## 7 Conclusions and Outlook  
This study focuses on the problem of cloud cover in multi-spectral satellite data. The presented approach combines the Closest Spectral Fit (CSF) algorithm proposed by [PERSON] _et al._[35] with thesynergistic application of multi-spectral satellite images and multi-frequency Synthetic Aperture Radar (SAR) data. The image restoration algorithm overcomes several disadvantages of other methods. As the algorithm replaces missing digital numbers (DNs) with information from the same image, a radiometric calibration becomes obsolete. Furthermore, the approach is independent of sensor specifications, land cover types or seasonal dynamics. The analyses are embedded in a statistically reliable framework, which is capable to investigate the image reconstruction quality based on objective measures. The applied methodology includes a three stage approach comprising an examination of the plausibility of the CSF method, the reconstruction of a multi-spectral SPOT image by means of a Landsat image, as well as the reconstruction of cloud contaminated areas in a Landsat image with the aid of multi-frequency SAR data. The results indicate the potential of mono-temporal SAR data to overcome data gaps in multi-spectral images under certain restrictions. The outcomes evidently show that a synergistic usage of the complementary nature of multi-spectral and SAR satellite images can help to overcome limitations in data availability and quality. An urgent need for future work resulting from this study is the implementation of the algorithm and experimental setup in a greater framework. Only in case of further improvements can this approach make its way towards an operational approach. Based on the study findings and the implications discussed, three major aspects of possible future research are summarized here. The integration of object based approaches as an avenue to adaptive spatial averaging implies a great potential to increase the homogeneity and reliability of the image restoration results. The extraction of small, non-overlapping homogenous objects may significantly enhance the usability of the data, especially in the framework of land cover classifications. According to the results of the study, the integration of additional information increases the agreement certainty and therefore the image reconstruction quality. At the moment, the examination of interferometric coherence data would violate the rule of short temporal baselines. However, in the course of the ESA Living Planet program, the C-Band SAR Satellite constellation Sentinel 1A and 1B will enable the derivation of coherence data with a revisit cycle of 6 days [47, 48]. This temporal baseline would enable the integration of this type of information into the image restoration process. Furthermore, the potential of full-polarimetric SAR systems (e.g., TerraSAR-X/TanDEM-X, ALOS PalSAR) is worth exploring. In addition, the integration of multi-spectral data in order to remove cloud cover from different multi-spectral images should be the subject of further investigation. Sensors with a high temporal resolution (e.g., MODIS [49]) have the inherent potential to overcome the drawback of significant land cover changes between the image acquisitions. Furthermore, this study indicated that the image reconstruction quality in case of a multi-spectral fill image is very high (_cf._ Section 5.1.3). An essential step towards the evaluation of the operational practicability of the presented approach is an extensive investigation of the restoration sufficiency of certain land cover types. For a reliable knowledge of the expectable reconstruction quality of a certain land cover class, the analysis must be applied to distinct agricultural classes, biotope habitats, different forest types, urban areas, _etc_. Furthermore, the application of the CFV technique can be extended to several scientific issues within the field of remote sensing. Since cloud cover can be regarded as areas of unavailable data, also other cases of missing data can be examined.