## 1 Introduction
For instance, the adaptive ability of low-latitude functional vegetation during migration is relatively inferior to that of high-latitude functional vegetation and exhibits greater LAI variability [31]. Nonetheless, low-latitude functional vegetation, especially in tropical monsoon and rainforest climate areas, has generally higher forest biomass densities in tropical rainforest climate zones than high-latitude functional vegetation [42]. Moreover, the study showed forests and grasslands with different vegetation functions had high spatial heterogeneity in the four vegetation parameters of gross primary productivity (GPP), net primary productivity (NPP), LAI, and vegetation cover (FVC). This was manifested by different rates of vegetation recovery, vegetation productivity, and ecosystem stability [43]. Mainly influenced by climatic drivers, the differences in urban forest functions are also reflected in many aspects such as green density, green intensity, and biodiversity [44]. Linking vegetation characteristics obtained from remote sensing to vegetation functions can effectively improve spatial distribution ecological modeling and assessment [45]. Hence, it is essential to utilize zoning specifically for vegetation functions to investigate the spatio-temporal variability of vegetation dynamics and further explore the correlation between the vegetation dynamics and the underlying drivers. Since the dynamic change of LAI can be used as a reflection index and differentiation basis for vegetation function [46], this study suggests a new approach to divide vegetation pixels into distinct vegetation functional areas. The algorithm used in this study is an algorithm extended in hyperspectral image classification to multi-temporal image classification. The implementation process takes China as an example, and the derivative product is named China Vegetation Functional Zones (CVFZ). Furthermore, several prior studies have only been conducted at a single-pixel or small-area scale [47; 48; 49]. However, given that most long-term changes in vegetation dynamics are nonlinear, non-stationary, and complex [50; 51], analysis focused solely on a single-pixel or small-area scale may inaccurately reflect or even underestimate vegetation's response mechanism to climate change [52; 53]. To accurately analyze vegetation behavior that is difficult to detect using terrestrial methods [54; 55], studies carried out at large-scale spatial scales for ecosystems and biodiversity and considering the importance of diagnosing and responding to global climate change are necessary. In this study, we investigate the spatio-temporal variability of vegetation dynamics in China from 25 June 2000 to 25 June 2022, as reflected by satellite LAI, through the classification perspective of multi-temporal images. The average LAI in China during this period was 1.04 m\({}^{2}\)m\({}^{-2}\) with a standard deviation of 1.00 m\({}^{2}\)m\({}^{-2}\) (Figure 1). Our study is divided into two main parts. The first part involves the production and validation of CVFZ, which is based on Vertex Component Analysis-Maximum Likelihood Classification (VCA-MLC). The vegetation pixels in China are divided into 10 regions through CVFZ, and cross-validation with other land cover classification maps indicates the need for the functional zoning of vegetation. In the second part, we conduct a large-area scale analysis, using CVFZ to refine other land cover classification maps (we use MCD12Q1 as an example in this paper) and obtain a more detailed land cover classification map (MCD-CVFZ) that can distinguish vegetation functions. Finally, we extract the subregion LAI mean time series according to the MCD-CVFZ and separately analyze the annual and intra-annual fluctuations of vegetation dynamics through Seasonal-Trend Decomposition based on the Loess (STL) method. Figure 1: Temporal average of MODIS LAI in China, 25 June 2000 to 25 June 2022.