Machine learning approaches for satellite-derived bathymetry in tropical coastal waters: A comparative study from Nha Trang marine protected area, Vietnam

Hieu Nguyen Trinh Duc; Quang Nguyen Hao; Quang Tri Nguyen; Dien Tran Duc; Ha Vo Thi; Lien Nguyen Phuong; Nguyen Phuong Lan; Tran Nguyen Dang Huyen; Gorin Sergey; Dan Le Van; Giang Dang Ngoc Thi; Thang Ha Nam

doi:10.15625/2615-9783/24020

Author affiliations

Authors

Nguyen Trinh Duc Hieu Coastal Branch of Joint Vietnam-Russia Tropical Science and Technology Research Center, Khanh Hoa, Vietnam
Nguyen Hao Quang 1-Laboratory of Environmental Sciences and Climate Change, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam; 2-Faculty of Environment, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam
Tri Nguyen-Quang Biofluids and Biosystems Modelling Laboratory (BBML), Dalhousie University, DAC, Truro, NS B2N 5E3, Canada
Tran Duc Dien Coastal Branch of Joint Vietnam-Russia Tropical Science and Technology Research Center, Khanh Hoa, Vietnam
Vo Thi Ha Coastal Branch of Joint Vietnam-Russia Tropical Science and Technology Research Center, Khanh Hoa, Vietnam
Nguyen Phuong Lien Coastal Branch of Joint Vietnam-Russia Tropical Science and Technology Research Center, Khanh Hoa, Vietnam
Phuong Lan Nguyen Faculty of Information Technology and Semiconductor, Thai Binh Duong University, Khanh Hoa, Vietmam
Nguyen Dang Huyen Tran Department of Academic Affairs, Thai Binh Duong University, Khanh Hoa, Vietnam
Gorin Sergey Lomonosov Moscow State University Marine Research Center, Moscow, Russia
Le Van Dan University of Agriculture and Forestry, Hue University, Hue City, Vietnam
Dang Ngoc Thi Giang University of Agriculture and Forestry, Hue University, Hue City, Vietnam
Ha Nam Thang University of Agriculture and Forestry, Hue University, Hue City, Vietnam

DOI:

https://doi.org/10.15625/2615-9783/24020

Keywords:

PlanetScope, Nha Trang MPA, bathymetry, machine learning

Abstract

Bathymetry mapping plays a critical role in coastal zone management, marine conservation, and navigation safety. With the increasing availability of high-resolution satellite imagery, such as PlanetScope (3−5 m), remote sensing-based bathymetry retrieval offers a cost-effective and scalable alternative to traditional in-situ surveys. This study explores the capability of PlanetScope imagery to retrieve a wide range of bathymetry (-0.5 − ~ -40 m) in the southern area of the Nha Trang Marine Protected Area (MPA), Vietnam - an ecologically significant and dynamic coastal region. We conduct a comprehensive comparison between traditional approaches, including the Stumpf ratio model and Multiple Linear Regression (MLR), and a suite of advanced machine learning (ML) algorithms, including Random Forest (RF), Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGB), CatBoost (CB), and Gradient Boosting (GB). Among these, RF achieved the highest performance with an R² of 0.85, RMSE of 2.66 m, and MAE of 1.85 m, significantly outperforming the Stumpf model (R²= 0.29) and MLR (R²= 0.57). This study represents one of the most extensive model comparisons to date for satellite-derived bathymetry using PlanetScope data, offering a benchmark for future applications in tropical coastal environments. Results underscore the potential of machine learning to advance spatially detailed and accurate bathymetric mapping from space.

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