Evaluation of validity of bathymetry retrieval data based on high-spatial resolution remote sensing image

Wang Yanru; Zhang Liyong; Liu Wen; Zhang Kai; Wang Xin

doi:10.12284/hyxb2023026

Volume 45 Issue 3

Feb. 2023

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Article Navigation > Haiyang Xuebao > 2023 > 45(3): 136-146

Wang Yanru，Zhang Liyong，Liu Wen, et al. Evaluation of validity of bathymetry retrieval data based on high-spatial resolution remote sensing image[J]. Haiyang Xuebao，2023, 45(3)：136–146 doi: 10.12284/hyxb2023026

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Evaluation of validity of bathymetry retrieval data based on high-spatial resolution remote sensing image

doi: 10.12284/hyxb2023026

Wang Yanru^1
,,
Zhang Liyong¹,
Liu Wen¹,
Zhang Kai^{1, 2
,
,},
Wang Xin^{1, 3}

1.
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2.
Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
3.
Guangzhou Sanhai Marine Engineering Surveying & Designing Co., Ltd., Guangzhou 510220, China

Received Date: 2022-05-10
Rev Recd Date: 2022-08-25

Available Online: 2022-09-05

Publish Date: 2023-02-01

Abstract

Abstract

Satellite derived bathymetric using multispectral imagery is an effective means to obtain shallow water depth information. However, its validity is limited to optical shallow water areas, but presents a “pseudo-shallow sea” distortion phenomenon in deep water areas. Therefore, accurately identifying the valid region of satellite derived bathymetry (SDB) data is crucial for its wide application. Based on high-spatial resolution remote sensing image, a data-driven method for evaluating the validity of SDB based on analysis of the differences in the statistical distribution of radiance in deep/shallow water regions is proposed in this paper. This method uses the local standard deviation of the radiance information of satellite images as a feature, optimizes the statistical characteristics of the optical deep water area based on the K-S test method, and uses the hypothesis test method to identify the SDB corresponding to the deep water invalid area. The experimental results in Ganquan Island region show that the method can effectively identify the invalid SDB associated with the optical deep water area by dividing the boundary between optical shallow and deep water area. After removing the invalid data, the mean absolute error (MAE) of SDB in the optical shallow region is 1.01, and the root mean square error (RMSE) is 1.52. The experimental results show that the proposed method can accurately identify the optical shallow region of SDB result, which benefits the interpretation and application of SDB results.
- bathymetric retrieval,
- data validity,
- pseudo-shallow sea,
- optical shallow water area

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References(33)

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