###### Abstract  
The net primary productivity (NPP) of vegetation holds a pivotal character for the global carbon balance as a key parameter for characterizing terrestrial ecological processes. The most commonly used indices for estimating vegetation NPP, for instance, the normalized difference vegetation index (NDVI), often suffer from saturation issues that can compromise the accuracy of NPP estimation. This research utilizes a new vegetation index based on the radial basis function (RBF) to estimate vegetation NPP in Chinese terrestrial ecosystems over the past two decades (2001-2020) and investigates the spatiotemporal variation characteristics of NPP and the driving mechanisms. The results indicate that the kernel vegetation index (\(k\)NDVI) can effectively alleviate the saturation problem and significantly improve the accuracy of NPP estimation compared to NDVI. Over the past two decades, the NPP of Chinese terrestrial vegetation ranged from 64.13 to 79.72 g C/m\({}^{2}\), with a mean value of 72.75 g C/m\({}^{2}\), showing a fluctuating upward trend. Changes in the NPP of terrestrial ecosystems in China are mainly affected by precipitation. The dominant factors influencing NPP changes varied over time and had different impacts. For instance, in the period of 2001-2005 the climate had a positive effect on NPP changes, with the dominant factors being evaporation and precipitation. However, in the period of 2010-2015 the dominant climate factors shifted to evaporation and temperature, and their effect on NPP changes became negative. The outcomes of this research aim to serve as a foundation for carbon cycle research and ecosystem environment construction in China. net primary productivity; kernel vegetation index; spatiotemporal variation; driving mechanism 2023152871118711871187118711871187118711871187118711871187118711871871187187118711871187187118718711871871187187187187187187187187187187187187187187187187187187187  
acquisition cost, and short revisit period. The models currently used for NPP estimation include statistical models, process models, and light energy efficiency models [5]. Statistical models typically rely on the vegetation index (VI) and leaf area index (LAI) for estimation, and the model data are relatively accessible. However, the physiological and ecological mechanisms underlying these models are not always well understood, which can lead to errors in the estimation results [6]. While process-based models, for example the Biome BioGeochemical Cycles model (BIOME-BGC), Boreal Ecosystem Productivity Simulator (BEPS), Terrestrial Ecosystem Model (TEM), and Integrated Terrestrial Ecosystem C-budget model (InTEC), have clear mechanisms and produce more accurate NPP estimates, their model structures are relatively complexity and require numerous parameters, which can be challenging to scale up to the regional level [7; 8; 9; 10]. Light energy efficiency models, such as the Carnegie-Ames-Stanford Approach (CASA) and Global Production Efficiency Model (GLO-PEM), have been developed [11; 12; 13], of which the CASA model is based on the theory of plant photosynthesis mechanism and has minimal dependence on ground measurement data. Additionally, the CASA model is relatively straightforward, with few input parameters and high ease of use and applicability, making it one of the most commonly utilized models for NPP estimation across various spatial scales [14]. Despite these advantages, the CASA model has deficiencies in simulating vegetation NPP, such as uncertainties in the estimation of vegetation light energy utilization. To address this, researchers have attempted to compensate for these deficiencies by constructing new water stress factors or correcting the maximum light energy utilization for ideal contexts, thereby improving the accuracy of vegetation NPP estimation [15; 16; 17]. Additionally, the nonlinear relationship between NDVI and vegetation NPP can be a limitation for accurate estimation of NPP using the CASA model [18; 19]. However, the use of NDVI to estimate FPAR has two main issues.