## 1 Introduction
Nevertheless, the spatial resolutions differ from 10 m for Sentinel-2, 30 m for Landsat and 250 m for MODIS which has a strong impact on the quality, especially on the resulting delineation of land and water masks at the shores. The Global Land Cover Facility (GLFC) developed the GLS2000 product which is a global surface water data set using Landsat and MODIS data for the year 2000 and with a spatial resolution of 30 m [16]. Another high-resolution global surface water data set based on Landsat and Google Earth Engine providing long-term information has been developed by [PERSON] et al., without pixel-wise accuracy and remaining data gaps [17]. The global WaterPack is a daily land-water data set based on MODIS data with a spatial resolution of 250 m where data gaps have been filled by a temporal interpolation [18]. The reconstruction of data gaps caused by voids, clouds, cloud shadows, or ice is an important step for the creation of homogeneous surface area time series. For that reason, a new approach has been developed in this paper. So far, several other approaches based on different data sets have already been developed. For example, data gaps in MODIS data with a high temporal resolution of 1-2 days can be reconstructed by applying a temporal interpolation [18]. Combining SAR with optical images is another approach to reduce data gaps caused by clouds [19]. Further multi-mission applications arise of using surface areas derived from optical images in combination with satellite altimetry for validation or computation of volume changes which has been already proven in several studies [8; 20; 21]. In this paper, a new approach for computing monthly surface area time series of lakes and reservoirs based on optical images from Landsat and Sentinel-2 is presented. This approach is divided into two major parts. The first part is the extraction of land-water masks with remaining data gaps by combining five water indexes and an automated threshold computation. The combination of different water indexes leads to more accurate land-water masks. The second part of this approach is the step of filling data gaps caused by voids, clouds, cloud shadows, or snow. This is performed by estimating a long-term water probability mask based on data gained between January 1984 and June 2018. Then, this mask is used in an iterative approach for filling the data gaps in all monthly masks. And finally, a gap-less surface area time series is computed for 32 selected lakes and reservoirs. The new gap-filling approach leads to improved and more consistent surface area time series if data gaps are corrected. The article is structured as follows. In Section 2 the data which serve as input for the processing and validation are described. In Section 3 the methodology for estimating surface area time series of inland waters based on classification and thresholds is explained. Section 4 begins with the introduction of 32 study areas. Then, three selected study areas are presented in detail followed by a validation and quality assessment of all study areas and its resulting surface area time series. The paper concludes with a summary of the results with discussion and an outlook.