Although optical remote sensing can intuitively detect algal bloom, it is limited by the weather conditions. Synthetic aperture radar (SAR) is not affected by inadequate weather conditions. According to visual interpretation of SAR images and comparisons of quasi-synchronized optical images, the gathering areas of algal bloom present as “dark regions” on SAR images. It is shown that using SAR to monitor the water surface is workable. However, dark regions may also be caused by other factors, such as low wind speeds. This challenges with SAR monitoring of algal bloom on the water surface. In this study, an improved K-means algorithm, combined with multi-Otsu thresholding algorithm, was proposed to segment the dark regions. After feature analysis and extraction of Sentinel-1A images, an algal bloom recognition model with a support vector machine (SVM) was applied to discriminate the algal bloom dark regions from the low wind dark regions. According the experimental results, the overall accuracy achieved 74.00% in Taihu Lake. Additionally, this method was also validated in Chaohu Lake and Danjiangkou Reservoir. Therefore, it can be concluded that SAR can provide a new technical means for monitoring algal bloom of inland lakes, particularly when it is cloudy and unsuitable for optical remote sensing. To obtain more information about algal bloom, multi-band and multi-polarization SAR images can be considered for future.

Great inland freshwater lakes play an important role in regulating inland water resources, and the usage of synthetic aperture radar (SAR) images for the accurate waterline mapping is an effective technical means to study the dynamic changes of great inland lakes. In this article, the Danjiangkou (DJK) reservoir is selected as a study case, and a novel waterline mapping method with four main parts is proposed to monitor the water area dynamically. First, a coarse segmentation method is implemented to extract the initial waterline. Second, a strategy of division in local regions is given to speed up the subsequent processes. Third, a combination of a speckle filter and an improved geometric active contour model is used for refined segmentation. Finally, a change detection method is used to study the changing lake. Furthermore, six SAR images obtained by the Gaofen-3 (GF-3) and Sentinel-1A (S-1A) satellites in the DJK reservoir, Hongze lake and Poyang lake are tested to verify the universality of the proposed water area extraction method. The results demonstrate excellent performances with an accuracy of over 97% and an average contour offset under 0.7 pixels. Besides, the time-series analysis of the DJK reservoir is applied based on the mapped waterlines of 37 SAR images collected from January to December in 2017. Comparing with the changing tendency of the water level surveyed in the DJK reservoir, the waterline mapping results and the filed survey data have great consistency, which further proves the validity of the proposed method, also presents the significant potential of the GF-3 and S-1A SAR images for managing the water resource.

For accurate motion compensation (MOCO) in airborne synthetic aperture radar (SAR) imaging, a high-precision inertial navigation system (INS) is required. However, an INS is not always precise enough or is sometimes not even included in airborne SAR systems. In this paper, a new, raw, data-based range-invariant motion compensation approach, which can effectively extract the displacements in the line-of-sight (LOS) direction, is proposed for high-resolution sliding spotlight SAR mode. In this approach, the sub-aperture radial accelerations of the airborne platform are estimated via a well-developed weighted total least square (WTLS) method considering the time-varying beam direction. The effectiveness of the proposed approach is validated by two airborne sliding spotlight C band SAR raw datasets containing different types of terrain, with a high spatial resolution of about 0.15 m in azimuth.