## 5 Conclusions  
This paper presents a new approach for the automated extraction of consistent time-variable water surfaces of lakes and reservoirs using optical images from Landsat and Sentinel-2. It is based on a careful data pre-processing step creating monthly combined Landsat and Sentinel-2 scenes which are used as input for computing land-water masks. This is performed by estimating five established water indexes namely Modified Normalized Difference Water Index (MNDWI), New Water Index (NWI), Automated Water Extraction Index for Non-Shadow Areas (AWEI\({}_{\rm{nsh}}\)), Automated Water Extraction Index for Shadow Areas (AWEI\({}_{\rm{sh}}\)) and Tasseled Cap for Wetness (TC\({}_{\rm{wet}}\)). Then, an automated threshold separating land and water is computed based on mutual integration of five water indexes. All monthly land-water masks with data gaps caused by clouds, cloud shadows, snow/ice and voids are merged in order to compute a long-term water probability mask for the time period between January 1984 and June 2018 for the investigated inland water body. Based on the long-term water probability mask, all remaining data gaps in the monthly masks are filled using an iterative threshold approach. The results of this new method are gap-less surface area time series for lakes and reservoirs and their reliable area errors. The data holding of the DAHITI has been extended by that new product named surface area time series presented in this study.  
The performance of this new approach is assessed for 32 globally distributed lakes and reservoirs by comparison with water level variations from gauges or satellite altimetry. The validation shows no direct connection between surface area accuracy and the shape of the water body and the cloud coverage even if the internal errors increased in these cases. Calculating the squared Spearman correlation \(R^{2}\) between gap-filled surface area time series with respect to water level from in situ data and satellite altimetry leads to an average \(R^{2}\) of 0.834 and 0.880, respectively. Compared to the initial land-water masks with data gaps this improved by 37% and 44% respectively. The performed cross-validation shows RMS errors smaller than 40 km\({}^{2}\) for all study areas. This corresponds to relative errors below 8% for all 32 targets. These values are in the same order of magnitude than the internal surface area errors derived from the AWAX approach.  
The new method of computing gap-less surface area time series provides valuable results with a good quality for hydrological applications. But there is still potential for improvements and challenges for the future. In this study, the resulting surface area time series have a monthly temporal resolution. This can be increased by using daily scenes which can be also incomplete as the new approach can also fill images where only a part of the water body is covered. But this will be very challenging for the data management as the required data storage will increase dramatically. There are also possibilities to reduce the data gaps in the monthly scenes which will improve the quality of the resulting surface area time series. This can be achieved by introducing for example MODIS data which are available in lower spatial but higher temporal resolution. But also radar images from Sentinel-1 which are not affected by clouds can be used for creating improved land-water masks as an additional data set especially in very cloudy regions. Our approach has the capability to integrate further diverse data sets as long as they are reliable land-water masks. Finally, one can conclude that this is a very important data set for hydrological applications. Surface area time series have also the potential to be used for the creation of enhanced products such as storage variations of lakes and reservoirs. This method can also slightly be modified in order to derive changes of river widths from land-water masks and help in deriving river discharge.