## 1 Introduction  
Vegetation is a crucial component of terrestrial ecosystems, connecting elements such as the atmosphere, water, biology, rocks, and soil. It is the most critical component of ecosystems, and all organisms depend on it [1; 2; 3]. Changes in ecosystems significantly affect the type, quantity, and quality of vegetation [4]. In particular, global warming has led to alterations in vegetation phenology events [5; 6]. The increased frequency of extreme climate events has resulted in severe negative impacts on agriculture, water resources, ecosystems, and socio-economic aspects [7]. Therefore, the long-term monitoring, research, and management of vegetation change are of great value [8]. Ecological problems frequently occur, especially in northern latitudes [9; 10]. As a result of economic and population growth, over half of the world's ecosystems have degraded. China, especially in the north, faces ecological issues such as land degradation, soil erosion, and desertification [11]. China has extensive latitude and longitude spans, rich vegetation, and abundant natural resources. Assessing the dynamic changes in vegetation, understanding the spatial heterogeneity of vegetation change and its interaction with extreme climate and various land use types, and predicting future vegetation development are essential. The Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Solar-Induced Chlorophyll Fluorescence (SIF) are three remote sensing products that provide valuable information about vegetation dynamics. The NDVI and EVI are closely related to vegetation cover, leaf area index, biomass, and land use, while the SIF directly reflects plant photosynthesis dynamics [12; 13; 14]. Given the importance of understanding vegetation dynamics in the context of climate change and ecological challenges, our study aims to assess the dynamic changes, driving mechanisms, and spatial model predictions via remote sensing of terrestrial vegetation in China using these three indices. We hypothesize that by combining these three indices, we can obtain a more accurate and comprehensive understanding of vegetation change and its driving factors. Traditional spatial prediction models, such as regression analysis, often have limited predictive power regarding the spatial distribution of remote vegetation sensing [15]. Moreover, while more studies are utilizing machine learning for spatial distribution prediction, these methods often need more explanation regarding the underlying physical mechanisms [16]. Notably, physical models can offer advantages in mechanism studies and yield better prediction results. In response to the need for more accurate and comprehensive vegetation monitoring and prediction, we propose using a physical model, the CA-Markov model, for predicting the spatial distribution of vegetation remote sensing products. The CA-Markov model combines the long-term prediction advantages of the Markov model with the ability of cellular automata to simulate spatial changes in complex systems, making it effective at analyzing and acting on land use changes [17]. However, few studies have employed this model to make predictions with vegetation remote sensing products, especially the EVI and SIF. Our study aims to assess the dynamic changes, driving mechanisms, and spatial model predictions of the remote sensing of terrestrial vegetation in China using the NDVI, EVI, and SIF. We will analyze the dynamic changes of these indices in China with a focus on exploring their relationships with meteorological, land use, and socio-economic factors to reflect the variability of vegetation growth characteristics and driving mechanisms more accurately. Our research will employ the CA-Markov model to simulate and forecast using the three vegetation remote sensing products (NDVI, EVI, and SIF) from 2020 to 2025, contributing to China's theoretical and technical system of terrestrial vegetation remote sensing.