## 3 Results and Validation
### Temporal Perspectives  
Currently, there is no annual land cover sample dataset available for the years 1980-1990 in the study area; only samples from 1986 can be collected. Therefore, it is not possible to evaluate the mapping performance of updated land cover datasets for each year. By randomly generating 1000 spatially uniform sample points within the study area (Figure 17) and comparing the consistency between the dataset produced in this study and CLUD-A dataset, an annual consistency table is presented in Table 5. These randomly generated sample points are objective, allowing for an unbiased comparison of temporal consistency between the datasets. Table 5 indicates that the 1000 sample points exhibit high temporal consistency in land cover types, with consistency percentages exceeding 95% for all years except 1980 and 1990, which both utilized CNLUCC products. The high temporal consistency between consecutive years indicates that the two datasets exhibit significant similarity and continuity in annual changes and trends in land cover types from 1980 to 1990. For instance, both datasets show a substantial increase in built-up areas and a decrease in unused land and forested areas. Conversely, the error rates between consecutive years reveal that there is approximately a 2% discrepancy in land cover types or change trends between the two datasets. To evaluate the performance and stability of the proposed method over an extended time span, we reconstructed annual land cover data from 1995 to 2005 and compared the trends in land cover type changes with the existing CNLUCC product for that period. Figure 18 illustrates the area change for each land cover type in the study area by year from 1995 to 2005. The triangles on the vertical dashed line refer to the year with CNLUCC while the rest circles represent interpreted annual land cover datasets. In 2000, the average area error for all land cover types across the two datasets was 0.0501 million ha. The largest error was for unused land at 0.0930 million ha, and the smallest was for built-up at 0.0061 million ha. The yearly area changes and trends of each land cover type from 1995 to 2005 were also successive and similar to the original CNLUCC. These changes demonstrate the value of using existing CNLUCC and coarse time series datasets to derive long-term annual land cover maps in regions without frequent satellite observations and the stability and scalability of the SRGAN-BFAST method over extended time periods. Change Perspectives  
Before conducting change detection, we utilized the CNLUCC product to obtain the land cover change map from 1980 to 1990. Changes primarily occurred in urban expansion and increase in cropland, which aligned closely with the quantitative statistical trends in area change, thus reinforcing the mapping performance from change perspectives. To assess the accuracy of the change detection analysis in determining the change years, this study selected 54 samples from the changed pixels where there was a notable shift in land cover types over five years. Subsequently, using HR images from Google Earth and Landsat time series, the precise change years were visually interpreted (see Figure 19). The  
Figure 18: Area change in different land cover classes from 1995 to 2005. The fluctuations for each land cover type are enlarged using the different ranges of y-axis. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline Year & 1980 & 1981 & 1982 & 1983 & 1984 & 1985 & 1986 & 1987 & 1988 & 1989 & 1990 \\ \hline
**Consistency (\%)** & 1 & 98.39 & 98.45 & 98.36 & 98.36 & 97.83 & 97.90 & 97.95 & 98.02 & 98.20 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Our land cover dataset and CLUD-A dataset consistency. temporal and spatial distribution of pixel changes in the study area is also illustrated in Figure 19. The identified change years from the change detection algorithm were compared with the actual change years of these test samples to evaluate the accuracy of the detection results.