## 1 Introduction  
Gross primary productivity (GPP) is the comprehensive product of vegetation fixing CO\({}_{2}\) through photosynthesis [1]. As an important factor in terrestrial carbon cycle research, it plays a crucial role in the biosphere [2]. Under the goal of \"carbon neutrality\", achieving a precise measurement and estimation of GPP is not only of profound significance for understanding the mechanisms of the carbon cycle [3; 4], but also plays an important role in determining the comprehensive response of CO\({}_{2}\) to climate change. Currently, the main methods for measuring and estimating GPP include model simulations, ground-based observations, and satellite-based remote sensing. Model simulations primarily refer to methods based on light use efficiency (LUE) models [5; 6]. Due to differences in underlying surfaces, variations in vegetation structure, and the comprehensive impact of climate change, the structure and related parameters of LUE models are difficult to accurately construct and quantify, resulting in certain limitations in this method. Ground-based observations primarily refer to the eddy covariance (EC) measurement technique [7]. The EC method measures the net ecosystem exchange (NEE) through flux towers [8], and then decomposes NEE into GPP and ecosystem respiration (Reco) [9]. Due to the limited coverage area of EC measurements, the EC method needs to use related approaches to upgrade the measurement scale when conducting large-scale GPP estimate [10; 11]. The upscaled GPP also has certain limitations, such as incomplete driving factors, excessive parameters, and constraints from the original EC flux towers. Therefore, to achieve a precise measurement and estimation of GPP and to promote the realization of the \"carbon neutrality\" goal, it is necessary to further explore other GPP measurements or alternative methods. Chlorophyll fluorescence is a light signal produced when chlorophyll molecules move from a low-energy state to a high-energy state and then return to the low-energy state. The wavelength of this fluorescence is approximately in the range of 650-800 nm, which is also referred to as solar-induced chlorophyll fluorescence (SIF). SIF encompasses the spectra contributed by both Photosystem I and Photosystem II. Compared with traditional vegetation indices, SIF can better reveal the dynamic changes and carbon cycling processes of GPP. SIF has become one of the hot research topics in the field of vegetation remote sensing [12]. For example, [PERSON] et al. [13] conducted linear and spatiotemporal analyses using real-time tower-measured GPP from FLUXNET in 2015 and two types of SIF (CSIF and GOSIF), demonstrating that both showed a positive correlation across different spatiotemporal scales. [PERSON] et al. [14] applied SIF and GPP to characterize the spatiotemporal features and regulatory factors of terrestrial ecosystems in China from 2007 to 2018. They compared the spatiotemporal consistency of SIF and GPP between arid and nonarid regions by analyzing different climatic zones. [PERSON] et al. [15] characterized the response changes of SIF and GPP to drought processes under arid conditions. [PERSON] et al. [16] coupled SIF and GPP satellite products and applied multiple indicators to demonstrate the reduced lag effect of GPP in grasslands in arid regions. [PERSON] et al. [17] demonstrated the advantages of using SIF and GPP data with multiple indicators to represent the water storage sensitivity of desert vegetation in arid and semi-arid regions. [PERSON] et al. [18] integrated remote sensing data with multiple indicators and showed that SIF is more effective in representing the GPP of various systems in arid areas than normalized difference vegetation index and vegetation optical depth. However, given the increasingly important role that SIF satellite products play in GPP estimation, many scholars have begun using models, machine learning, and other methods to generate SIF satellite products with different spatiotemporal characteristics from different platforms.