Land surface water area (hereafter LSWA) is of paramount importance to the survival of all life forms (Karpatne et al., 2016). Water not only provides habitat for aquatic organisms but also affects various aspects of human life, such as for agricultural, domestic and industrial purposes (Vörösmarty & Sahagian, 2000). LSWA is highly dynamic and variations therein can be used as a direct indicator of climate change (Williamson et al., 2009) or human-induced changes (Pekel et al., 2016). LSWA is thus an essential variable in ecological, hydrological, climatic, and economic studies (Hirabayashi et al., 2013; Raymond et al., 2013; Willner et al., 2018). For such applications, accurate water information at adequate spatiotemporal resolution is crucial. Estimation of LSWA relies on three methods: ground surveys, remote sensing, and models. Among these methods, ground surveys cannot fully describe the water dynamics due to their slow updating frequency (Carroll et al., 2009; Lehner & Döll, 2004) and the significant cost of covering a large spatial domain. Remote sensing using satellites is an outstanding method that can provide regular large-scale observations of water surfaces. Various satellites have been used to identify LSWA, including Landsat (