The log-ratio method (LRM) proposed by Stumpf et al. has been widely used to map bathymetry from multispectral imagery for oligotrophic waters, while the selection criteria of bands for the LRM have been subject to tradeoffs between maximum detectable depth and sensitivity. In this study, we first applied a method for global sensitivity analysis to a semi-analytical forward model of optically shallow waters with the WorldView-2 band-set. The results show that the sensitive wavelength band in water-leaving reflectance for water depth varies from the longer wavelength band to the shorter wavelength band with increasing water depth. Then, we developed an adaptive blended algorithm approach (ABAA) to seamlessly map bathymetry from the shallower region to the deeper region. The LRM with different band combinations was selected for the sub-algorithms of the ABAA. The sub-algorithms and depth range used for each sub-algorithm of the ABAA were automatically determined by the proposed applicable depth range analysis that considers logarithmic regression for the LRM. The ABAA was applied to WorldView-2 and Landsat-8 imagery of the Xisha Qundao. When the in-situ bathymetry data are available, compared with the LRM with the blue and green bands, the ABAA significantly improves the accuracy of the estimated depth, especially for waters shallower than 6 m (root mean square error (RMSE)=0.31 to 0.94 m for WorldView-2 data, RMSE=0.25 to 1.42 m for Landsat-8 data). When the in-situ bathymetry data are absent, the ABAA performs better than the LRM with a single band ratio and an optimization-based method overall.

This paper introduces multi-angular imagery into physics-based bathymetry in order to compensate for the shortage of bathymetric spectral bands caused by the low spectral resolution of current high-spatial-resolution satellite multispectral imagery. The focus is to propose a selective bathymetric retrieval method to eliminate the negative effect of non-optimal image data on depth retrieval in multi-angular imagery-based bathymetry. The elimination of the negative effect is implemented by excluding non-optimal pixels in every individual image from bathymetric retrieval. An empirical criterion is designed for the determination of non-optimal pixels. The proposed method can uses multi-angular image data selectively, avoiding situations where bathymetric retrieval results from the whole multi-angular imagery are poorer than that from part of the individual images. The method was tested in two typical areas within the Xisha (Paracel) Islands of the South China Sea using two-angle WorldView-2 multispectral images. The test showed that the derived depths of the method (i.e., depths derived from the selective image data) provided a better fit to the validation depths than those from the entirety of both images. The underestimation of depths derived from the entirety of both images was also improved to some extent.

Optical satellite remote sensing (RS) is a time- and cost-effective approach for shallow-water bathymetry over large areas. The photogrammetric and physics-based methods, which do not require in situ depth calibration data, are two implementations of RS-based bathymetry. This article compares these two bathymetric methods by using the same WorldView-2 imagery product of a study area in the Xisha Islands of the South China Sea. The focus is to investigate the difference between the resulting depths. The resulted accuracy of the photogrammetric method was slightly higher than that of the physics-based method based on the backward image, but both of them were significantly higher than that of the physics-based method based on the forward image. In the calm clear waters, the resulted depth of the photogrammetric method was closer to the ground truth than that of the physics-based method based on the backward image. In the shoal and adjacent waters where the water was turbid or larger waves were present, the photogrammetric method produced a highly noisy digital depth model (DDM), whereas the physics-based method derived a low-noise DDM from the backward image. In conclusion, the photogrammetric method outperforms the physics-based method in calm clear waters, but the physics-based method performs better than the photogrammetric method in shoal and adjacent waters that has turbid water or waves larger than the image spatial resolution. The photogrammetric DDM is noisier than the physics-based DDM. The accuracy of the physics-based method is also influenced by the non-optimal sun-target-sensor geometry of the images used.