Bathymetric Retrieval Selectively Using Multiangular High-Spatial-Resolution Satellite Imagery

Abstract

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.

Nearshore Bathymetry Based on ICESat-2 and Multispectral Images: Comparison Between Sentinel-2, Landsat-8, and Testing Gaofen-2

“…Compared with the linear method, the band ratio method reduces the parameters that need to be adjusted, and at the same time exhibits stronger stability. Liu et al [32] proposed an adaptive blended algorithm approach to improved the method proposed by Stumpf, which significantly improved the accuracy of the estimate of water depth; Cao et al [33] used the band ratio method to estimate the depth of the St. Croix area; Forfinski-Sarkozi et al [34] used the Stumpf's method to fuse singlephoton data with Landsat-8 data to draw the Keweenaw Bay nearshore water depth map; Ma et al [24] used the above two classic methods to fuse single-photon data as in-situ data with Sentinel-2 images and the map of water depth map was drawn on the shallow water area of Acklins Island.…”

Satellite-Derived Bathymetry of an Inland Reservoir in India

“…The works use depth information collected from acoustic systems [11]- [13], [15], [16], [20]- [23], [31]- [35], airborne LiDAR systems [10], [11], [14], [17], [19], [26], [28], [36]- [38] or LiDAR data obtained from Ice, Cloud, and Elevation Satellite-2 (ICESat-2) satellites [18], [29], [30], as ground truth data. The study areas of interest in most of the above works belong to coastal regions [10]- [12], [14], [15], [17]- [19], [21], [28], [32], [34], [37], [39] and only a small number of works study SDB for inland water bodies [13], [16], [38]. As mentioned, most of the above works consider the reflectance values collected by satellites as input for estimating depth of water columns.…”

“…In the existing works, the trained models are evaluated on the same region in [11], [13], [21], [23], [30], [32]- [34], [37], [38] and different nearby regions in [10], [12], [14]- [19], [26], [28], [29], [35], [36]. We evaluate the proposed models in the same region, as in multiple works above.…”