studies, remote sensing data have played an essential role by providing consistent, large-scale, and high-resolution observations that enable the detailed assessment of vegetation responses to climatic factors across diverse and complex landscapes. Nevertheless, accurately capturing the influence of climate variability on GPP at fine spatiotemporal scales remains a significant challenge, especially in heterogeneous and mountainous regions where environmental conditions vary sharply over short distances. Traditionally, methods for obtaining GPP can be broadly categorized into two main types: site observation-based methods and model simulation-based methods [8]. Site observation-based methods, such as eddy covariance (EC) methods, directly measure the ecosystem carbon fluxes, which can provide site-level GPP with high precision, but their application is usually limited by the spatial and temporal resolution [11]. Model simulation-based methods are the mainstream methods for global or large-scale GPP applications. With the advancement of remote sensing technology, various remote sensing-driven models have been developed, which are better suited for large-scale applications. Remote sensing techniques can be used to calculate vegetation indices through the combination of different spectral bands. Examples of vegetation indices are the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI). Various structural and functional parameters of vegetation can also be derived, including the leaf area index (LAI), leaf chlorophyll content (LCC), and maximum rate of carboxylation (Vcmax). These parameters are the fundamental drivers for many models, which can be primarily divided into statistical models, process models, and light use efficiency (LUE) models [12].