## 3 Results
### SIF Data Accuracy Improvement Analysis
#### 3.3.3 Comparative Analysis of Accuracy improvement Based on Canopy and Linear Methods  
From Figure 9, it can be seen that the linear accuracy improvement method can enhance the accuracy of RTSIF data across different ecosystems in arid regions, with overall R\({}^{2}\) values exceeding 0.75 after improvement. However, the canopy accuracy improvement method is only applicable to areas with a higher leaf area index in arid regions, and it cannot  
Figure 8: Linear fitting diagram between the 2020 GPP data of each station and the corresponding RTSIF station data after improving based on the linear method. (**a**) Kelameili Station, desert vegetation area. (**b**) Ulastai Station, pasture and grassland area. (**c**) Ulan Usu Staion, cultivate land and farmland area.  
achieve improvement in regions with low vegetation cover. Interestingly, the R\({}^{2}\) fitting values decrease overall for the pasture grassland area and cultivated and farmland area after improvement using the linear method, with decreases of 0.03 and 0.05, respectively. This indicates that while the linear method is suitable for various ecosystems in arid regions, its effectiveness in areas with high vegetation cover is not as pronounced as that of the canopy method.  
The differences in the error fitting parameters after improvement based on both methods are listed in Table 5. Compared to the canopy method, for the pasture grassland area and the cultivated and farmland area using the linear improvement method results in increased MB, RMSE, and SD errors, with error increases ranging between 0-0.0021, 0.0009-0.0211, and 0.0065-0.0671, respectively. Conversely, for the desert vegetation area, using the linear improvement method results in relatively small MB, RMSE, and SD errors, with error values of 0.0002, 0.0029, and 0.0110, respectively, and the improvement effect is relatively significant. This further emphasizes that while the linear method has broader applicability than the canopy method in arid regions, its effectiveness is relatively weaker in areas with a higher leaf area index.  
In summary, the linear method can enhance the accuracy of SIF data across different ecosystems in arid regions and further reduce errors between parameters after improve  
Figure 9: The R\({}^{2}\) fitting values for various sites based on two accuracy improvement methods: canopy and linear.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station** & **Parameter** & **RMSE** & **1 MB** & **SD** \\ \hline \multirow{3}{*}{Ulan Usu Station} & Canopy improvement value & 0.0915 & 0.0002 & 0.3054 \\  & Linear improvement value & 0.1126 & 0.0021 & 0.3725 \\  & Difference & 0.0211 & 0.0019 & 0.0671 \\  & Canopy improvement value & 0.0058 & 0.0001 & 0.0242 \\ \multirow{3}{*}{Ulastai Station} & Linear improvement value & 0.0067 & 0.0001 & 0.0307 \\  & Difference & 0.0009 & 0 & 0.0065 \\ \multirow{3}{*}{Kelameili Station} & Canopy improvement value & - & - & - \\  & Linear improvement value & 0.0029 & 0.0002 & 0.0110 \\ \cline{1-1}  & Difference & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: The difference in error fitting parameters for various sites based on two improvement methods.  
ment. However, its effectiveness in areas with high vegetation cover is slightly weaker than that of the canopy method. Therefore, the canopy method can be utilized to improve the accuracy of SIF data in regions with high vegetation cover in arid regions, whereas the linear method can be used, as a supplement, to enhance the accuracy of SIF data for other types of underlying surfaces.After careful verification, we found that the title did not express it clearly, which was due to our negligence. We deeply apologize for this. We have rephrased the title, and the revised title is clear and reasonable. We have marked the modified content in red.