###### Abstract
First, the relationship between NDVI and vegetation NPP is nonlinear and easily reaches saturation, which may affect the accuracy of NPP estimation. Second, NDVI is mainly reflective of vegetation leaf conditions rather than actual photosynthesis. Several studies have proposed using alternative vegetation indices that are less affected by saturation, such as the NDVI2[20], red-edge normalized vegetation index [21], and enhanced vegetation index (EVI) [22]. While these methods may improve the estimation accuracy of vegetation NPP to an extent, they cannot fundamentally address the nonlinear relationship and saturation issues between vegetation indices and NPP. Solar-induced chlorophyll fluorescence (SIF) is a promising alternative to traditional reflectance-based vegetation indices, as it directly measures the fluorescence emitted by plants during photosynthesis [23]. Recently, SIF has been increasingly used as a tool for estimating vegetation carbon stocks due to its ability to directly measure plant photosynthetic activity. However, there remains much debate among scholars regarding the relationship between SIF and vegetation green biomass. Additionally, the spatial resolution of SIF is limited, with values ranging from 40 \(\times\) 80 km\({}^{2}\) to 0.05\({}^{\circ}\), which makes it difficult to obtain detailed information on green vegetation biomass [24]. Therefore, the construction of a new remotely sensed vegetation index to improve the accuracy of NPP estimation by solving the problem of linearity and saturation is a pressing issue. Previous research has attempted to improve the performance of vegetation indices using empirical or parameter transformations. However, these methods only construct new vegetation indices that try to utilize information from bands other than the NIR and red bands, and it is not clear whether all available information is being used. Moreover, these methods fail to address the saturation problem that occurs in vegetation indices. In contrast, the kernel function approach takes a machine learning perspective and can derive nonlinear approaches from linear algorithms while remaining reliant on linear arithmetic for operations [25]. This approach is universal, meaning that any index of kernel vegetation embedded in a Gaussian kernel can optimize the use of spectral information, leading to lower uncertainty in the propagation of kernelized vegetation indices compared to non-kernelized ones. Therefore, in this study, a new vegetation index is used to address the saturation phenomenon in estimating the vegetation NPP of Chinese terrestrial ecosystems over the past two decades, then the Theil-Sen median and Mann-Kendall trend are used to explore the trends of vegetation NPP in Chinese terrestrial ecosystems. Second-order partial correlation, multiple correlation analysis, the differential equation method, and the PLS-SEM method are used to analyze the role of each influencing factor on the change in vegetation NPP in Chinese terrestrial ecosystems. This study can provide a valuable scientific reference for ecological environmental conservation, carbon cycle, and resource allocation research in China.