## 1 Introduction  
Understanding the intricate dynamics of land carbon fluxes is essential in grasping the Earth's carbon cycle and its implications for global climate change [1; 2]. There are several types of carbon fluxes, such as gross primary production (GPP), terrestrial ecosystem respiration (RECO), and net ecosystem exchange (NEE). GPP signifies the total assimilation of carbon dioxide by plants through photosynthesis, while RECO represents the release of carbon dioxide by ecosystems through respiration processes [3; 4]. On the other hand, NEE refers to the net carbon exchange between ecosystems and the atmosphere, which can be positive (carbon release) or negative (carbon uptake) [5; 6]. These crucial carbon fluxes play a significant role in regulating the global carbon balance and are influenced by diverse factors such as climate, vegetation type, and land use changes [7; 8]. To enhance our understanding of land carbon fluxes and their intricate interactions, researchers have employed diverse datasets obtained through various methodologies. These include remote sensing observations, ecosystem modeling, and machine learning techniques [9]. In recent years, advancements in remote sensing technology have significantly contributed to our knowledge of global photosynthesis and to theories, simulations, and observations of carbon fluxes. Among the widely used remote sensing products is the MODIS satellite, which provides global GPP products at 8-day intervals, with a spatial resolution of 500 m, based on light use efficiency (LUE) [10]. To address the errors and uncertainties arising from mismatched input data scales and data quality issues, [PERSON] et al. modified the input meteorological data of MODIS products and other relevant parameters [11]. Additionally, the VPM (vegetation photosynthesis model) remote sensing product utilized an improved light use efficiency model, incorporating remote sensing variables and NCEP (National Centers for Environmental Prediction) reanalysis data. The algorithm related to vegetation indices was also enhanced, thereby addressing the limitations of previous GPP products [12]. Another valuable remote sensing product, the Global Land Surface Satellite product (GLASS), based on remote sensing data, offers high-resolution global data for 12 variables, dating back to 1981. Notably, GLASS data exhibit good spatial continuity and contain no missing values [13]. Many ecosystem models have been developed to describe the land carbon cycle in ecosystems, and these models can also respond to different climatic and atmospheric conditions [14]. Models based on light use efficiency have proven effective in improving estimates of GPP [8; 15]. For example, [PERSON] et al. used an eddy covariance-light use efficiency model to generate high-resolution GPP products, accurately representing both spatial and temporal patterns [16; 17; 18]. A process-based model combining atmospheric, radiative, photosynthetic, transport, and energy balance factors was used to estimate global GPP and evapotranspiration (ET) by utilizing FLUXNET 2015 data and MODIS data, achieving the same spatial resolution as the MODIS product [19; 20]. Moreover, [PERSON] et al. addressed the uncertainty in estimating GPP from solar-induced chlorophyll fluorescence (SIF) by exploring eight different forms of the relationship between SIF and GPP. They obtained a high-resolution GPP global dataset at 0.05\({}^{\circ}\) resolution, which exhibited a strong fit to the observed data [21]. [PERSON] et al. developed a remote sensing-based model for predicting RECO driven by multiple MODIS remote sensing data in northern China and the Tibetan Plateau region [22]. [PERSON] et al. presented three models for predicting RECO and employed statistical methods to assess model and parameter uncertainties, providing valuable references for RECO modeling in terms of data and methods [23]. [PERSON] et al. developed a predictive model for RECO in grassland ecosystems in northern China, integrating factors such as temperature, humidity, and productivity.