## 3 Methodology
### Computation of Monthly Land-Water Masks with Data Gaps
#### 3.2.1 Pre-Processing  
In the pre-processing step, monthly composite images and quality flags derived from Landsat and Sentinel-2 are created. Figure 3 gives an overview of the applied steps to achieve monthly composites. Acquisition and Pre-Processing of Landsat Imagery  
The coarse boundary of the inland water body from the initialization is used as input data to extract the data of interest using the Google Earth Engine. First, available scenes of the different Landsat missions within the area and month of interest are selected and stored in an image collection. Every scene contains additional information such as cloud coverage in percent and a quality band with flags for clouds, cloud shadows, and snow calculated by the CFMASK algorithm. These are used  
Figure 3: Flowchart of the AWAX-Pre-processing. for merging all scenes to create the best gap-free composite image. The creation of composite images is performed pixel-wise in the Google Earth Engine by iterating trough all scenes sorted by cloud coverage, searching for the smallest quality flag. Considering the order of cloud coverage leads to a more homogeneous cloud-free composite image. In this study, we use the red (\(RED\)), green (\(GREEN\)) and blue (\(BLUE\)) bands as well as the near-infrared (\(NIR\)), shortwave infrared 1 (\(SWIR_{1}\)) and shortwave infrared 2 (\(SWIR_{2}\)) bands. Additionally, existing masks of voids, clouds, cloud shadows and snow are extracted for managing data gaps and are stored in a quality (\(QUALITY\)) band. All seven bands are downloaded as monthly composites from the Google Earth Engine. Acquisition and Pre-Processing of Sentinel-2 Imagery  
The data source for downloading Sentinel-2 images is the Copernicus Open Access Hub provided by the ESA. In this study, the Level-1C data set of Sentinel-2A and Sentinel-2B are used. These scenes are stored as tiles with an extent of \(100\,\mathrm{km}\times 100\,\mathrm{km}\) which are TOA reflectances in cartographic geometry. Initially, all available Sentinel-2A and Sentinel-2B tiles within the boundary of the inland water body defined in the initialization step are downloaded. Naturally, Level-1C are only raw data sets, without additional information such as clouds, cloud shadows, or snow flags which are required in this study. Therefore, we apply the FMask algorithm [27] in advance to create these flags. Based on the computed flags and the daily images, we create a monthly composite which is as free of gaps as possible by averaging all gap-free pixels. Since the spatial resolution for the bands differ (\(10\,\mathrm{m}\) for \(RED\), \(GREEN\) and \(BLUE\), \(20\,\mathrm{m}\) for \(NIR\), \(SWIR_{1}\) and \(SWIR_{2}\)), we resample the infrared bands to \(10\,\mathrm{m}\) spatial resolution. Combination of Landsat and Sentinel-2 Imagery  
In this step, the monthly Landsat and Sentinel-2 composites are merged to achieve a multi-mission composite. A monthly temporal resolution was selected to reduce the amount of data. But this approach can also be adapted on daily images. Since both data sets have different spatial resolutions ('\(10\,\mathrm{m}\)' or '\(30\,\mathrm{m}\)') and projections ('\(WGS\,84\)' or '\(WGS\,84\)/UTM'), a homogenization is necessary. In our case, all the monthly Landsat and Sentinel-2 composites are resampled to a spatial resolution of \(0.0001^{\circ}\) which is about \(10\,\mathrm{m}\) and reflects the spatial resolution of Sentinel-2. Also, we reproject all images to the WGS84 coordinate system. Then, the images are cut out with respect to the polygon of the investigated water body to get two images with an identical shape. Finally, we merge each band of both images pixel-wise. This is performed for the \(RED\), \(GREEN\), \(BLUE\), \(NIR\), \(SWIR_{1}\), \(SWIR_{2}\) and \(QUALITY\) band. If both pixels are without data gaps, both pixels of each data set are averaged. In case of a data gap in one of the two data sets, the gap-less value is taken. If both pixels have data gaps, the data gap remains in the final merged composite.