## 1 Introduction  
Overwhelming anthropogenic CO\({}_{2}\) emissions lead to aggravated global warming effects [1], frequent extreme climatic events and natural disasters, ecological environment deterioration [2, 3], and natural resource depletion. Under such circumstances, maintaining the global carbon balance is an urgent necessity [4]. The Paris Agreement was officially adopted at the 21 st session of the United Nations Climate Change Conference in response to issues arising from carbon imbalance. The objectives of the document lie in the carbon sink capacity enhancement of global ecosystems and anthropogenic carbon emission counteraction via carbon sequestration of plants [5; 6; 7]. Net primary productivity (NPP) is the total amount of organic matter accumulated by plants per unit of time and space, serving as the energy taken up by plants for growth and reproduction [8; 9]. It is also a direct indicator of carbon sequestration capacity and the changes in the ecological environment [10]. In this sense, studying the spatiotemporal distribution and dynamics of vegetation NPP and its driving forces is of great significance to achieve ecosystem restructuring and stability, thus attracting wide interest from researchers [11]. In traditional ecological studies, the field-testing methods for estimating vegetation NPP include direct harvesting techniques, photosynthesis measurement, and CO\({}_{2}\) measurement [12; 13]. Although these methods can provide accurate raw data, they are not readily adopted over large areas due to deficiencies such as poor timeliness, limitations in the range scale by the number and distribution of observation stations, and irrecoverable vegetation depletion [14]. With the rapid development of aerospace and remote sensing technology, remote sensing imagery data have gradually become an important means for monitoring terrestrial ecological environments due to their recording characteristics of long-term, macroscopic, and periodic monitoring [15; 16]. In this sense, the range limitation of field-testing methods can be solved by establishing estimation models with the combination of remote sensing data, providing a fast and effective way to monitor large-scale vegetation NPP [14; 17; 18]. The vegetation NPP estimation models can be broadly classified into statistical, parametric, and process models [19; 20; 21]. These three types of models were established sequentially from perspectives of climate, light energy utilization, and biogeochemical cycles, and the effective calculation at a global scale was achieved by combining remote sensing data of WorldView, Landsat, MODIS, and NOAA [22; 23]. For example, the National Aeronautics and Space Administration (NASA) MOD17 product uses the BIOME-BGC model to simulate global vegetation NPP by quantifying the effective range of biomes and climate-induced shifts and distinguishing total vegetation production and autotrophic respiration processes, thereby avoiding the complexity of canopy micrometeorology and carbon balance theory [24; 25; 26]. These datasets have been validated and applied in investigations in different regions of the world. Researchers have obtained numerous results globally and locally by revealing the trends of NPP through modeling methods [27; 28; 29]. In the context of global climate change, owing to differences in characteristics such as geographical location, ecosystems have different responses to changes in temperature (TEM) and hydrological conditions depending on the region [30; 31; 32; 33]. This distinct feature restricts the carbon sequestration potential of vegetation, resulting in large differences in the response of terrestrial NPP to climate change worldwide. Over the past 20 years, the global NPP generally displayed an increasing trend with significant improvements in tropical rainforests in Africa [34], subtropical regions [35], and the alpine Tibetan Plateau in China [36].