## Materials and Methods
### GPP from SCOPE Model Simulations  
Considering the absence of GPP data at XTS in 2017 and 2018 (the EC system was not operating properly, and the flux data were incorrect), we used the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) model v1.61 [49] to simulate GPP at 30 min intervals to relate the measured SIF data. According to the global sensitivity analysis (GSA) of the SCOPE model in Reference [50], apart from the meteorological inputs, the chlorophyll content density (Cab), LAI, and maximum carboxylation rate (V\({}_{\text{cmax}}\)) were the main driving variables that governed the simulated SIF and likely the GPP. For GPP simulation at XTS in 2018, the input vegetation parameters, including Cab and LAI, were measured from field experiments at 6-10 days intervals, as listed in Table 2. Cab was determined using measurements from a SPAD-502 (Soil and Plant Analyzer Development) chlorophyll meter [51], and the LAI was measured by the scanning method. For simulation at XTS in 2017, daily LAI was estimated with the SAVI-LAI linear model that derived from the measurements in 2018; then, Cab was retrieved with the model inverting method similar to Reference [52]. More specifically, daily Cab was estimated by minimizing the difference between the measured and SCOPE-simulated reflectance spectra in the region at 650-750 nm, with a lookup table (LUT) of input Cab values from 20 to 70 \(\upmu\)g/cm\({}^{2}\) and a step of 5 \(\upmu\)g/cm\({}^{2}\). V\({}_{\text{cmax}}\) can be estimated from net CO\({}_{2}\) flux measurements by inverting the commonly used modeling paradigm of canopy CO\({}_{2}\) and energy fluxes [53]. Based on this method and using the SCOPE model, our previous study [54] successfully estimated the V\({}_{\text{cmax}}\) values throughout the growing season of wheat at XTS in 2015 and 2016. The estimated V\({}_{\text{cmax}}\) values between the two years had consistent seasonal values, with a range of 30-110 \(\upmu\)mol/m\({}^{2}\)/s. According to a previous study [55], during the crop's growing season, V\({}_{\text{cmax}}\) increased rapidly with time until it reached its maximum during the mid-growing season and then declined toward the end of the growing season. Based on this prior knowledge and considering that wheat has a similar growth status between adjacent years under conventional fertilizer and irrigation management, the V\({}_{\text{cmax}}\) values on different days of the year in 2017 and 2018 were set as their values on corresponding days of the year in 2015 and 2016. Table 2 lists the retrieved or measured Cab, LAI, and V\({}_{\text{cmax}}\) values for the model inputs during the time window of the SIF measurements at 4-10 days intervals. Meteorological variables that control the variations in GPP simulations (i.e., R\({}_{\text{in}}\), Ta, p, u, and VPD) were derived from the AWS observations. All half-hour meteorological variables and seasonally changed inputs (including Cab, LAI, and V\({}_{\text{cmax}}\), as shown in Table 2) were imported into the model input files and loaded for the time series simulations of GPP with SCOPE. Meanwhile, the diurnal SZAs were automatically calculated during the simulation using the inputs Julian day, time, and field site longitude and latitude. Other parameters required by the SCOPE model were set to their default values for each plant functional type (for details, see Reference [50]). With all of these directly measured or indirectly retrieved inputs, the SCOPE model accurately interpreted the diurnal and seasonally changed values of the GPP.