## 1 Introduction  
As the most widely distributed producer in terrestrial ecosystems, plants participate in the carbon cycle by converting light energy into chemical energy through photosynthesis. In the context of global climate change, extreme climate events occur frequently [1], which may lead to the degradation of ecosystems and the loss of service functions [2]. Vegetation gross primary productivity (GPP) plays an important role in the global ecosystem's carbon cycle and can judge the production capacity of vegetation. GPP is affected by climate change and vegetation activity has feedback on the climate [3; 4; 5]. The main remote sensing models currently used to achieve estimations of the GPP at regional and global levels include the light use efficiency model [6], the photosynthetic mechanism process model [7] and the vegetation index model [8; 9]. Among them, the vegetation index model has been widely used and developed because of its few input parameters and simple structure. With the development of spectral observation techniques, researchers have found that solar-induced chlorophyll fluorescence (SIF) can capture the photosynthetic physiological information of plants quickly and non-destructively [10]. Compared with the conventional vegetation indices, which are based on canopy structure or vegetation greenness--such as the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI) and the leaf area index (LAI)--SIF, as a by-product of light reaction in plant photosynthesis, is more sensitive to physiological changes in the vegetation canopy [11]; therefore, it has been widely used in stress monitoring, phenology change and GPP estimations [12; 13; 14; 15]. In recent years, SIF data have mainly come from satellite remote sensing [16; 17], in situ observation [18; 19] and unmanned aerial vehicle (UAV) systems [20; 21]. The instantaneous value collected by the satellite or UAV lacks the ability to analyze the temporal dynamics of SIF. The pixels identified by satellite sensors usually cover a large area, and the data are usually mixed with information from multiple species, which will lead to divergence of SIF and GPP [22]. The accumulation of data under different spatio-temporal resolutions can clarify the characteristics of SIF, which is crucial in improving data availability. The tower-based fluorescence observation system usually has a high spectral resolution and signal-to-noise ratio, and can realize the continuous observation of the red and near-infrared wavelengths of canopy scale SIF, which is the main data source for current research studies on ground SIF [23; 24; 25]. Ground-based data provide important validation data for the development of optical fluorescence models and the validation of satellite products. [PERSON] et al. established a GPP estimation mechanism model, driven by 760 nm canopy SIF, air temperature (Ta), photosynthetically active radiation (PAR) and other parameters [26]. The estimation results of this model showed higher accuracy compared with the observed GPP of winter wheat. [PERSON] et al. used ground SIF data to verify the improved model, BEPS-SIF, which was based on the boreal ecosystem productivity simulator. They applied the model to simulate and analyze SIF on a global scale [27]. Continuous canopy observations can clarify the change dynamics of SIF at different temporal scales and can have a better spatio-temporal identity with the ground data at ecosystem scales (such as the GPP and meteorological variables). This can provide a detailed and profound analysis of the coupling mechanism between fluorescence and photosynthesis [28]. The light energy absorbed by plant chlorophyll is released through a photochemical reaction, heat dissipation and fluorescence, which are all closely related to each other [29]. Environmental conditions will affect the proportion of the three pathways of energy dissipation after light absorption, which may change the emission of SIF [10].