## 1 Introduction  
Plant photosynthesis, termed gross primary productivity (GPP), is the main source of energy for all life on Earth [1], and it drives ecosystem functions and carbon cycling [2]. Accurate estimations of GPP play important roles in the global land carbon budget in the context of global warming [3; 4]. As the most effective method of estimating regional-scale GPP, remote sensing has been widely used in carbon cycle studies over the last few decades. In traditional remote sensing GPP estimation methods, GPP is calculated using light use efficiency (LUE) models based on vegetation indices (such as the normalized difference vegetation index [NDVI] and enhanced vegetation index [EVI]) and auxiliary meteorological data (such as temperature and solar radiation) as inputs. A variety of LUE models have been developed with different combinations of vegetation indices and environmental stress expressions, such as the vegetation photosynthesis model (VPM) [5], the moderate-resolution imaging spectroradiometer photosynthesis (MODIS-PSN) algorithm [6], the Carnegie Ames Stanford Approach (CASA) model [7], and the eddy covariance (EC)-based LUE (EC-LUE) model [8]. Vegetation indices based on reflectance can track the greenness well, however, they are less sensitive to actual variations in photosynthesis [9; 10; 11]. Vegetation indices such as NDVI cannot reflect diurnal physiological changes caused by heat and water stress [12]. LUE models such as the MODIS GPP algorithm require many variables, including auxiliary meteorological data, the use of these data can result in uncertainty to the estimations of GPP [13]. With the rapid development of remote sensing techniques, machine learning methods have become a very useful tool to process GPP-related data. Global flux products were simulated by using the machine learning methods to scale EC data up remote sensing data [14]. The basal areas of spruce and fir were mapped by using machine learning techniques in conjunction with remote sensing and measurement data in Central Siberia [15]. Sun-induced chlorophyll fluorescence (SIF) data have been reported to be a promising new tool to accurately monitor GPP [16; 17; 18]. Light energy absorbed by the leaf chlorophyll molecules has three different pathways: Photochemistry, nonphotochemical quenching (NPQ, i.e., heat dissipation), and a small amount of energy emitted as SIF at long wavelengths [19; 20]. Complementing reflectance-based vegetation indices, space-borne SIF observations are excellent indicators of the vegetation photosynthesis process and these observations provide a new method of monitoring photosynthesis from space [19; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. Recently, several studies evaluated SIF using GPP observed by the EC technique. SIF has been used to capture the seasonal variations in crops in Nebraska, USA [24]. Satellite SIF data have been shown to be correlated with the canopy photosynthesis at diurnal and seasonal scales in a mixed temperate forest in central Massachusetts, USA; thus, SIF can be used as a proxy for GPP [22]. SIF has been reported to accurately capture seasonal and interannual GPP dynamics across dryland ecosystems in Southwestern North America [32]. Global Ozone Monitoring Experiment-2 (GOME-2) SIF data have been used to monitor time-resolved photosynthesis in the US corn belt and European grassland, and reports have indicated that SIF-based crop GPP estimates are 50-75% higher than the results from state-of-the-art carbon cycle models [33]. Moreover, researchers have performed initial evaluations of the use of Orbital Carbon Observatory 2 (OCO-2 SIF) SIF to estimate GPP for crops in the Minneapolis-Saint Paul metropolitan area [34], grass at an OzFlux grassland site [35], and temperate forests in Northern Wisconsin, USA [31]. SIF-based GPP models have been rapidly developed [24; 36; 31], including a simple regression GPP model [33] and a nonlinear GPP-SIF model [31], and reports have indicated that temporal correlations with GPP can be improved by spatially downscaling SIF [37].