# Application of Image Segmentation in Surface Water Extraction of Freshwater Lakes using Radar Data
## 1 Introduction  
Water is significant for all ecosystems on Earth. The presence of surface water on Earth mainly consists of oceans, lakes and rivers [1]. The extent of lakes accounts for nearly 3% of the surface [2] and is endowed with irreplaceable functions to supply water [3], control flooding [4], sustain species [5] and provide ecosystem services to nations and regions [6] due to the unique role of water in climate [7], biological diversity [8] and human wellbeing [9]. Meanwhile, natural phenomena and human activities affect the variation of water occurrence in response, especially the water dynamics of inland freshwater lakes [10]. Timely monitoring of freshwater lake surface is indispensable for sustainable development [11] and regional and global ecosystem dynamics [12]. Remote sensing, the science and art of detecting objects from a distance, has been the most common approach to monitor and analyze land features for several decades [13]. In imagery, land features aretypically represented as mixed classes of different vegetation cover and surface types. There are many satellite-based sensors that differ in terms of temporal and spatial resolution, corresponding to revisit time and ground area represented by a pixel, respectively. Medium resolution imagery is the most widely used for lake water surface detection, (with approximately 10 days revisit time and each pixel ranging from 10 to 30 m), due to its open access compared to the cost of acquiring higher resolution imagery [14] and are less prone to the mixed pixels problem of coarse resolution imagery [15]. Aside from temporal and spatial resolution, there are both passive sensors and active sensors. Passive sensors, known as optical systems, have been employed since the 1970s when the first satellite sensor, Landsat multispectral scanner (MSS), was launched into space [16]. However, lack of vertical information, issues with wetland vegetation overlapping canopy, and haze and cloud cover problems have largely impeded the accuracy of results [17]. Thus active sensors, particularly radar systems, have also contributed to remote sensing of water dominated systems, such as lakes. Radar backscatter is sensitive to moisture content and roughness of landscape, and the wavelength of C-band Sentinel-1 sensor enables penetration of both clouds and thick canopies to deal with the challenges of complicated weather and flora conditions [18]. Nevertheless, the procedure of processing Sentinel-1 radar data involving data acquisition, calibration, speckle filtering, geometry and terrain correction, classification and validation [19] is extremely time consuming with use of traditional image process platforms, even those with built-in toolboxes, such as ENVI and ERDAS software packages [20]. This cost can limit the timeliness and efficiency of research. With the help of high-performance computing and network systems, Google Earth Engine (GEE) allows online processing and analysis of radar imagery by writing light-weight scripts with a Google account, speeding the process in a cloud-based platform [21]. The plethora of data catalogs and innovative processing algorithms provided by GEE can effectively eliminate the barriers caused by the traditional platforms. The water detection algorithms based on radar sensors have emerged in several categories: thresholding, classification and object-based image analysis. In general, thresholding has commonly been adopted to discriminate water from nonwater surface in the logarithmic representation of the radar imagery, where the water and nonwater features are shown as two Gaussian distributions in the histogram of backscatter coefficient of radar data in dB scale. Although it is limited by double bounce scattering issues because waters beneath vegetation layers may cause extra radar backscatter [22], thresholding is still an efficient and simple method for water extraction of rural areas in winter season with less complicated vegetation coverage.