Research of the Dual-Band Log-Linear Analysis Model Based on Physics for Bathymetry without In-Situ Depth Data in the South China Sea

The current widely used bathymetric inversion model based on multispectral satellite imagery mostly relies on in-situ depth data for establishing a liner/non-linear relationship between water depth and pixel reflectance. This paper evaluates the performance of a dual-band log-linear analysis model based on physics (P-DLA) for bathymetry without in-situ depth data. This is done using WorldView-2 images of blue and green bands. Further, the pixel sampling principles for solving the four key parameters of the model are summarized. Firstly, this paper elaborates on the physical mechanism of the P-DLA model. All unknown parameters of the P-DLA model are solved by different types of sampling pixels extracted from multispectral images for bathymetric measurements. Ganquan Island and Zhaoshu Island, where accuracy evaluation is performed for the bathymetric results of the P-DLA model with in-situ depth data, were selected to be processed using the method to evaluate its performance. The root mean square errors (RMSEs) of the Ganquan Island and Zhaoshu Island results are 1.69 m and 1.74 m with the mean relative error (MREs) of 14.8% and 18.3%, respectively. Meanwhile, the bathymetric inversion is performed with in-situ depth data using the traditional dual-band log-linear regression model (DLR). The results show that the accuracy of the P-DLA model bathymetry without in-situ depth data is roughly equal to that of the DLR model water depth inversion based on in-situ depth data. The results indicate that the P-DLA model can still obtain relatively ideal bathymetric results despite not having actual bathymetric data in the model training. It also demonstrates underwater microscopic features and changes in the islands and reefs.

[1]  Chuanmin Hu,et al.  Benthic classification and IOP retrievals in shallow water environments using MERIS imagery , 2020 .

[2]  ZhongPing Lee,et al.  Hyperspectral Shallow-Water Remote Sensing with an Enhanced Benthic Classifier , 2018, Remote. Sens..

[3]  André Morel,et al.  Diffuse reflectance of oceanic shallow waters: influence of water depth and bottom albedo , 1994 .

[4]  James W. Brown,et al.  A semianalytic radiance model of ocean color , 1988 .

[5]  Menghua Wang,et al.  Shallow water bathymetry with multi-spectral satellite ocean color sensors: Leveraging temporal variation in image data , 2020, Remote Sensing of Environment.

[6]  P. Reinartz,et al.  Machine learning-based retrieval of benthic reflectance and Posidonia oceanica seagrass extent using a semi-analytical inversion of Sentinel-2 satellite data , 2018, International Journal of Remote Sensing.

[7]  Jian-wei Shen,et al.  Formation Mechanism of Beach Rocks and Its Controlling Factors in Coral Reef Area, Qilian Islets and Cays, Xisha Islands, China , 2018, Journal of Earth Science.

[8]  David R. Lyzenga,et al.  Shallow-water bathymetry using combined lidar and passive multispectral scanner data , 1985 .

[9]  Jie Xu,et al.  Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations , 2018, Remote. Sens..

[10]  Gong Lin,et al.  A semi-analytical scheme to estimate Secchi-disk depth from Landsat-8 measurements , 2016 .

[11]  R. Stumpf,et al.  Determination of water depth with high‐resolution satellite imagery over variable bottom types , 2003 .

[12]  Yi Ma,et al.  Shallow Water Bathymetry Based on Inherent Optical Properties Using High Spatial Resolution Multispectral Imagery , 2020, Remote. Sens..

[13]  Dewei Xu,et al.  A dual band algorithm for shallow water depth retrieval from high spatial resolution imagery with no ground truth , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[14]  G. Asner,et al.  Adaptive bathymetry estimation for shallow coastal waters using Planet Dove satellites , 2019, Remote Sensing of Environment.

[15]  Xiaorun Li,et al.  An Exponential Algorithm for Bottom Reflectance Retrieval in Clear Optically Shallow Waters from Multispectral Imagery without Ground Data , 2021, Remote. Sens..

[16]  Rongyong Huang,et al.  Bathymetry of the Coral Reefs of Weizhou Island Based on Multispectral Satellite Images , 2017, Remote. Sens..

[17]  H. Gordon Remote sensing of ocean color: a methodology for dealing with broad spectral bands and significant out-of-band response. , 1995, Applied optics.

[18]  Ronghua Ma,et al.  The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters , 2019, Remote. Sens..