## 6 Discussion  
### Methodology  
The depicted framework of this study, comprising cloud simulation, image reconstruction and statistical evaluation, was designed as basic research tool for (1) the evaluation of the performance of the Closest Feature Vector (CFV) technique, and (2) the potential of space-borne SAR data to fill data gaps in multi-spectral imagery. The cloud simulation is a mandatory part since it facilitates the comparison of the original image with the reconstructed image based on statistics. The application to a real case, _i.e._, \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Agriculture** & **Forest** & **Urban** \\ \hline SAM & 0.0917 & 0.1100 & 0.1558 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Spectral angle mapper. Figure 19: Statistical evaluation of the image restoration depending on land cover classes. (**a**) Mean Bias; (**b**) Difference of Variances; (**c**) Standard Deviation of the Difference Image; (**d**) Correlation Coefficient. integrating the image reconstruction together with cloud detection in an actually clouded image, can be considered as a subsequent research topic. In this study, this \"real case scenario\" was not considered, since the focus of the conducted work was on analyzing the capability of SAR data together with CFV to reconstruct data gaps. Therefore, the artificial introduction of missing information was mandatory. The statistical evaluation protocol covered every aspect of image quality according to [PERSON] and [PERSON] [37]. This study was focused on the assessment of the overall feasibility of the integration of SAR data as a reference for cloud removal. Therefore, the global image quality was assessed by comparing every pixel of the original image to every pixel of the reconstructed image. Using this approach, conclusions on the overall image quality for classification, parameter retrieval, _etc._, can be drawn. Therefore all statistics shown in this study were derived straightforwardly by this method. As an alternative, a detailed reconstruction quality can be assessed by evaluating the statistical protocol only for filled areas. This approach allows for an enhanced understanding of error sources in the fill algorithm. This statistical evaluation can be considered as useful tool for further development of the fill algorithm and for the analysis of the nature of clutter and noise effects. Nevertheless, this detailed error analysis was beyond the focus of this study. The used reconstruction approach depicts a special form of the concept of image fusion. By definition \"image fusion is the combination of two or more different images to form a new image by using a certain algorithm\" [45]. The proposed approach does not denote the fusion of the pixel DN values per se; the algorithm rather makes use of a derivation of geolocation values. This is a major advantage in comparison with any substitution technique since the reconstructed DNs are completely retrieved from the very image that is supposed to be filled. Therefore, based on the assumption of homogenous atmospheric conditions within every image, atmospheric and radiometric corrections between the images are not necessary when following the proposed approach. Nevertheless, certain requirements are made on the data basis. The coregistration of optical and SAR data has to be as accurate as possible to avoid the mismatch of pixels. This is the major prerequisite for the successful application of the algorithm. It is also very important to correct for the differing viewing geometries of optical and SAR data. Topographic normalization of the Radar images is necessary to avoid artifacts from SAR shadow, Layover and Foreshortening effects. The restoration accuracy also depends on the time gaps between the clouded image and the fill image. In general, it is understood that the shorter the time span between the acquisition of the two images, the better the reconstruction quality, since the land cover was not changing too much.