## 1 Introduction
They used MODIS surface data to simulate RECO, offering a comprehensive understanding of the climatic and environmental influences on RECO in grassland ecosystems [24]. Moreover, [PERSON] et al. estimated seasonal variations in GPP, RECO, and NEE in a tropical forest in Brazil. Their results revealed that carbon fluxes were influenced by precipitation and vegetation distribution, while NEE remained balanced even during the dry season [25]. [PERSON] et al. employed soil and air temperature, radiation, and leaf area index (LAI) to model and estimate the distribution ofGPP, RECO, and NEE in western Siberia, demonstrating that the studied ecosystem acted as a carbon sink [26]. [PERSON] et al. investigated the relationship between carbon fluxes and climate variables during different phases of the El Nino-Southern Oscillation (ENSO) by utilizing carbon flux outputs from a global ecosystem model [27]. [PERSON] et al. used the biogeochemical Carnegie Ames Stanford Approach (CASA) model to develop a dataset of GPP, RECO, and NEE for North America [28]. Data-driven machine learning methods are playing an increasingly significant role. This approach typically uses satellite remote sensing data, meteorological data, and other datasets related to land carbon fluxes to construct machine learning models. Numerous research projects have utilized a variety of machine learning models to forecast carbon fluxes. Among these projects, the FLUXCOM project has been widely adopted [29]. The model tree ensemble approach, which relies on remote sensing and meteorological data, represents a significant advancement in global land carbon flux estimation [30]. The resulting dataset can be used to evaluate existing models of land surface processes and the biosphere's condition [31]. [PERSON] et al. evaluated the performance of a random forest model for different target variables across various regions [32]. Their study employed 11 machine learning algorithms from four major classes, including tree models, kernel function methods, neural networks, and regression splines. The outputs were compared to the results of existing models [33; 34]. Prediction results using a random forest model demonstrated its superiority over the MODIS product in certain areas [35]. [PERSON] et al. utilized a modified regression tree to predict net ecosystem carbon exchange in the United States [36]. A cubic regression tree model based on MODIS remote sensing data and reanalysis meteorological data was used to predict global SIF and GPP [37]. A tree ensemble model was also employed to generate a national dataset for China [38]. [PERSON] et al. employed a random forest model to upscale GPP, RECO, and NEE data from the FLUXNET2015 dataset to derive global carbon flux products spanning 1999 to 2019 [39]. Neural network models employing remote sensing SIF, along with other radiometric and meteorological data as predictors, have also been employed to predict global GPP [40]. Furthermore, a support vector regression model can produce GPP and NEE products on both site and spatial scales within Asia [41]. For the aforementioned three types of land carbon flux products, it is necessary to evaluate the carbon fluxes derived from their predictions due to the differing methodologies employed by these products. With the rapid advancement of machine learning technology in carbon flux simulation, a notable example being the recently released Global Carbon Flux Dataset (GCFD) product [42; 43], it is crucial to also examine the reliability of products generated using this technology. This study aimed at comparing three distinct types of products, including machine learning products, ecosystem model products, and remote sensing products, to further assess whether the carbon flux products obtained through machine learning algorithms adequately reflect the realistic distribution of carbon fluxes. The objective was to enhance the reliability and applicability of carbon flux products predicted by machine learning algorithms.