## 3 The Need for More Generic Data Restoration Strategies  
Most of the applied methods for image restoration beneath clouds suffer from certain major drawbacks. Simple substitution or compositing techniques either assume that no significant land cover changes between the time step of the image to be reconstructed and the time step of the image used for reconstruction have happened or they simply ignore temporal offsets entirely [21]. Furthermore, a completely cloud-free image cannot be guaranteed since certain pixels may be obscured by cloud cover in all images the composite is based on. Additionally, the image-wise correction of atmospheric conditions as well as radiometric differences between images is a challenging task and must be accomplished first. Interpolation over time requires a high temporal resolution and a sufficient length of the image acquisition period to cover seasonal aspects. The interpolation methods mentioned in Section 2.2 are restricted in terms of sensor type and/or ground cover type and depend on the cloud type [20]. Therefore, more generic approaches are necessary to restore the image information beneath clouds.  
To overcome the drawbacks of the existing algorithms, it is not necessarily required to come up with fundamentally new and complex image processing algorithms. The key to a reliable method to restore missing image information in general, and more specifically to remove cloud cover, might be already provided by existing data and technology. A straightforward combination of complementary data and meaningful algorithms is a possible solution to restore error-affected satellite images in a more suitable way. The essential step to accomplish this objective is to take into account the complementary nature of data sources like multi-spectral and SAR data. Intuitively, the differences between the image acquisition processes, the underlying physics and the differing imaging geometry appear to be an obstacle for the synergistic combination of these two data sources. However, by taking a closer look, the synergetic potential becomes obvious. Both multi-spectral and SAR data rely on interactions of electromagnetic radiation with the Earth's surface. Based on this premise it seems reasonable to investigate the synergetic potential of these data sources to fill data gaps as shown by [22]. Figure 1 demonstrates the plausibility of this hypothesis. Comparing a single SAR band (e.g., L-HH) to a multi-spectral RGB image, the separability between the features is very poor, especially for agricultural areas. However, by combining different frequency bands (e.g., X-HH\(\|\)X-HVL-HH) to a color composite, the strong relation between visible/near-infrared (VIS/NIR) and microwave information becomes obvious.  
Figure 1: Comparison of SAR backscatter intensities, a Landsat multi-spectral image and SAR color composites. (**a**) X-HH; (**b**) C-VV; (**c**) L-HH; (**d**) TM5 B3\(\|\)B2\(\|\)B1; (**e**) X-HH\(\|\)L-HH\(\|\)C-VV; (**f**) X-HH\(\|\)X-HV\(\|\)L-HH.  
Therefore, this study combines multi-spectral and SAR satellite images to reconstruct cloud affected areas in the multi-spectral images. On the way to a meaningful technology aiming for the mentioned goal, it is mandatory to be able to evaluate the quality of the image reconstruction results. Therefore a complete framework for image reconstruction and quality estimation was implemented (Figure 2), comprising cloud cover simulation, image restoration and statistical evaluation of the differences between the original cloud-free image and the associated restoration result. In the following the individual modules of Figure 2 are described in detail.  
Figure 1: _Cont._  
Figure 2: Workflow of the study.