## 1 Introduction  
Traditional vegetation indices (VIs) derived from remotely sensed data have been used effectively in several previous studies in the field of terrestrial ecosystem research [1; 2; 3]. However, these 'greenness'-based VIs (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI)) only capture changes in vegetation canopy reflectance attributable to leaf loss or chlorophyll content [4]. Furthermore, some of these VIs can experience a saturation effect in cases where the target vegetation is particularly dense. As an alternative, solar-induced chlorophyll fluorescence (SIF) has been widely used in studies concerned with monitoring vegetation physiology, given its functional connection with plant photosynthesis [5; 6; 7; 8]. The terrestrial ecosystem carbon cycle has been studied for decades by using time serious remote sensing data, as the cycle is directly related to the actual photochemistry of plants [9; 10]. SIF is a faint chlorophyll fluorescence signal (representing less than 5% of the total light absorbed by plants), which is emitted by the dissipation of excess light during the absorption process [7; 11; 12]. SIF has been widely proposed as a proxy measurement of plant photosynthesis and as an input for terrestrial primary productivity models [7]. A closed linear relationship between SIF and gross primary production (GPP) (SIF-GPP) has been evidenced both at regional and at global scales [12; 13; 14; 15], as SIF contains information on both the absorption of photosynthetically active radiation (PAR) and energy partitioning in photosystems [4; 16]. [PERSON] et al. [17] argued that in the cases of both C3 and C4 plants, the relationship between SIF and GPP is nonlinear. A strongly non-linear relationship between SIF and GPP was also found in temperate evergreen needle leaf forests, likely due to the different responses to absorbed photosynthetically active radiation (APAR) and air temperature particular to the plant species found in these areas [18]. This non-linear relationship may also be due to the lack of saturation effect observed in SIF under conditions of high light and dense vegetation, whereas VIs such as GPP are susceptible to saturation effects [17]. The differences in fundamental processes between SIF and GPP may also be a contributing factor [19]. Since SIF is not susceptible to saturation effects [20], there has been extensive research concerned with densely vegetated areas, such as forests and croplands [6; 12; 21]. The close linear or non-linear relationship between SIF and canopy GPP can be used to monitor vegetation photosynthesis, as well as the carbon cycle, at regional and global scales [18; 22]. SIF also offers advantages in the study of vegetation phenology because it is sensitive to both the initial and final stages of photosynthesis, which are not mechanistically linked to leaf greenness [6; 23]. SIF may better detect water stress and temperature stress in vegetation than VIs, which rely on the spectral reflectance signatures produced by changes in canopy structure [24]. In tropical rainforests, SIF decreases in the dry season, resulting in a decrease in GPP, which indicates that SIF varies with water availability and that drought weakens the ability of vegetation to engage in carbon fixation [4]. A meta-analysis also revealed that drought stress, accompanied by a decrease in SIF, as well as heat stress (which is typically associated with drought stress), usually causes a decline in SIF [25]. [PERSON] et al. [24] used soybean plants in an experiment designed to explore the response of SIF yield to temperature stress. Experimental results indicated that, beyond a certain threshold, yield decreased as temperatures increased, revealing a negative correlation in instances where temperature exceeded the growth optimum. They also found that the SIF value was affected by canopy structure. Hitherto, SIF data have been derived from numerous sensors.