###### Abstract
These are characterized by complex interactions between vegetation and atmospheric processes, such as photosynthesis, respiration, transpiration, and evaporation [29; 30; 31; 32]. However, many of the model parameters are difficult to obtain, and the estimation of GPP is still subject to significant uncertainties. Currently, data-driven models are considered to be the most accurate for estimating GPP. However, owing to the limited number of flux sites and their uneven distribution, global GPP spatial and temporal patterns cannot be obtained directly through the spatial expansion method. Chlorophyll fluorescence, which is a byproduct of photosynthesis, responds immediately to disturbances in environmental conditions, such as light and water. Therefore, it can directly reflect the photosynthetic activity of plants, compared to the reflectance-based vegetation index that is traditionally used for vegetation remote sensing applications. Furthermore, it can be used as a remote sensing index for estimating photosynthetic energy conversion and carbon absorption [7; 33; 34; 35]. In recent years, chlorophyll fluorescence analysis has been shown to be an effective method to monitor the photosynthesis of vegetation from space [25; 36; 37; 38]. It can rapidly evaluate the growth status of plants, and its coupling with physiological processes is more closely related to dynamic changes in physiological functions and light energy utilization than the normalized difference vegetation index (NDVI) or enhanced vegetation index [39; 40]. Recent studies have shown that sunflower chlorophyll fluorescence (SIF) has great advantages regarding GPP monitoring, and can better track the seasonal and inter-annual changes of GPP for different plant functional types [9; 25; 41; 42; 43; 44]. There are two approaches to estimating GPP based on SIF: one is to establish a direct empirical linear model of the two [7; 9; 45; 46; 47; 48; 49; 50; 51], and the other is based on the Soil-Canopy-Observation of Photosynthesis and the Energy Balance (SCOPE) model [3; 52; 53; 54]. The latter, however, has a large number of input parameters, which limits its application on a large scale. Studies have shown the relationship between SIF and GPP, and have used this relationship to prove the ability of SIF to estimate GPP [9; 35; 55]. However, species-specific physical and physiological traits mean that the relationshipsbetween ecosystem GPP and SIF are biome-dependent [7; 33; 56]. Meanwhile, most studies in this field have been based on space-borne and airborne technologies, which ignore the influence of canopy structure on the relationship between SIF and GPP. This makes it difficult to explain clearly with a simple SIF-GPP linear relationship [57; 58; 59; 60]. Furthermore, it is difficult to obtain high-quality SIF data with appropriate spatial and temporal resolutions, and it is difficult to quantitatively assess the factors that influence the relationship between SIF and GPP. Although SIF and GPP have a significant relationship at the regional scale, the mechanisms that drive and determine the relationship between them, reflect complex underlying physiological processes, which prohibit the use of SIF for estimating GPP across larger scales. The purpose of this study was to estimate global GPP from the global, OCO-2-based SIF product (GOSIF) and other auxiliary data using machine learning techniques. In situ GPP measurements from flux towers are small in volume, not evenly distributed globally and has a smaller measured footprint than the GOSIF data (0.05\({}^{\circ}\)). It cannot be used directly for model building. Here, in situ measurements were scaled through two fine-resolution (500 m) satellite GPP products: Moderate Resolution Imaging Spectroradiometer (MODIS) and Global LAnd Surface Satellite (GLASS).