## 1 Introduction
[28] partitioned the sunlit and shaded leaves of the canopy to improve the simulation of GPP and validated its application in six ecosystems. However, a previous comparative study based on 26 models showed that the structural differences between LUE-based models and process-based models did not affect their GPP estimations [29]. Compared to the use of improvements of the structural and process deficiencies of models, parameter adjustment is used more commonly to reduce the uncertainty of the simulation results [30; 31] as it involves negligible modification of the modelling mechanism [32]. For example, [PERSON] et al. [30] and [PERSON] et al. [33] found that adding the soil water factor to the LUE algorithm would make the adjusted LUE parameter more robust for sites with the same PFT and among different PFTs. The seasonal fluctuations of the maximum LUE parameters were considered to improve the capability of GPP variations [5; 34]. Much research on the evaluation of SIF-based GPP estimation has also been published in recent years. Some studies focused on comparing the uncertainties of SIF products derived from different retrieval algorithms and their effects on GPP estimation [11]. Evaluating the structural errors between linear and nonlinear (hyperbolic) GPP-SIF models is another way to explore the relationship between GPP and SIF [1; 35; 36]. Similar to the LUE-base models, in addition to the SIF-based structure, calibrating the parameters and studying the factors that affect the spatiotemporal relationship between SIF and GPP are also a first consideration [10; 37; 38]. As mentioned before, parameter tuning plays a vital role in the evaluations and developments of GPP models. This is because adjusting key parameters can compensate for the errors introduced by the model structure [39; 40]. It is known that the parameter adjustment was mostly addressed by calibrating them on situ or across different ecosystems using half-hour EC flux data in previous studies. Recent studies have suggested that the establishment of parameter robustness in parameterization is very important to determine whether there is a degree of \"universality\" in the parameter values of models and in which circumstances they can be used to predict GPP [10; 41]. Particularly, along with the breakthroughs of global retrievals of SIF from space-borne sensors, an exploration of how SIF could improve the characterization of photosynthesis, whether the GPP-SIF relationship is convergent across spatial and temporal, and what role it plays in GPP models becomes a very important and active area. However, less attention has been given to whether the calibrated parameters are robust under different situations, such as across different timescales (e.g., mid-day, daily, and monthly) or under varying environment stresses (e.g., soil water content, photosynthetically active radiation (PAR), and air temperature) [37; 42; 43]. Comparison between the SIF model and other GPP models has also rarely been explored currently, let alone an examination of the degree of difference between their parameter stabilities. [PERSON] et al. [3] examined the effect of meteorological conditions on the slope of OCO-2 SIF versus tower GPP at a grassland site. Their results suggest that under different meteorological conditions, the GPP-SIF linear relationship is more robust at the canopy scale than the theory based on leaf-level processes. [PERSON] et al. [37] found that linear GPP-SIF scaling is insensitive to the spatial or temporal scale for corn and a heterogeneous landscape. Nonetheless, the former only focused on a mid-day timescale and the latter mainly evaluated the consistency of the linear relationship between GPP and SIF across instantaneous to monthly time scales. They both only used the SIF-based model without comparison with other GPP models.