## 1 Introduction  
GPP of vegetation refers to the total amount of organic carbon fixed through photosynthesis per unit time and area. GPP is a critical metric for assessing the capacity of ecosystems to provide material and energy support, forming an essential basis for studying and understanding ecosystem functions and processes [1]. Global forest ecosystems occupy approximately one-third (33%) of the Earth's total terrestrial surface area, encompassing an estimated 43 million square kilometers [2]. Forest GPP contributes about 48.5% of the total GPP of global terrestrial ecosystems [3], playing a pivotal role in global biodiversity, climate regulation, and carbon flux. In the context of rising greenhouse gas concentrations [4], accurately estimating forest GPP is vital for understanding forest ecosystem functions and addressing global environmental changes. GPP inversion involves using specific methods and auxiliary variables for extending limited measurements to a regional scale [5; 6]. This process requires selecting appropriate tools and variables, where \"tools\" refer to inversion methods, and \"variables\" denote the reference inputs [7]. Inversion tools encompass the Light Use Efficiency (LUE) model [8], terrestrial ecosystem process models [9], data-driven models based on Eddy Covariance (EC) observations [10], and ML and artificial intelligence [11]. These methods have shown great potential in solving complex and nonlinear problems in the RS field and are thus widely used in vegetation productivity inversion. For instance, [PERSON] et al. employed the Support Vector Machines (SVM) model to simulate and evaluate the GPP of cornfields in the northwest region, demonstrating SVM's high predictive accuracy and stability in that area [12]. [PERSON] et al. used Sentinel-2 and Landsat-8 data to estimate crop GPP through neural networks, achieving promising results [13], while [PERSON] et al. established a GPP regression model using meteorological data and LAI based on the RF algorithm [14]. Previous studies have demonstrated the high performance and accuracy potential of ML algorithms such as RF, SVM, and ANN (Artificial Neural Network) in addressing complex spatiotemporal prediction tasks [15; 16; 17]. However, effectively utilizing RS data with appropriate ML algorithms to enhance the accuracy of forest GPP estimation remains a challenging task, despite the notable successes and inherent limitations of these techniques. Selecting the appropriate ML algorithm and feature set poses a significant challenge, as different algorithms have unique strengths and weaknesses and are suitable for different data types and problems [18]. It is especially important to choose RS variables that accurately reflect the ecological and physiological characteristics of forests for GPP estimation [19]. Additionally, ML often faces issues with multicollinearity when processing RS image data. High correlations among variables can lead to instability in model estimation and reduce explanatory power, a problem that has not been extensively researched [20; 21]. Hyperparameter tuning is crucial for enhancing the performance of ML models, as unoptimized hyperparameters can lead to underfitting or poor generalization. However, finding the optimal parameter combination is often time-consuming and computationally intensive. This study proposes a robust optimization framework that systematically addresses these challenges through integrated tool selection, variable optimization, and hyperparameter fine-tuning mechanisms [17; 22; 23]. This study aims to utilize advanced model optimization algorithms to fine-tune ML models for constructing an accurate estimation model of the GPP of forest ecosystems in China.