## 1 Introduction  
Monitoring and modeling of the Earth's water cycle has become increasingly important in the last few years, especially in the context of climate change. Though only 0.013% [1] of the Earth's water is stored in lakes and reservoirs, the knowledge about storage changes is of great importance for the development of hydrological models. In the 1970s, the first remote sensing satellites such as Landsat-1 for mapping the Earth's surface and Seasat for measuring the water level from space were launched. In the following decades until the present day, numerous further remote sensing satellites carrying sensors of improved quality have been launched. Presently, the usage of multi-mission approaches allows scientists to create time series of surface areas or water levels over a period of more than 30 years for lakes and reservoirs. Time series give an insight in the dynamics of lakes and reservoirs which are fundamental for risk assessment of natural disasters. The number of reported natural disasters increased from about 150 in 1980 to more than 400 in 2008, where in 38% of the events can be assigned to wet mass movementssuch as floods [2]. Also, the number of dams used worldwide for irrigation, hydroelectricity, etc. has increased over the last few decades. This can lead to water shortage in countries such as Iran which can end up in political crisis [3]. Over the last few decades, the number of in situ data used for local monitoring has strongly decreased. The reasons are mainly that no data exists or is not accessible. Therefore, remote sensing is a valuable tool for observing water bodies independently from space. An example for decreasing of in situ data is the Global Runoff Data Center (GRDC) where the number of river discharge stations decreased from about 7900 to 600 between 1979 and 2018 [4]. The extraction and identification of water bodies is essential for applications such as monitoring flooded regions [5] and wetlands [6; 7], but also for the water storage quantification of lakes and reservoirs [8]. Remote sensing is also capable of observing long-term geomorphological changes of rivers and their meandering [9]. The main objective of using optical images is the classification of land cover types in categories such as water, vegetation, ice, etc. In this research field, one of the most common approaches is the land-water classification which is mainly based on the calculation of water indexes using different spectral information from optical images. Many water indexes such as Modified Normalized Difference Water Index (MNDWI) [10], New Water Index (NWI) [11], Automated Water Extraction Index for Non-Shadow Areas (AWEI\({}_{\text{Nh}}\)) [12], Automated Water Extraction Index for Shadow Areas (AWEI\({}_{\text{Nh}}\)) [12], and Tasseled Cap for Wetness (TC\({}_{\text{wet}}\)) [13] have been developed and used in different modifications, combinations, and approaches over the last few decades. Based on the calculated land-water indexes, the more challenging task is the delineation of land and water in optical images. [PERSON] et al. [14] gives a clear overview of various algorithms such as artificial neural networks, clustering, decision trees, Gaussian maximum likelihood, or Support Vector Machines which have already been used for the classification of land cover. This also includes the separation of land and water pixels. All these approaches have a common weakness which is that they require training data for their classification. This is not the case in our study. For the separation of land and water pixels, the proven Otsu threshold algorithm [15] is often used. However, this requires a bimodal histogram distribution. In this paper, a new approach for the threshold computation based on five histograms derived from different land-water indexes is demonstrated. A variety of global surface land-water products using optical images such as MODIS, Landsat, or Sentinel-2 has already been available for more than 30 years.