## Materials and Methods
### Simulating SIF and APAR at Different Latitudes  
Remote sensing often observes ground features across different latitudes that receive changed levels of solar radiation. The latitudinal pattern of incoming solar radiation causes large uncertainties in the application of remote sensing at the global scale. For the \(\mathrm{SIF_{inst}}\) observation, latitude is a large factor that affects the relationships between \(\mathrm{SIF_{inst}}\) and \(\mathrm{SIF_{daily}}\) (see Figure 1a) and, thus, may affect the performance of SIF for the APAR or GPP estimation. However, this study only obtained data from two sites distributed at similar latitudes, which was insufficient for the investigation of SIF applications at different latitudes. To better understand the latitudinal effects on the SIF-APAR relationship, we used the SIF, PAR, and FPAR measurements at limited sites with a latitude of \(\sim\)40\({}^{\circ}\) to simulate their corresponding values at other latitudes. As the latitudinal patterns of incoming solar radiation were only attributed to the latitudinal variations in SZA, it was reasonable to neglect the weather changes among different latitudes and, thus, to simulate PAR values at different latitudes based on the latitudinal SZA changes. Similarly, based on the assumptions that the crop growth status and plant physiological response to illumination are the same across latitudes, the SIF, FPAR, and thus APAR values at these latitudes can be simulated. The spatial heterogeneities of environmental conditions (e.g., weather, temperature, and precipitation), soil types, plant varieties, and vegetation growth status were not taken into consideration because they were irrelevant to the analysis of latitudinal variations in SIF-APAR relationships. In this study, we picked three different latitudes, including \(-20^{\circ}\), \(-40^{\circ}\), and \(60^{\circ}\), to perform simulations based on the measurements at each of the two sites. These latitudes were selected because they are spatially representative and the minimum SZA values at these latitudes were lower than the minimum SZA values at a latitude of 40\({}^{\circ}\) during our measuring period. Figure 3 displays an example of the simulation of SIF at different latitudes. Using the diurnal SIF measurements at a latitude of 40\({}^{\circ}\) (gray line in Figure 3), the diurnal SIF values on the same day at other latitudes (green and red lines in Figure 3) were calculated based on the latitude-dependent SZA values. Specifically, the SIF value of time t1 at a latitude of 60\({}^{\circ}\) or \(-40^{\circ}\) equaled the SIF value of time t2 at a latitude of 40\({}^{\circ}\), where t1 and t2 correspond to the same SZA and both occur in the morning or afternoon. For instance, the \(\mathrm{SIF_{T30}}\) value at 10:00 at a latitude of 60\({}^{\circ}\) (see the green arrow in Figure 3) can be simulated as follows: (1) calculate the range of SZA (\(\Delta\)SZA) corresponding to the time range [9:45 10:15] at a latitude of 60\({}^{\circ}\), (2) search for the set of \(\mathrm{SIF_{0}}\) measurements at a latitude of 40\({}^{\circ}\) in the morning that satisfy the condition where their corresponding SZA values are within \(\Delta\)SZA (the green frame in Figure 3), and (3) average the searched SIF set in the previous step as the SIF value at 10:00 at a latitude of 60\({}^{\circ}\). The simulation of the \(\mathrm{SIF_{T30}}\) value at 15:00 at a latitude of \(-40^{\circ}\) (see the red arrow in Figure 3) was similar but the searched time window was in the afternoon. By repeating these steps for each half-hour time for all days and other latitudes, 12 time series of continuous SIF, PAR, and APAR datasets at 30 min intervals were generated at four different latitudes at two sites. Figure 3: The simulation of diurnal SIF values at the latitudes of 60\({}^{\circ}\) and \(-40^{\circ}\) with continuous SIF measurements at a latitude of 40\({}^{\circ}\) at the XTS site on 27 April 2018.