## 4 Discussions  
This study introduces an updated version of the image fusion model (mESTARFM) that can blend the multi-source remotely sensed data at fine spatial resolution. The simulation capacity in the three study areas with different time intervals has been tested using the ESTARFM and mESTARFM model. Compared with the original Landsat images, the synthetic Landsat-like images produced using the updated model have higher accuracy than ESTARFM.  
Specifically, mESTARFM obtains improved synthetic images, because it uses more information around the central pixel and the additional ancillary data, i.e. land cover data. As shown in the lower row of Figure 3b, these two versions of the models have the capacity of mainly capturing the annual  
Figure 6: Scatter plot of the observed and simulated reflectance by mESTARFM and ESTARFM for the NIR, red and green band (**a–f**, changes over several years around the EOBS flux site).  
changes, while the updated version (mESTARFM) preserves more spatial details compared to ESTARFM. As shown in Figures 3c and 6, for green and red bands, the performance of simulated Landsat-like images is not good. This may be caused by long time intervals and no suitable land cover data around the simulated date. This would lead to lower accuracy if any major land disturbances happened. The simulated reflectance of green and red bands contains a number of pixels with zero values, while the corresponding observed reflectance values are not, which may be due to the fact that ice only exists in the simulated date. Taking the scatter plot at three study areas into account (Figures 4-6), the scatters of mESTARFM are closer to the 1:1 line. This indicates that the simulated Landsat-like reflectance results are more similar to the observed Landsat reflectance compared with ESTARFM.  
The most important improvement of mESTARFM is the selection of spectrally similar neighbor pixels within a moving window with optimal size. The ESTARFM uses the information of the entire image to select the similar pixels within the local moving window; however, the information of the image is not necessarily relevant locally. According to [PERSON]'s First Law of Geography, the pixels that are nearer are more related to the center pixel. For the pixels outside the local moving window, the effect on the central pixel can be considered negligible. The size of the local moving window would increase until the most similar simulated fine-resolution image is obtained compared to the observed one. Besides this, the land cover data were used as auxiliary information for the selection of spectrally similar neighbor pixels. The criteria for determining spectrally similar neighbor pixels includes the threshold of standard deviation, the number of land cover types and the difference between the central pixel and neighbor pixels within the local moving window.  
Although improvements of mESTARFM are made, there are still limitations, including: (i) the available land cover data for a certain date may not be sufficient for the spectrally similar neighbor pixels, since that land cover type may change from date 1 to date 2; (ii) the land cover data may not take much effect if the date suitable for the land cover data is far away from the simulated time. Therefore, further research may need to focus on the following issues: (i) The combination of the land cover classification data on date 1 (beginning) and date 2 (ending); the pixels may obtain whether land cover type changes between these two dates [20]. More accurate remotely-sensed data can be simulated if more accurate similar neighbor pixels are selected according to the better classification algorithm. (ii) The establishment of the typical vegetation's annual variation of spectral reflectance curve may be useful to get more accurate conversion coefficients. (iii) A combined Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH) [15]/mESTARFM approach could better indicate the disturbance or changes in spectral reflectance, and that would improve the predictions.