This study investigated how the synthetic aperture radar (SAR) parameters and natural conditions affect the spatial distribution of backscattering coefficient of SAR images around the shoreline. Primary findings of this study are summarized as follows: (i) the HH polarization mode is the best mode for shoreline detection; (ii) SAR scenes with incident angle ranging from 30° to 50° under sea-to-land observation direction are recommended for shoreline detection; (iii) clear contrast of the backscattering coefficient between the beach and sea is expected when the nearshore significant wave height is smaller than 0.5 m for L-band SAR scenes; and (iv) X-band SAR scenes may be preferred to L-band SAR scenes for shoreline detection especially where the grain size of the beach material is relatively fine.

Monitoring shoreline change is one of the essential tasks for sustainable coastal zone management. Due to its wide coverage and relatively high spatiotemporal monitoring resolutions, satellite imagery based on synthetic aperture radar (SAR) is considered a promising data source for shoreline monitoring. In this study, we developed a robust shoreline detection method based on satellite SAR imagery using an artificial neural network (NN). The method uses the feedforward NN to classify the pixels of SAR imagery into two categories, land and sea. The shoreline location is then determined as a boundary of these two groups of classified pixels. To enhance the performance of the present NN for land–sea classification, we introduced two different approaches in the settings of the input layer that account not only for the local characteristics of pixels but also for the spatial pixel patterns with a certain distance from the target pixel. Two different approaches were tested against SAR images, which were not used for model training, and the results showed classification accuracies higher than 95% in most SAR images. The extracted shorelines were compared with those obtained from eye detection. We found that the root mean square errors of the shoreline position were generally less than around 15 m. The developed method was further applied to two long coasts. The relatively high accuracy and low computational cost support the advantages of the present method for shoreline detection and monitoring. It should also be highlighted that the present method is calibration-free, and has robust applicability to the shoreline with arbitrary angles and profiles.

The present study develops a SAR-based shoreline monitoring system which consists of: (i) selection of SAR scenes suitable for shoreline detection; (ii) extraction of shoreline from the selected scenes; and (iii) estimation of the shoreline at arbitrary water level. The first model focused on ALOS-2 SAR scenes and determined fitting formulae for estimation of the backscattering coefficients at a sea and a beach surfaces. To quantify the suitability of scenes for shoreline detection, a parameter, P, was newly introduced. The value of P represents a probability at which a backscattering coefficient at a beach becomes higher than the one at a sea and it was confirmed that P reasonably indicates suitability of scenes for shoreline detection. A shoreline extraction technique was then developed based on the edge detection technique. Bilateral filter was applied to reduce the speckle noise of SAR images and wavelet transform was applied to detect the edge location. Validity of the proposed technique was confirmed through application of the present technique to Shimizu and Shonan coasts. Finally, a simple polynomial function was introduced to represent the shoreline location at arbitrary water level. Applicability of the proposed technique was also tested and validated against the detailed data obtained by image analysis.

In recent years, the development of China’s marine aquaculture has brought serious challenges to the marine ecological environment. Therefore, it is significant to classify and extract the aquaculture zone and spatial distribution in order to provide a reference for aquaculture management. However, considering the complex marine aquaculture environment, it is difficult for traditional remote sensing technology and deep learning to achieve a breakthrough in the extraction of large-scale aquaculture zones so far. This study proposes a method based on the combination of piecewise linear stretching and R³Det to classify and extract raft aquaculture and cage aquaculture zones. The grayscale value is changed by piecewise linear stretching to reduce the influence of complex aquaculture backgrounds on the extraction accuracy, to effectively highlight the appearance characteristics of the aquaculture zone, and to improve the image contrast. On this basis, the aquaculture zone is classified and extracted by R³Det. Taking the aquaculture zone of Sansha Bay as the research object, the experimental results showed that the accuracy of R³Det in extracting the number of raft aquaculture and cage aquaculture zones was 98.91% and 97.21%, respectively, and the extraction precision of the area of the aquaculture zone reached 92.08%. The proposed method can classify and extract large-scale marine aquaculture zones more simply and efficiently than common remote sensing techniques.

Many urban flood models employ coarse grids to reduce the computational cost of real‐time forecasting. Such a coarse grid, however, may result in poor predictions of the discharge rate of surface water because of difficulties in representing the detailed features of drainage facilities. In the present study, an urban flood model that consists of a one‐dimensional (1D) river flow model, a two‐dimensional (2D) ground surface flow model, and a 1D sewer network model was developed. A coarse grid is used for the 2D ground surface flow model, and the tail networks of the sewer systems are neglected to reduce computational cost. A novel approach is proposed for determining the integrated water discharge from the ground surface to the trunk sewer network to retain the accuracy of a simplified coarse‐grid system. Numerical experiments were performed to evaluate the performance of the proposed method. Measured water levels at manholes were employed to examine the accuracy of the model. The results show that this approach significantly improves the accuracy of the simulated water level in a sewer network with enhanced computational efficiency.