Machine Learning-Constrained Semi-Analysis Model for Efficient Bathymetric Mapping in Data-Scarce Coastal Waters Article Swipe
Qifei Wang
,
Xianliang Zhang
,
Zhongqiang Wu
,
Chang Han
,
Longwei Zhang
,
Peng Xu
,
Zhihua Mao
,
Yueming Wang
,
Changxing Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17183179
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17183179
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.
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- Language
- en
- Landing Page
- https://doi.org/10.3390/rs17183179
- https://www.mdpi.com/2072-4292/17/18/3179/pdf?version=1757932191
- OA Status
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- Cited By
- 2
- References
- 45
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- https://openalex.org/W4414208718
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- Title
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Machine Learning-Constrained Semi-Analysis Model for Efficient Bathymetric Mapping in Data-Scarce Coastal WatersWork title
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articleOpenAlex work type
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enPrimary language
- Publication year
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2025Year of publication
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2025-09-13Full publication date if available
- Authors
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Qifei Wang, Xianliang Zhang, Zhongqiang Wu, Chang Han, Longwei Zhang, Peng Xu, Zhihua Mao, Yueming Wang, Changxing ZhangList of authors in order
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https://doi.org/10.3390/rs17183179Publisher landing page
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-4292/17/18/3179/pdf?version=1757932191Direct OA link when available
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2Total citation count in OpenAlex
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