## 5 Conclusions  
Time-series for Sentinel-2A/2B (Sentinel-2) MSI images are valuable resources for environmental study and change monitoring. Although the revisiting capability of Sentinel-2 constellation has been greatly improved compared to other sensors such as Landsat, values of Sentinel-2 imagescannot be fully realized due to cloud coverage, especially in the cloudy regions. This study aimed to reconstruct cloud-free Sentinel-2 image time-series from a limited number of clear-sky Sentinel-2 observations and MODIS MCD43A4 images using an extended spatiotemporal image fusion approach. With this approach, the cloud-free pixels of a time-series of Sentinel-2 observations were kept while the cloudy and cloud shadow pixels were replaced by the pixels of their corresponding synthetic images. The proposed new algorithm of this study to identify cloudy, cloud shadow, and haze pixels was simple but effective. To take advantages of high quality synthetic Sentinel-2 images, the identification of cloudy, cloud shadow, and haze pixels was based on analysis of different cloud contaminated Sentinel-2 observations and synthetic image from clear-sky Sentinel-2 and MODIS MCD43A4 images. By replacing only cloud contaminated pixels while keeping all cloud-free pixels of observations, the method produced the highest quality of a reconstructed time-series image. Experiment with reconstruction of a cloud-free time-series of 67 Sentinel-2 images of the case study indicated that the proposed method worked well. Hence, our proposed approach could make Sentinel-2 images more usable and useful.  
This reconstruction method depends on the quality of synthetic images. The higher the quality of synthetic images, the higher quality of a reconstructed image time-series. In this regard, choice of a good image fusion model is necessary. In this study we used PSRFM for image fusion as previous studies indicated that it produced high quality of synthetic Sentinel-2 images at various landscape situations.  
While this study demonstrated the proposed method with Sentinel-2 images, this method can be also applied to reconstructing time series Landsat 8 imagery since the PSRFM model was successfully used to produce synthetic Landsat 8 image time-series in their earlier studies.  
One of the main limitations of this technique was that it generated only six bands of cloud-free time-series Sentinel-2 MSI images due to the constraint of spectral bandwidth scope. The image fusion methods, including PSRFM, were built on the similarity of reflectance bandwidths between fine (e.g., Sentinel-2) and coarse (e.g., MODIS) images. As the two sensors had only six similar bands, as shown in Table 1, only the reflectance of the six bands were blended and processed for time-series image reconstruction.  
Conceptualization, F.Z. and D.Z.; methodology, F.Z. and D.Z.; software, D.Z.; validation, F.Z.; formal analysis, F.Z. and D.Z.; investigation, F.Z. and D.Z.; resources, F.Z.; data curation, R.P.; writing-- original draft preparation, F.Z.; writing--review and editing, D.Z. and R.P.; visualization, F.Z. and D.Z.; supervision, F.Z.; project administration, F.Z.; funding acquisition, F.Z. All authors have read and agreed to the published version of the manuscript.  
This study is a part of the research and development project \"Big Data Analytics of Earth Observation Data in Support of Evidence-Based Decision Making for Climate Change\" funded by Natural Resources Canada and Canadian Space Agency's GRIP Program.  
Authors would like to thank [PERSON] and anonymous reviewers for their insightful review of and valuable comments on the manuscript, which helped to improve the quality of the paper.  
The authors declare no conflict of interest.