## 1 Introduction  
Solar-induced chlorophyll fluorescence (SIF), emitted by vegetation in the pigment beds of photosystems, is an indicator of the efficiency by which photons are transmitted into photochemical reaction centers [1]. SIF has recently emerged as a novel tool for the estimation of terrestrial gross primary production (GPP), the amount of carbon fixed by terrestrial vegetation via photosynthesis [2, 3]. SIF is seen as a more promising proxy for GPP than the traditional vegetation indices that are derived from reflectance data [4, 5, 6]. GPP is a key component of the global carbon cycle, and its accurate estimation at region and global scales is vital for understanding the interactions between the terrestrial carbon cycle and climate change [7, 8]. Previous studies have shown strong relationships between ecosystem SIF and GPP using SIF data from ground measurements (e.g., [9, 10]), airborne imaging spectrometers (e.g., [4, 11]), and satellite sensors such as the Greenhouse GasesObserving Satellite (GOSAT) [12], Global Ozone Monitoring Mission Experiment-2 (GOME-2) [13], and Orbiting Carbon Observatory-2 (OCO-2) [2,14]. These studies showed the great potential of SIF in estimating GPP at ecosystem to global scales. However, ground observations and airborne SIF measurements (e.g., [4,9,11]) often have limitations in spatio-temporal resolution and continuous monitoring of vegetation photosynthesis over large regions. Finer-resolution satellite SIF has limited swath width and does not provide globally continuous coverage during each repeat cycle. For example, the swath width of OCO-2 is only 10.3 km, leading to sparse SIF observations globally. Process-based models have been used to explicitly simulate SIF. SIF has been incorporated into ecological models such as the 1-D (dimensional) SCOPE (Soil Canopy Observation, Photochemistry and Energy Fluxes) model [15,16] and 3-D models (e.g., DART [17], and FluorFLIGHT [18]) for more complex canopies. The SCOPE model is a state-of-the-art model integrating radiative transfer, photosynthesis, energy balance, and fluorescence [15] and has been widely used in SIF and photosynthesis studies (e.g., [19, 20, 21, 22]). The model was partly developed for the upcoming Fluorescence Explorer (FLEX) mission that will be able to capture SIF for the full spectral range from 500 to 780 nm with 300-m spatial resolution and bi-weekly global coverage [23]. Currently, the SCOPE model has the capability of simulating fluorescence, spectral reflectance, and photosynthesis of vertically heterogeneous, multi-layer vegetation canopies [24]. The model has been incorporated in several terrestrial biosphere/land surface models including the Community Land Model (e.g., [25]). Coupling the SIF and photosynthesis processes together have extended the functions of these ecosystem models, and improves their capability in simulating GPP and thus the global carbon cycle [25]. Accurately calibrating key parameters related to ecosystem processes remains an important procedure in carbon cycle modeling [26,27]. Parameter uncertainty is one of the most important sources of uncertainty in model simulations [27, 28, 29]. The complexity of parameter calibration is in proportion to the complexity of the model and the number of parameters [30]. The SCOPE model integrates various sub-models and thereby contains a large number of parameters [15]. There is significant uncertainty associated with these parameters, which can in turn induce large uncertainty to simulated SIF and GPP. Therefore, it is increasingly important to identify strategies that are able to make full use of existing information from various data sources to improve ecosystem models. As an integrated model incorporating routines of momentum, carbon, and water fluxes as well as radiation transfer, transmission, and chlorophyll fluorescence, SCOPE contains over twenty biochemical and physiological parameters [15].