## 4 Discussion
### Limitations of the Current Study  
The results of this study reveal that the GOSIF dataset has great potential for estimating GPP for different vegetation coverage types. Our method requires fewer input variables and has higher computational efficiency than other satellite GPP estimation methods. Satellite-based SIF data provide a unique opportunity for more accurate, near real-time GPP mapping in the future. However, its suitability varies somewhat. One of the main reasons for this is that significant amounts of flux tower data are not available to the public, and the available data are not evenly distributed across the globe. Consequently, there are huge challenges for using GPP\({}_{\text{EC}}\) to validate SIF-estimated GPP or other GPP products on a global scale. In addition, inaccurate GPP\({}_{\text{EC}}\) observations can also cause errors. To some extent, GPP\({}_{\text{EC}}\) observations obtained by flux towers can simply be considered as \"reference values\". However, different flux towers have different qualities [85]. Moreover, in this study, we assumed that the vegetation coverage types around the flux towers were homogeneous and uneven. This assumption does not hold consistently. In general, we believe that the smaller the pixel corresponding to the product at the location of the flux tower, the more representative the reference value of the pixel likely is. Here, we used site data to calibrate the GPP products with a spatial resolution of 500 m, and then resampled to 0.05\({}^{\circ}\); this will affect the accuracy of the results. Above all, the input variables of the model training set used in this study also introduced certain uncertainties. The SIF signal exhibited a strong relationship with GPP, which can be explained mechanistically as follows. Based on the LUE model, GPP can be expressed as Equation (1). SIF can be similarly conceptualized as a byproduct of photosynthesis, which can be expressed as [34]:  
\[\text{SIF}(\lambda)=PAR\times fPAR\times\epsilon_{F}(\lambda)\times f_{\text{ esc}}(\lambda) \tag{3}\]  
where \(\lambda\) is the spectral wavelength, \(\epsilon_{F}\) is the fluorescence quantum yield (i.e., the rate at which a plant absorbs photosynthetically effective radiation and re-emits fluorescence at the wavelength \(\lambda\)), and \(f_{\text{esc}}(\lambda)\) is the ratio at which fluorescence emitted by chloroplasts can escape out of the canopy. Equations (1) and (3) can be combined to describe the theoretical formulation of the GPP-SIF relationship as follows:  
\[\text{GPP}=\text{SIF}(\lambda)\times\frac{\epsilon_{p}}{\epsilon_{F}(\lambda) }\times\frac{1}{f_{\text{esc}}(\lambda)} \tag{4}\]  
For the same vegetation type, \(f_{\text{esc}}(\lambda)\) remains relatively constant, and if the ratio \(\frac{\epsilon_{p}}{\epsilon_{F}(\lambda)}\) is constant then \(\text{SIF}(\lambda)\) should be a good constraint on the photosynthetic rate, regardless of changes in FPAR, PAR, or environmental stresses. Thus, for this study, the land cover type determined \(f_{\text{esc}}(\lambda)\) to some extent, whereas NIRv was used as an approximation of the canopy-escaping probability of emitted SIF. Figure 7: The relative importance of variables to the GPP estimation GBRT model built from the training dataset. Over the years, many researches have demonstrated that SIF and GPP have a strong linear relationship compared with the traditional vegetation index [9; 45; 48]. However, GPP estimates by SIF have been limited by the spatial resolution of the satellite spatial resolution. GOSIF(0.05\({}^{\circ}\)) offers a finer spatial resolution than other SIF products (e.g., 0.5\({}^{\circ}\)), providing an unprecedented opportunity to explore the estimation of GPP on an ecosystem or global scale. However, the spatial resolution of GOSIF is currently far from sufficient. To date, there is no global high-precision SIF remote sensing product for estimating GPP on a large regional scale.