## 3 Results
### Comparison of Downscaling Model Accuracy  
The models selected for this study include CNN, Stacking integrated learning model, RF, ET, ADA, and GBDT. These models were optimized, and the parameters were adjusted to the appropriate values for training. The dataset 1 was input into each of the six models for training, and the experiment extracted a total of 138,845 pieces of sample data, of which 70% was used as the training set and 30% as the test set. Figure 4 displays the density plots of the training true values and the predicted eSIF for each model. The plots show that, despite using the same downscaled sample dataset, the models exhibit good precision, indicating high accuracy. Although the accuracy metrics are satisfactory, the fitted lines in the plots are somewhat dispersed from the ideal 1:1 line. Table 1 presents the accuracy evaluation metrics for the training results from each model using dataset 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & \(\mathbf{R^{2}}\) & **RMSE** & **MAE** \\ \hline CNN & 0.814 & 0.091 & 0.057 \\ Stacking & 0.886 & 0.061 & 0.0372 \\ RF & 0.896 & 0.060 & 0.0352 \\ GBDT & 0.883 & 0.065 & 0.042 \\ ET & 0.841 & 0.075 & 0.0472 \\ ADA & 0.76 & 0.083 & 0.059 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the accuracy evaluation metrics computed in each model for dataset 1. Figure 3: Correlation analysis heatmap. The experiment inputs dataset 2 (from October 2018 to May 2019) into six models for training, respectively, and it is found that, after adding the driving variables, each model not only improves the prediction accuracy of the model at a higher level, but according to Figure 5, it can also be seen that, in the density plot of each model, the 1:1 line is more closely fit to the fitted line, and the density of the dots is more centralized. Comparing Figures 4 and 5, the scatter density of RF, Stacking, and GBDT is more centralized, while CNN, ET, and ADA exhibit more dispersed scatter points. Based on these observations, the RF model, which demonstrated the highest accuracy and the best prediction performance, was chosen to construct the downscaling model and invert the 1 km SIF. Table 2 shows the accuracy evaluation of the training results of dataset 2 in each model indicator. Figure 4: Density fit plots for model projections from October 2018 to May 2019 (seven variables). Figure 5: Density fit plots for model projections from October 2018 to May 2019(welve variables). For the dataset from October 2019 to May 2020, a downscaled model based on the RF algorithm was trained using the same driving variables. Figure 6 demonstrates the fitted density plot of the real value and the predicted eSIF based on the RF algorithm, and the results show that the model has a great fitting effect. The specific accuracy metrics are R\({}^{2}\) = 0.926, RMSE = 0.058, and MAE = 0.034. The fitted line is very close to the 1:1 line, which shows a high degree of consistency between the model predictions and the actual observations. The driving variables with a 1 km resolution were input into the downscaling model to generate the 1 km resolution SIF product (denoted as SIF 1 km) for Shandong Province from October 2018 to May 2019. Figure 7 demonstrates the eSIF maps synthesized for each month from October 2018 to May 2019, while Figure 8 demonstrates the downscaled SIF for the same period, with a resolution of 1 km. The comparison reveals that the downscaled SIF has a finer spatial resolution. From October 2018 to November 2018, wheat was mainly planted in the northwest and southwest of Shandong Province. During this period, as the temperatures gradually declined and the vegetation cover decreased, the SIF values showed a decreasing trend. As Shandong Province is located in Northern China, the temperature is low in winter, and the winter wheat after sowing in the fall is affected by the temperature and grows slowly. With temperatures increased, the winter wheat entered the greening period, and the SIF values rose significantly.