## 1 Introduction  
Photosynthesis, which supports most life on earth [1], is a vital process driving the global carbon cycle [2]. The explicit estimation of the photosynthetic exchange flux, known as gross primary productivity (GPP), can offer key information for exploring and quantifying terrestrial carbon fixation by plants [3]. The eddy covariance (EC) technique makes it possible to measure the carbon dioxide (CO\({}_{2}\)) exchange between vegetation and the atmosphere and partition it into GPP and ecosystem respiration [4]. However, EC flux towers are dispersed and only cover very limited regions. Remote sensing offers the unique possibility of deriving the continuous global monitoring of carbon exchange. Currently, global GPP can be estimated using a light use efficiency (LUE) model, which combines reflectance-based remote sensing and climate variables [5; 6]; a data-driven method, which integrates EC flux observations, remote sensing and climate data [2; 7]; process-based models [8; 9]. However, uncertainties in the diagnostic models, measured variables, and auxiliary products all propagate into the global GPP estimation, which limits the monitoring power of these methods [2; 10]. The remote measurements of sun-induced chlorophyll fluorescence (SIF) offer a new tool for monitoring vegetation photosynthesis. SIF refers to the emission of red and far-red light from chlorophyll during the absorption of photosynthetically active radiation (PAR) under natural sunlight. As a byproduct of photosynthesis, SIF has been shown to have more direct and strong links with the GPP than with reflectance-based data [11; 12; 13]. Extensive studies have demonstrated that the spatial and temporal patterns of space-based SIF have linear correlations with those of GPP, although the correlations are biome specific [14; 15; 16]. In the last decade, several ground-based and simulation-based studies have verified empirical SIF-GPP correlations but found that the correlations are highly dependent on the spatial and temporal scales [17; 18], plant functional type [19], canopy structure [20], and illumination conditions [21]. More recently, with the successful retrieval of SIF from satellite sensors (e.g., References [14; 15; 22; 23; 24; 25]), the space-based SIF has been successfully used to estimate GPP at global scales. In diurnal or seasonal cycles, SIF and GPP are both highly variable and regulated by many dynamic factors (e.g., the incident light intensity, solar zenith angle (SZA), canopy structure and vegetation amount) and meteorological factors or species that affect the efficiency of light used for photosynthesis and fluorescence. Therefore, the temporal scales of SIF and GPP should be noted when investigating the SIF-GPP relationship. To date, satellites can only record very few SIF observations per month. Tower-based systems combining continuous SIF and EC flux measurements are rare [26]. Thus, most observed SIF signals and products are basically instantaneous (with a unit of mW/m\({}^{2}\)/nm/sr), which cannot represent the real variation in photosynthetic capacity during an integrated time period. However, detecting the global carbon cycle always requires the integrated GPP for one day (with a unit of umolCO\({}_{2}\)/m\({}^{2}\)/d or gC/m\({}^{2}\)/d). Previous studies have mainly investigated the relationship between instantaneous SIF (SIF\({}_{\text{inst}}\)) and daily integrated GPP (GPP\({}_{\text{daily}}\)) without regard to the differences between the instantaneous and daily scales. The question arises as to whether the temporal mismatch between SIF\({}_{\text{inst}}\) and GPP\({}_{\text{daily}}\) impacts the spatio-temporal patterns of SIF-GPP correlations. Despite the temporal mismatch between SIF and GPP existing in many SIF-GPP studies, few studies have focused on this issue.