###### Abstract
The near-infrared reflectance of vegetation (NIR\({}_{\rm v}\)), a recently proposed novel vegetation index, has been shown to correlate well with GPP [32]. It has the advantage of overcoming the sensitivity of the NDVI to the vegetation fraction and can refine vegetation's contribution to NIR reflectance no matter how sparse the canopies are and how bright the soil background is. Nevertheless, the common deficiency of the above VIs constructed based on the spectral characteristics of vegetation is that they are insensitive to photosynthetic processes that have not yet caused reflectance changes and do not correlate well with the photosynthetic activities of plants in the short term, making it difficult for them to accurately and timely reflect the dynamic changes in vegetation photosynthesis [33]. Several studies have proven that these indices perform poorly in ecosystems without high variability in greenness [34; 35]. The absorption of sunlight by vegetation for photosynthesis is accompanied by the re-emission of red and near-infrared photons, called solar-induced chlorophyll fluorescence (SIF). SIF, which is closely related to the two processes of nonphotochemical quenching and photochemical quenching, can capture transient photosynthesis in vegetation even if there is no change in greenness or structure. One study surprisingly discovered a strong spatiotemporal correlation between satellite-derived SIF and flux tower-derived GPP estimates [36]. This finding provides new insight for estimating ecosystem-scale GPP and encourages us to utilize SIF in carbon cycle research as a reliable proxy for GPP. However, as SIF products have only been available since 2000, the reasons for the variations in the relationship between SIF and GPP over time and space need to be clarified [37; 38; 39]. Accumulating evidence suggests that ecosystem-scale GPP is influenced by a combination of vegetation's biophysical properties [37; 38; 40; 41; 42; 43] and environmental factors [12; 44; 45]. Data-driven models based on machine learning are good at handling the interaction of individual factors and can achieve excellent prediction accuracy. It has been shown that supplementing the vegetation index with other information about vegetation can improve the prediction results of GPP models [35; 46]. On the other hand, the black-box nature of machine learning results in model performance that depends heavily on how the explanatory variables are combined. The relative performance of various VIs in predicting GPP and what additional factors should be combined with them to mechanistically better reveal the photosynthetic capacity of vegetation are still poorly understood. In this study, we used a remote sensing time series, including the GOSIF, MODIS NIR\({}_{\rm v}\), and MODIS LAI, and GPP observations from 197 flux towers to examine their ability to quantify the spatiotemporal variation in GPP. The objectives of this study are to (1) evaluate the performance of models in GPP estimation based on two widely used machine learning models, i.e., the random forest (RF) and back propagation neural network (BPNN) models; (2) generate several sets of global gridded GPP products for 2003-2018 based on the optimal machine learning models and compare them to previous products; and (3) systematically investigate the environmental factors and plant traits that best match each vegetation index in estimating global GPP.