# Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics
## 1 Introduction
Climate change: It has been proposed that the impact of CO\({}_{2}\) on GPP in the tropics may be offset by the effects of climate-related changes. Globally, abundant rainfall and sunlight favor vegetation growth and increases in GPP [17; 22]. However, different regions in the tropics also have different temperature and rainfall patterns, which may lead to different carbon cycle responses; further analysis is therefore required to determine the impact of these changes on GPP in specific regions [27]. Nitrogen deposition: The carbon balance of most terrestrial ecosystems is affected by nitrogen deposition, and in the case of the tropics, tropical forests do not respond significantly to nitrogen deposition due to phosphorus limitation [28; 29]. Land cover change: [PERSON] et al. showed that land cover change negatively affects GPP on a global scale [30]. In the tropics, land cover change has more pronounced negative impacts in southeastern South America, eastern and central Africa, and the Indian Peninsula, with slightly positive impacts observed in Southeast Asia. (3) Methods for estimating GPP: Methods for estimating regional and global GPP are broadly categorized into observation-driven and model-driven approaches [31]. The first category, observation-driven methods employing machine learning algorithms, scales tower-based GPP to regional and global levels, such as random forest [32]. These models rely heavily on extensive datasets of field observations for training. A paucity of training data can significantly impair the precision of these models' GPP estimates. The second category involves estimating GPP from satellite-based photosynthetic proxies, such as Light Use Efficiency (LUE) and Solar-Induced Fluorescence (SIF) models [33; 34]. The dependability of GPP estimates using observation-driven methods hinges largely on the quality and quantity of the observations used to train the models. Such models, which involve multiple parameters and inputs, are inherently complex. This complexity can introduce uncertainties, particularly when parameters are set based on broad vegetation types, potentially impacting the accuracy of regional and global GPP estimates [35]. Despite the advancements in data-driven models, remote sensing models based on LUE remain predominant in multi-scale GPP estimation due to their greater data accessibility and fewer parameters [36]. Accurately determiningthe actual LUE is both a challenge and a prerequisite for reducing uncertainties in GPP estimation [37]. Compared to traditional LUE models, the original P model exhibits superior performance and has been successfully applied to GPP estimation across various regions [38]. Nevertheless, the original P model does not account for the impact of water stress on GPP [39]. Therefore, numerous scholars have proposed various enhancements to the original P model. For instance, the P model by [PERSON] et al., which calculates absorbed photosynthetically active radiation (APAR) using a big-leaf algorithm, fails to distinguish between sunlit and shaded leaves, potentially leading to inaccuracies in capturing the actual APAR [40]. Consequently, this paper utilizes [PERSON] et al. 's improved P model, which integrates five water stress factors, thereby significantly enhancing the accuracy of GPP estimation [36]. Although datasets for global Gross Primary Productivity (GPP) estimation have been available for some time, each dataset relies on assumptions. Whereas most regions in the tropics have conditions conducive to photosynthesis, GPP estimation exhibits significant variability and uncertainty. The GPP products from different satellites exhibit significant variability [41], which still needs further analysis. Previous studies have not thoroughly analyzed how various influencing factors contribute to GPP, often overlooking the interactions among variables. This gap may hinder accurate modeling and prediction of ecosystem behavior. To address these deficiencies, our study adopts an innovative approach by utilizing the improved P model to estimate GPP.