## 3 Results  
A total of seven pairs of Landsat and GOCI remote sensing images were selected in this paper. To make the data interval as small as possible, the intermediate date, 22 September, was selected as the target date. Since the ESTARFM requires at least 2 pairs of basic image inputs, the remaining 6 pairs of images were combined to obtain 15 groups of basic input data. The combination is shown in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline
**Date** & **25 Jan** & **14 Mar** & **15 Apr** & **8 Oct** & **25 Nov** \\ \hline
14 March & 14 March–25 January & & & & \\
15 April & 15 April–25 January & 15 April–14 March & & & \\
8 October & 8 October–25 January & 8 October–14 March & 8 October–15 April & & \\
25 November & 25 November–25 & 25 November–14 & 25 November–15 & 25 November–8 & \\  & January & March & April & October & \\
11 December & 11 December–25 & 11 December–14 & 11 December–15 & 11 December–8 & 11 December–25 \\  & January & March & April & October & November \\ \hline \end{tabular}
\end{table} 
Table 3: The combination of input data. Among all these combinations, the combination of 8 October and 11 December was selected to evaluate the effect of the two models on coastal Chl-a. Both dates were the nearest to the target date and most of the images had less cloud cover or failed pixels. The results of the two models were derived from the GOCI image of the target date. So, the Landsat Chl-a of the target date was used to evaluate the model performance. Figure 4 shows a scatter plot comparing the Landsat data of the target date with the results of the two models. GOCI and Landsat data were strongly correlated, with an \(R^{2}\) of 0.848. The systematic difference between them could be identified partially with an \(RMSE\) of 0.466 and a bias of \(-\)0.073. The performances of the two models were similar. There were significant linear correlations between remote sensing Chl-a and both modeled ones, with an \(R^{2}\) of 0.887. The distributions of the scatters of the two models were generally consistent with that of the GOCI, which indicates that the GOCI images have a higher numerical contribution and the systematic difference between the Landsat and GOCI Chl-a data still exists in the model results. However, the \(RMSE\) values of the model results were lower than that of the GOCI one, so the input Landsat information managed to help predict Chl-a values close to the target. The resulting images are shown in Figure 5. The upper two pictures are the Chl-a data retrieved from GOCI and Landsat images taken on 22 September 2019. The bottom two pictures are the results of the ESTARFM and ESTARFM_p. Compared with the GOCI data, the results of the two fusion models show refined textures, especially in nearshore and areas where the Chl-a concentration changes rapidly. Compared with the Landsat image, the results of the two models show similar spatial distribution, which indicates the information in fine-resolution images is modeled successfully. Both models successfully predicted the Chl-a in the target date. Compared with the results of the new model, the ESTARFM image showed abrupt patterns in some areas, while the new model showed better spatial consistency. Although the general trends of the results are consistent with the Landsat Chl-a images, the detailed spatial textures are quite different, as shown in Figure 6. The detailed features in Landsat images were caused by the phytoplankton and hydrodynamic conditions on the target date, which are difficult to calculate using the model. The input for the model was only low-resolution data of the target date, and the model cannot directly create details from nothing. The texture characteristics expressed in the results were derived from other high-resolution basic data. For the model, the process can be considered as combining several images to integrate their advantages through a suitable weighting mode. So, it is hard for the models to obtain images as smoothly as the Landsat does.