###### Abstract  
The gross primary production (GPP) is important for regulating the global carbon cycle and climate change. Recent studies have shown that sun-induced chlorophyll fluorescence (SIF) is highly advantageous regarding GPP monitoring. However, using SIF to estimate GPP on a global scale is limited by the lack of a stable SIF-GPP relationship. Here, we estimated global monthly GPP at 0.05 spatial resolution for the period 2001-2017, using the global OCO-2-based SIF product (GOSIF) and other auxiliary data. Large amounts of flux tower data are not available to the public and the available data is not evenly distributed globally and has a smaller measured footprint than the GOSIF data. This makes it difficult to use the flux tower GPP directly as an input to the model. Our strategy is to scale in situ measurements using two moderate-resolution satellite GPP products (MODIS and GLASS). Specifically, these two satellite GPP products were calibrated and eventually integrated by in situ measurements (FLUXNET2015 dataset, 83 sites), which was then used to train a machine learning model (GBRT) that performed the best among five evaluated models. The GPP estimates from GOSIF were highly accurate coefficient of determination (R\({}^{2}\)) = 0.58, root mean square error (RMSE) = 2.74 g C\(\cdot\)m\({}^{-2}\), bias = -0.34 g C\(\cdot\)m\({}^{-2}\)) as validated by in situ measurements, and exhibited reasonable spatial and seasonal variations on a global scale. Our method requires fewer input variables and has higher computational efficiency than other satellite GPP estimation methods. Satellite-based SIF data provide a unique opportunity for more accurate, near real-time GPP mapping in the future. g 202  
commonly GPP estimates based on remote sensing data estimation models can generally be divided into three categories: data-driven models upscaled from eddy covariance, light use efficiency (LUE) models, and process-based models. (1) Data-driven models upscaled from eddy covariance require a combination of flux data, remote sensing data (vegetation index or leaf area index), and meteorological data. Their methods consist of two fundamental steps [4; 17; 18; 19]: (i) Using complex statistical methods to establish a relationship between GPP and explanatory variables, and (ii) using gridded remote sensing and meteorological data to calculate a space-time pattern of GPP, based on the established relationship. Data-driven models, however, can feature high uncertainty. For example, the relationships constructed by statistical models may only have regional applicability, the time scales of observational data can affect the time scales of GPP products, and there is no mechanism for describing plant physiological processes [17]. (2) LUE models are commonly used to estimate GPP. It is believed that under suitable environmental conditions (temperature, moisture, and nutrients), the amount of carbon sequestered by photosynthesis is directly related to the amount of solar photosynthetic effective radiation absorbed by plants [20; 21]:  
\[\text{GPP}=\text{fPAR}\times\text{PAR}\times\varepsilon_{p} \tag{1}\]  
Here, PAR is the photosynthetically active radiation received by the plant canopy, fPAR is the proportion of PAR absorbed by plant canopies, and \(\varepsilon_{p}\) is the light energy efficiency of photosynthesis. These indicators are, however, often disturbed by the environment. fPAR is usually based on vegetation index [22; 23] or fPAR remote sensing products [24]. These indicators are often disturbed by the environment. When vegetation is under stress (for example by extreme temperatures or limited water), they are not sensitive to rapid changes in plant photosynthetic status [25; 26]. At the same time, \(\varepsilon_{p}\) exhibits greater variability in a single biological community [27; 28]. (3) Process-based models involve mechanisms of plant ecology as series of nonlinear equations to represent the atmosphere-vegetation-soil system and associated fluxes simulating plant growth.