## 6 Conclusions and Outlook  
In this study, a framework for the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) fusion algorithm was developed to make it applicable for large, cloud-prone and heterogeneous areas. For this purpose, several modifications were introduced that focus on the automation, acceleration of processing speed, handling of the algorithm and maximizing use of Landsat input data. The way similar pixels are selected for the regression between Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) was enhanced by accounting for spatial differences in the heterogeneity of the study area. Furthermore, an automatic filling of cloud gaps was developed to maximize the use of available cloud-free Landsat input data. In addition, the processing of time series in the ESTARFM framework was automated and processing speed was accelerated significantly by parallelization of the algorithm.  
The results indicate that even in a challenging study area with heterogeneous land cover and abrupt fire and flood events, the developed ESTARFM framework proved to be suitable to generate time series on larger spatial scales. The errors varied between average mean absolute error (MAE) of 0.02 for the dry season and 0.05 for the vegetative season which indicates a higher prediction uncertainty for the latter. These fused datasets have the potential to substantially improve various regional analyses of land degradation, changes in land surface phenology, or changes in cropping practice where a combination of high temporal and spatial resolution is essential for gaining new insights in land surface processes.  
With most recent satellite missions such as the European Space Agency's Sentinel program, new possibilities emerge for data fusion approaches extending and adding to the period of observationsthat can be covered by MODIS-Landsat time series (2000\(-\)present time). Especially the satellite constellations of Sentinel-2 (10 m, -5 days) and Sentinel-3 (300 m, -2 days) are predestined for data fusion. The short repeat cycle of the Sentinel-2 satellites will significantly contribute to a better availability of high-resolution data in comparison to Landsat. However, capturing the complete phenological cycle of land surface using only Sentinel-2 data is still not likely in cloud-prone areas. For such regions, the fusion of Sentinel-2 with Sentinel-3, Proba-V or VIIRS (Visible/Infrared Imager Radiometer Suite) could significantly contribute to a better quality of high temporal and spatial resolution time series.  
This work was conducted in the WASCAL (West African Science Service Center on Climate Change and Adapted Land Use) project and supported by the German Federal Ministry of Education and Research (BMBF) under Grant FKZ 01 LG1202D. We thank [PERSON] for providing the code of ESTARFM and for some helpful comments. The new collection 6 time series have been kindly provided by Professor [PERSON] and [PERSON], School for the Environment, University of Massachusetts Boston, before the official distribution via the USGS MODIS websites. We would also like to thank the three anonymous reviewers for their helpful comments.  
[PERSON] designed the algorithm developments and together with [PERSON] conceived and designed the experiments; [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] guided the processing and interpretation. [PERSON] wrote the first version of the manuscript and all authors jointly improved the version based on critical discussion.  
The authors declare no conflict of interest.