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This article introduces multiangular 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 nonoptimal image data on depth retrieval in multiangular imagery-based bathymetry. The elimination of the negative effect is implemented by excluding nonoptimal pixels in every individual image from bathymetric retrieval. An empirical criterion is designed for the determination of nonoptimal pixels. The proposed method can use multiangular image data selectively, avoiding situations where bathymetric retrieval results from the whole multiangular imagery are poorer than that from a 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.

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 article, we first applied a method for global sensitivity analysis to a semianalytical 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 subalgorithms and depth range used for each subalgorithm 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.

Bathymetry is important to shallow coral reef management. Remote sensing is one of the most important techniques for mapping bathymetry. For instance, multispectral remote sensing images have been widely used to map bathymetry of coral reefs. These physically based methods, which are able to retrieve bathymetry, water column inherent optical properties (IOPs), and benthic reflectance simultaneously, generally can achieve good bathymetry results. However, there are strong limitations when facing the scenarios of mixing benthic reflectance for bathymetry with multispectral images. In this study, aiming at handling the mixing phenomenon on the use of multispectral remote sensing images for mapping bathymetry, we propose a new unmixing-based method, namely unmixing-based multispectral optimization process exemplar method (UMOPE). The new method incorporates endmember variability with a linear combination of three fixed endmembers and the relaxation of the sum-to-one constrained. UMOPE was validated with two existing methods with in situ data and a WorldView-2 image in the South China Sea. Results from in situ data show that the proposed method performs the best, with the smallest absolute root mean square error ${\rm{(RMSE)}}$ (2.26 m) and the best agreement ( $R^{2}= {\text{0.91}}$ ) between the measured and estimated water depth. Moreover, results from WV-2 imagery demonstrate the superior performance for the UMOPE at depth range from about 9 to 26 m. Furthermore, since the errors from IOPs can propagate to bathymetry, we further take this issue into account by analyzing the rule of influence of bottom on IOPs. Finally, the result of benthic classification map from UMOPE is shown.