## 4 Discussion
### The Different SIF-GPP Results between Our Study and Previous Research  
Many researchers argue that SIF is sensitive to photosynthesis and closely related to GPP at canopy and ecosystem scales because both SIF and GPP depend on absorbed radiation [21; 32]. However, the relationships are affected by many factors such as absorbed photosynthetically active radiation (APAR) [50], vegetation types [51] or canopy complexity [7]. SIF provides insight into GPP which helps us gain a more mechanistic understanding of the ecosystem carbon cycle [12; 52]; however, the correlations are different under various conditions. Contrary to previous studies, we did not find any significant relationship between SIF and GPP due to spatial resolution, vegetation type, and GPP accuracy.  
[PERSON], et al. [32] showed a significant relationship between GOSAT SIF and MODIS GPP (R\({}^{2}\) = 0.74) at 4\({}^{\circ}\times\) 4\({}^{\circ}\) spatial resolution. [PERSON], et al. [21] also a found similar conclusion (r\({}^{2}\) = 0.82) at 2\({}^{\circ}\times\) 2\({}^{\circ}\) spatial resolution. By employing GOSAT SIF, [PERSON], et al. [53] argued the R\({}^{2}\) values were 0.63-0.83 for different GPP datasets at 2.5\({}^{\circ}\times\) 2\({}^{\circ}\). [PERSON], et al. [54] calculated the relationship between SIF and MODIS GPP and obtained R\({}^{2}\) = 0.9 for a forest ecosystem from 15 training samples.  
However, previous studies used coarse resolution or limited sampling points. When decreasing the spatial resolution of remote sensing data, vegetation indices only reflect the mean value of a region because of mixed pixels. Due to mixed pixels at coarse spatial resolutions, the true SIF values of each vegetation type cannot be characterized. Thus, the objective relationships between vegetation types and SIF cannot be obtained. To eliminate the effect of mixed pixels, we need to understand the correlations at fine spatial resolutions. As mentioned above, SIF reflects vegetation photosynthesis and differences in vegetation types. In our study, no significant correlations were found between SIF and MODIS GPP at a 1 km spatial resolution in each 16-day interval.  
SIF and GPP are closely (indirectly) related through APAR which is affected by canopy structure, leaf biochemical parameters, sampling time, etc. Thus, in croplands, grasslands, and rice paddy fields, ecosystems mainly determined by the chlorophyll content, SIF and GPP were found to be closely related [45; 51]. In a word, different vegetation types in previous studies is another reason for no significant SIF-GPP relationship.  
The semiempirical MODIS GPP model (MOD17) relies on the combination of satellite-based estimates of the FAPAR and tabulated light use efficiency [21]. Due to the algorithm of MODIS GPP products, the accuracy was affected by the uncertainties from various upstream inputs, and the products have always been questionable [5; 55]. GPP obtained from eddy covariance (EC, GPP flux tower) is considered the most accurate GPP data. However, a flux tower has a small sampling area (<1 km\({}^{2}\)) and is unevenly distributed [51]. [PERSON], et al. [54] demonstrated that SIF-GPP\({}_{\text{flux tower}}\) (R\({}^{2}\) = 0.99) was higher than SIF-GPP\({}_{\text{MODIS}}\) (R\({}^{2}\) = 0.90) because of a higher flux tower GPP accuracy  
Figure 11: Highest (left) and lowest (right) SIF values; the south of Yilehuli Mountain is Lingnan, and the north of Yilehuli Mountain is Lingbei.  
in a forest based on a few samples. MODIS GPP accuracy was also the reason that the correlation of SIF-GPP was affected.  
GPP can be simulated using SIF at both regional and point scales because of the close relationships between them, but SIF cannot be simulated by MODIS GPP data because of the uncertainty of MODIS GPP data.