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Volume 17
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10.3390/rs17183179
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Article

Machine Learning-Constrained Semi-Analysis Model for Efficient Bathymetric Mapping in Data-Scarce Coastal Waters

by
Qifei Wang
1,
Xianliang Zhang
1,2,
Zhongqiang Wu
3,4,
Chang Han
1,
Longwei Zhang
1,5,
Pinyan Xu
1,2,
Zhihua Mao
1,*,
Yueming Wang
6 and
Changxing Zhang
6
1
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
2
Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
3
School of Information Science and Technology, Hainan Normal University, Haikou 571158, China
4
Hainan Engineering Research Center for Extended Reality and Digital Intelligent Education, Hainan Normal University, Haikou 571158, China
5
Ocean College, Zhejiang University, Hangzhou 316000, China
6
Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(18), 3179; https://doi.org/10.3390/rs17183179
Submission received: 10 August 2025 / Revised: 5 September 2025 / Accepted: 12 September 2025 / Published: 13 September 2025
(This article belongs to the Special Issue Remote Sensing of Coastal, Wetland, and Intertidal Zones)
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Abstract

Nearshore bathymetry is critical for coastal management and ecology. While airborne hyperspectral remote sensing provides high-resolution image data, obtaining rapid and accurate bathymetric inversion in coastal areas lacking in situ data remains challenging. The widely used Hyperspectral Optimization Process Exemplar (HOPE) achieves high accuracy but suffers from computational inefficiency, making it impractical for large-scale, high-resolution datasets. By contrast, HOPE-Pure Water (HOPE-PW) offers computational efficiency but exhibits limitations in capturing fine-scale spatial patterns of bottom reflectance (ρ), and its applicability in transitional waters between Case I and II types requires further validation. Against this background, we employed machine learning-based substrate classification (support vector machine, random forest, maximum likelihood) in Wenchang coastal waters, China, to constrain ρ estimation in HOPE-PW, with validation using ICESat-2 data that extends its conventional application scenarios. Results demonstrate that when constrained by the optimal classifier (random forest), HOPE-PW achieves comparable accuracy to HOPE in shallow water while reducing runtime by 56% and memory usage by 68%. However, HOPE-PW exhibits slight underestimation in deeper areas, likely because simplification reduces sensitivity to water optical properties. Future research will focus on this issue. This study proposes an efficient and reliable framework for monitoring and evaluating water depth in areas lacking in situ data, offering a practical solution for integrated coastal zone management.
Keywords: airborne hyperspectral; machine learning; substrate classification; bathymetric inversion; ICESat-2 airborne hyperspectral; machine learning; substrate classification; bathymetric inversion; ICESat-2

Share and Cite

MDPI and ACS Style

Wang, Q.; Zhang, X.; Wu, Z.; Han, C.; Zhang, L.; Xu, P.; Mao, Z.; Wang, Y.; Zhang, C. Machine Learning-Constrained Semi-Analysis Model for Efficient Bathymetric Mapping in Data-Scarce Coastal Waters. Remote Sens. 2025, 17, 3179. https://doi.org/10.3390/rs17183179

AMA Style

Wang Q, Zhang X, Wu Z, Han C, Zhang L, Xu P, Mao Z, Wang Y, Zhang C. Machine Learning-Constrained Semi-Analysis Model for Efficient Bathymetric Mapping in Data-Scarce Coastal Waters. Remote Sensing. 2025; 17(18):3179. https://doi.org/10.3390/rs17183179

Chicago/Turabian Style

Wang, Qifei, Xianliang Zhang, Zhongqiang Wu, Chang Han, Longwei Zhang, Pinyan Xu, Zhihua Mao, Yueming Wang, and Changxing Zhang. 2025. "Machine Learning-Constrained Semi-Analysis Model for Efficient Bathymetric Mapping in Data-Scarce Coastal Waters" Remote Sensing 17, no. 18: 3179. https://doi.org/10.3390/rs17183179

APA Style

Wang, Q., Zhang, X., Wu, Z., Han, C., Zhang, L., Xu, P., Mao, Z., Wang, Y., & Zhang, C. (2025). Machine Learning-Constrained Semi-Analysis Model for Efficient Bathymetric Mapping in Data-Scarce Coastal Waters. Remote Sensing, 17(18), 3179. https://doi.org/10.3390/rs17183179

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