Surface Water Mapping from SAR Images Using Optimal Threshold Selection Method and Reference Water Mask

Abstract

Water resources are an important component of ecosystem services. During long periods of cloudiness and precipitation, when a ground-based sample is not available, the water bodies are detected from satellite SAR (synthetic-aperture radar) data using threshold methods (e.g., Otsu and Kittler–Illingworth). However, such methods do not enable to obtain the correct threshold value for the backscattering coefficient (σ0) of relatively small water areas in the image. The paper proposes and substantiates a method for the mapping of the surface of water bodies, which makes it possible to correctly identify water bodies, even in “water”/“land” class imbalance situations. The method operates on a principle of maximum compliance of the resulting SAR water mask with a given reference water mask. Therefore, the method enables the exploration of the possibilities of searching and choosing the optimal parameters (polarization and speckle filtering), which provide the maximum quality of SAR water mask. The method was applied for mapping natural and industrial water bodies in the Pohjois-Pohjanmaa region (North Ostrobothnia), Finland, using Sentinel-1A and -1B ground range detected (GRD) data (ascending and descending orbits) in 2018–2021. Reference water masks were generated based on optical spectral indices derived from Sentinel-2A and -2B data. The polarization and speckle filtering parameters were chosen since they provide the most accurate σ0 threshold (on average for all observations above 0.9 according to the Intersection over Union criterion) and are resistant to random fluctuations. If a reference water mask is available, the proposed method is more accurate than the Otsu method. Without a reference mask, the σ0 threshold is calculated as an average of thresholds obtained from previous observations. In this case, the proposed method is as good in accuracy as the Otsu method. It is shown that the proposed method enables the identification of surface water bodies under significant class imbalance conditions, such as when the water surface covers only a fraction of a percent of the area under study.

Integrating Satellite Altimetry and SAR Technology for Manchar Lake Water Monitoring

“…This approach was also assessed with the Otsu threshold method. The method was found to be as good as the Otsu threshold with a reference water mask (Kavats, Khramov, and Sergieieva, 2022). Another study employed an optical remote sensing approach to calculate surface area variations of the Manchar Lake (Ismail et al, 2022).…”

“…Along with water levels, surface area calculation is also integral for the computation of volumetric variations in lakes. Different studies have utilized the interpretation of optical and SAR images to calculate water surface area (Kavats, Khramov, and Sergieieva, 2022). SAR data of Sentinel-1 were utilized to monitor Poyang Lake.…”

Comparative Performance Analysis of Simple U-Net, Residual Attention U-Net, and Vgg16-U-Net for Inventory Inland Water Bodies

“…While some approaches involve the use of multiple threshold values to identify an optimal threshold (Ji et al, 2009), this may not be universally applicable, particularly for water bodies with complex shapes, sizes, and spectral characteristics. Additionally, such methods may lack robustness in handling variations in image quality, resolution, and acquisition conditions (Sekertekin, 2021;Kavats et al, 2022). In water body classification, shadows produced by mountains, trees, buildings, and river banks can contaminate satellite imagery classification of water bodies (Pan et al, 2020).…”