## 3 Experimental Results
### Experimental Results for LGC  
For the LGC dataset, the input data includes MODIS-Landsat data pairs from 26 November 2004 and MODIS data from 12 December 2004. Significant differences exist between the two temporal scenes due to the impact of flooding. Compared to the prediction of phenological changes, predicting sudden land cover changes is more challenging. Figure 7 shows the predicted results for the LGC data on 12 December 2004 using different methods. Overall, except for CTSTFM, all other methods made relatively accurate predictions of the flood-affected areas. However, due to the significant spatial resolution difference between MODIS and Landsat, and the reliance on MODIS imagery at the prediction time for spatial information on the changes, the details of the flood-affected areas could not \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **Band** & **STARFM** & **FSDAF** & **Fit-FC** & **EDCSTFN** & **GAN-STFM** & **MLFF-GAN** & **STF-Trans** & **CTSTFM** & **Proposed** \\ \hline \multirow{6}{*}{UUQI} & 1 & 0.8140 & 0.8293 & 0.8190 & 0.8152 & 0.8979 & 0.9113 & 0.9201 & 0.9107 & **0.9245** \\  & 2 & 0.8152 & 0.8387 & 0.8275 & 0.8023 & 0.9107 & 0.9145 & 0.9279 & 0.9225 & **0.9312** \\  & 3 & 0.8165 & 0.8498 & 0.8400 & 0.7815 & 0.9132 & 0.9178 & 0.9294 & 0.9264 & **0.9336** \\  & 4 & 0.8275 & 0.8262 & 0.8370 & 0.8806 & 0.8976 & 0.9043 & 0.9128 & 0.9178 & **0.9184** \\  & 5 & 0.9222 & 0.9247 & 0.9224 & 0.9174 & 0.9444 & 0.9491 & 0.9551 & 0.9528 & **0.9576** \\  & 6 & 0.9206 & 0.9215 & 0.9193 & 0.9174 & 0.9368 & 0.9408 & 0.9477 & 0.9475 & **0.9521** \\  & Avg & 0.8527 & 0.8650 & 0.8609 & 0.8524 & 0.9168 & 0.9230 & 0.9322 & 0.9296 & **0.9363** \\ \hline \multirow{6}{*}{CC} & 1 & 0.8320 & 0.8370 & 0.8401 & 0.8331 & 0.9022 & 0.9116 & 0.9230 & 0.9148 & **0.9265** \\  & 2 & 0.8369 & 0.8501 & 0.8473 & 0.8234 & 0.9153 & 0.9160 & 0.9292 & 0.9247 & **0.9320** \\  & 3 & 0.8454 & 0.8654 & 0.8551 & 0.8013 & 0.9165 & 0.9198 & 0.9306 & 0.9275 & **0.9345** \\  & 4 & 0.8344 & 0.8281 & 0.8459 & 0.8813 & 0.8994 & 0.9049 & 0.9141 & 0.9187 & **0.9186** \\  & 5 & 0.9222 & 0.9249 & 0.9238 & 0.9180 & 0.9451 & 0.9497 & 0.9554 & 0.9532 & **0.9577** \\  & 6 & 0.9210 & 0.9217 & 0.9198 & 0.9177 & 0.9376 & 0.9412 & 0.9484 & 0.9478 & **0.9523** \\  & Avg & 0.8653 & 0.8712 & 0.8720 & 0.8625 & 0.9194 & 0.9239 & 0.9334 & 0.9311 & **0.9369** \\ \hline ERGAS & ALL & 1.3146 & 1.2666 & 1.2612 & 1.3488 & 1.0298 & 1.0047 & 0.9640 & 0.9931 & **0.9319** \\ \hline SAM & ALL & 11.1256 & 10.9104 & 10.8326 & 11.4756 & 8.8898 & 8.6679 & 8.0970 & 8.2494 & **7.8974** \\ \hline \end{tabular} The best values of the index are marked in bold.  
\end{table} 
Table 1: _Cont._  
Figure 6: Fusion and AAD visualizations for the subregions on 12 January 2001, generated by different models on the CIA dataset. (**a**) Ground truth. (**b**) STARFM. (**c**) FSDAF. (**d**) Fit-FC. (**e**) EDCSTFN. (**f**) GAN-STFM. (**g**) MLFF-GAN. (**h**) STF-Trans. (**i**) CTSTFM. (**j**) Proposed. be perfectly restored. A zoomed-in view of the black-boxed region from the true Landsat image in Figure 7a is taken, and the AAD map for that region is calculated, as shown in Figure 8. It can be observed that the advantages of DL methods on the LGC dataset are not as pronounced as those on the CIA dataset. From the AAD results, FSDAF, despite being a traditional method based on simple theories, demonstrates relatively good fusion performance due to its consideration of both class changes and spatial reconstruction. In contrast, CTSTFM, which emphasizes spatial detail enhancement, fails to capture temporal changes, resulting in significant errors in the flood-affected areas. The proposed method continues to demonstrate the smallest error values. Figure 8: Fusion and AAD visualizations for subregions on 12 December 2004. for the subregions on 12 December 2004, generated by different models on the LGC dataset. (**a**) Ground truth. (**b**) STARFM. (**c**) FSDAF. (**d**) Fit-FC. (**e**) EDCSTFN. (**f**) GAN-STFM. (**g**) MLFF-GAN. (**h**) STF-Trans. (**i**) CTSTFM. (**j**) Proposed.