)1 Canada Center for Remote Sensing, Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 6 th floor, 560 Rochester Street, Ottawa, ON K1S 5K2, Canada; [PERSON]RESS] (F.Z. ); [EMAIL_ADDRESS] (R.P. )1 Canada Center for Remote Sensing, Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 6 th floor, 560 Rochester Street, Ottawa, ON K1S 5K2, Canada; [PERSON]RESS] (F.Z. ); [EMAIL_ADDRESS] (R.P. )1 Canada Center for Remote Sensing, Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 6 th floor, 560 Rochester Street, Ottawa, ON K1S 5K2, Canada; [PERSON]RESS] (F.Z. ); [EMAIL_ADDRESS] (R.P. )1 Canada Center for Remote Sensing, Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 6 th floor, 560 Rochester Street Street, Ottawa, ON K1S 5K2, Canada; [PERSON]RESS] (F.Z. ); [EMAIL_ADDRESS] (R.P. )1 Canada Center for Remote Sensing, Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 6 th floor, 560 Rochester Street, Ottawa, ON K1S 5K2, Canada; [PERSON]RESS] (F.Z. ); [EMAIL_ADDRESS] (R.P. )1 Canada Center for Remote Sensing, Canada Centre for Mapping and Earth Observation, Natural Resources Canada, 6 th floor, 560 Rochester Street, Ottawa, ON K1S 5K2, Canada; [PERSON]RESS] (F.Z. ); rihana. Clouds and cloud shadow (CCS) contamination on images is common to satellite optical remote sensing. To overcome the CCS problems, many researchers have developed two different methodologies for reconstruction of cloud-free image time series. The first is the image composite method that uses observed image time series to reconstruct cloud-free images. For example, several methods have been developed for reconstruction of cloud-free time-series of Landsat images [9; 10; 11; 12; 13; 14; 15; 16; 17]. The second is the spatiotemporal image fusion approach that generates image time series by synthesizing fine resolution images (such as Landsat and Sentinel-2 images) from a limited number of clear-sky fine resolution images and coarse resolution images, such as observations from a moderate resolution image spectroradiometer (MODIS) when cloud-free fine images are unavailable. Such examples include the spatial and temporal adaptive reflectance fusion model (STARFM) [18], enhanced STARFM (ESTARFM) [19], the spatial and temporal adaptive algorithm for mapping reflectance change (STAARCH) [20], the unmixing-based spatial-temporal reflectance fusion model (U-STFM) [21; 22], the flexible spatiotemporal data fusion (FSDAF) [23], prediction smooth reflectance fusion model (PSRFM) [24], and Kalman filter reflectance fusion model (KFRFM) [25], etc. As the image composite method uses only the observed time series images for the reconstruction process, its effectiveness depends on two factors. One is the availability of cloud-free pixels from the observations within a specified period used for composing a cloud-free image. Another is how well the cloud-free pixels or the clouds and cloud shadow pixels can be identified or distinguished effectively. Both factors still limit the quality of the composited time series images. To improve the performance of the composite method, [PERSON] and [PERSON] [17] introduced climate data (temperature and precipitation) and AVHRR (advanced very high resolution radiometer) data into their reconstruction process. Introducing the extra information into the harmonic modelling [14] of Landsat time-series enhances the reconstruction results in the presence of substantial sparsity [17]. By exploiting the sub-daily revisit frequency of MODIS on the Terra and Aqua satellites, the developed spatiotemporal image fusion approaches can augment the temporal intensity and circumvent the sparsity of clear-sky observations in Landsat and Sentinel-2 time series. However, when developing the spatiotemporal image fusion algorithms [18; 19; 20; 21; 22; 23; 24; 25], these approaches focused on the technical challenge of how to blend input images for higher quality output (blended) images.