## 1 Introduction  
Gross primary production (GPP) represents the cumulative quantity of carbon that an ecosystem sequesters via photosynthesis or chemosynthesis within a specific time-frame [1,2]. It is a crucial indicator for measuring ecosystem productivity and the carbon fixation capacity, playing a vital role in understanding the global carbon cycle, ecosystem functions, and their responses to climatic variation. At present, the approaches utilized for measuring and estimating GPP chiefly consist of direct observation techniques, ecological modeling methods, and remote sensing estimation methods. Direct observation methods primarily gauge GPP through quantifying the quantity of carbon dioxide that plants take in when exposed to light. The commonly used equipment for this method is the eddy covariance system. However, it is costly and suitable only for local areas, making large-scale monitoring impossible [3]. Ecological modeling methods mainly use ecological models, combining climate data, soil characteristics, vegetation coverage, and other parameters to simulate and estimate GPP. When applying the model, a large amount of data need to be input, and its accuracy is affected by the input data and its own structure, which may lead to certain uncertainties [4, 5, 6]. Remote sensing estimation methods mainly use satellites, drones, and other remote sensing technologies to measure parameters such as surface reflectance spectra and chlorophyll content, and then combine them with relevant machine learning algorithms to estimate GPP. Nevertheless, the accuracy of some remote sensing data can be influenced by factors like ground conditions and climate, which may limit their ability to accurately capture small-scale GPP variations [7]. Therefore, in order to more effectively uncover the spatiotemporal changes and influencing elements of GPP and facilitate the formulation of \"carbon reduction\" policies, it is of great necessity to conduct a more in depth exploration of other comprehensive approaches for calculating GPP. Solar-induced chlorophyll fluorescence (SIF) is a phenomenon where plant leaves, after absorbing light energy under illumination, convert a port of the energy that is not used in the chemical reactions of photosynthesis into fluorescence and re-emit it [8]. It has been confirmed that SIF can effectively characterize GPP. For example, [PERSON] et al. [9] explored the feasibility of using SIF to effectively represent GPP in the Mediterranean region based on station flux data. [PERSON] et al. [10] analyzed the comprehensive characteristics of SIF satellite products in terrestrial ecosystems for GPP estimation and assessed the potential of SIF in carbon source/sink research. [PERSON] et al. [11] used the GPP/SIF ratio as a diagnostic indicator to assess the relationship and potential of using SIF for the integrated estimation of GPP across different ecosystems worldwide. However, these studies, which aimed to prove that SIF can be used for GPP's comprehensive characterization through different methods, rarely considered the comprehensive evaluation of different SIF satellite products in different ecosystems or the use of different methods to improve the accuracy of the selected optimal SIF satellite data. In addition, the commonly used physical model for inverting GPP based on SIF is the mechanical light reaction (MLR) model. This model mainly simulates how light energy is absorbed by pigments in plant leaves and participates in chemical reactions during photosynthesis by establishing dynamic equations for the light reaction stage [12]. In this model, photosynthesis is not only a biological process, but is also influenced by environmental factors and the internal characteristics of plants. However, research on GPP inversion based on this model mostly focuses on the C3 vegetation distribution area, and there is a lack of research on the C4 vegetation distribution area [13; 14; 15; 16; 17].