Shallow Water Bathymetry Mapping Using Sentinel-2 and Machine Learning in Raja Ampat Coastal Waters
DOI:
https://doi.org/10.59261/jequi.v7i2.252Keywords:
satellite-derived bathymetry, sentinel-2 imagery, random fores, shallow water bathymetry, raja ampat watersAbstract
Background: Satellite-Derived Bathymetry (SDB) has emerged as a cost-effective alternative for shallow-water depth mapping, particularly in remote and data-scarce marine regions. Raja Ampat, Southwest Papua, presents a challenging environment for bathymetric mapping due to its complex seafloor morphology and high water clarity, requiring robust analytical approaches to improve depth estimation accuracy.
Objective: This study aims to evaluate the effectiveness of Sentinel-2–based SDB by comparing empirical regression models and a Random Forest machine learning approach for shallow-water bathymetry mapping in the waters of Raja Ampat.
Methods: The research integrates Sentinel-2 multispectral imagery with three empirical regression methods—green band power regression, blue band power regression, and the Stumpf logarithmic ratio method—and a Random Forest algorithm. Field bathymetric data obtained from fishfinder measurements were used for model calibration and validation. All processing and analysis were conducted using the Google Earth Engine (GEE) platform.
Results: The empirical regression analysis shows that the green band model (Band 3) achieved the highest accuracy (R² = 0.7097; RMSE = 1.80–14.12 m across depth classes), followed by the blue band model (R² = 0.6194) and the Stumpf method (R² = 0.5693). The Random Forest model outperformed all empirical approaches by effectively capturing non-linear relationships between spectral reflectance and water depth, particularly over heterogeneous seabed substrates. The green band model performed optimally at depths of 0–20 m, while the Stumpf method demonstrated greater stability beyond 20 m.
Conclusion: The integration of Sentinel-2 imagery with machine learning provides an accurate, scalable, and cost-efficient solution for shallow-water bathymetric mapping in tropical archipelagic environments. This approach supports marine conservation planning and coastal resource management in Raja Ampat and similar regions.
Downloads
References
Al Najar, M., Benshila, R., El Bennioui, Y., Thoumyre, G., Almar, R., Bergsma, EWj, Delvit, J.-M., Wilson, D.G. (2022). Coastal bathymetry Estimation from Sentinel-2 Satellite Imagery: Comparing deep Learning and Physics-Based Approaches. Remote Sensing, 12(5), 1196. https://doi.org/10339/rs14051196
Asaad, I., Lundquist, C. J., Erdmann, M. V., & Costello, M. J. (2018). Delineating priority areas for marine biodiversity conservation in the Coral Triangle. Biological Conservation, 222, 198-211
Bekiashev, K. A., & Serebriakov, V. V. (1981). International Hydrographic Organization (IHO). In International Marine Organizations: Essays on Structure and Activities (pp. 478-485). Dordrecht: Springer Netherlands.
Bouguer, P. (1729). Essai d'optique sur la gradation de la lumière . Claude Jombert, Paris.
Caballero, I., & Stumpf, R.P. (2020) Towards Routine Mapping of Shallow Bathymetry in Environments with Variable Turbidity: Contribution of the Sentinel-2A/B Satellites Mission. Remote Sensing , 12 Vol. (3). 451. https://doi.org/10.339/rs12030451
Cahyono, B. K., & Sihombing, J. B. (2025). Evaluation of Water Depth Extraction Using Empirical Method with Sentinel 2 Image on Google Earth Engine in South Sea Island of Bangka Belitung. Engineering Headway, 27, 595-609.
Casal. T., Monteys, X., Sweeney, C., Gaulton, R. (2019). Understanding Satellite-drive Bathymetry Using Sentiel-2 Imagery and Spatial Prediction Models. Journal Of Applied Remote Sensing, 113(4), 046501. https://doi.org/10.1080/15481603.2019.1685198
Fan, B., Zhang, C., Chi, J., Liang, Y., Bao, X., Cong, Y., ... & Li, G. Y. (2022). The molecular mechanism of retina light injury focusing on damage from short wavelength light. Oxidative medicine and cellular longevity, 2022(1), 8482149.
Ghilani, C.D. (2010). Adjustment Computations: Spatial Data Analysis . 5th Edition. John Wiley & Sons, Hoboken, New Jersey.
Hewageegana, S., & Canestrelli, A. (2022). Satellite-drive Bathymetry Using Machine earning and Optimal Sentinel-2 Imagery in South-West Florida Coastal Water. Journal of Applied Remote Sensing, 16(3), 034522. https://doi.org/10.1080/15481603.2022.2100597
Huang, Y., & Coelho, V. R. (2017). Sustainability performance assessment focusing on coral reef protection by the tourism industry in the Coral Triangle region. Tourism Management, 59, 510-527.
Kurniawan, A., Khakhim, N., & Wicaksono, P. (2024). Seasonal Variation Influence to Water Image Properties to Retrieve Nearshore Bathymetry Based on Cloud Machine Learning Approach. International Journal of Geoinformatics, 20(7), 77-92.
Libiseller, C., Richter, R., & Schroeder M. (2022). Satellite-driven Bathymetry using Sentinel-2: A Comparative Study of Empirical Models in Coastal Water. “ Remoting Sensing, 12(9), 2085. http://doi.org/10.3390/rs.14092085
Lyzenga, D. R. (1978). Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics , 17(3), 379–383. https://doi.org/10.1364/AO.17.000379
Mather, P. M. (2004). Computer Processing of Remotely-Sensed Images: An Introduction . 3rd Edition. John Wiley & Sons, Chichester.
Misra, A., Vojinovic, Z., Ramakrishnan, B., Luijendijk, A., & Ranasinghe, R. (2018). Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery. International Journal of Remote Sensing, 39(13), 4431-4450.
Stumpf, R.P., Holderied, K., & Sinclair, M. (2003). Determination of water depth with high‐resolution satellite imagery over variable bottom types. Limnology and Oceanography , 48(1), 547–556. https://doi.org/10.4319/lo.2003.48.1_part_2.0547
Traganos, D., Poursanidis, D., Ahrens, B., Reinartz, P., & Chrysoulakis, N. (2018). Estimating Satellite-Derived Bathymetry (SDB) with the Google Earth Engine and Sentinel-2. Remote Sensing, 10(6), 859. https://doi.org/103390/rs10060859
White, A. T., Aliño, P. M., Cros, A., Fatan, N. A., Green, A. L., Teoh, S. J., ... & Wen, W. (2014). Marine protected areas in the Coral Triangle: progress, issues, and options. Coastal Management, 42(2), 87-106.
Wilson, KL, Wong, MC, & Devred, E. (2022) Comparing Sentinel-2 and WorldView-3 Imagery for Coastal Bottom Habitat Mapping in Atlantic Canada. Remote Sensing, 14(5), 1254. https://doi.org/10.3390/rs14051254
Yang, H., Guo, H., Dai, W., Nie, B., Qiao, B., & Zhu, L. (2022). Bathymetric mapping and estimation of water storage in a shallow lake using a remote sensing inversion method based on machine learning. International Journal of Digital Earth, 15(1), 789-812.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Muhammad Ichsan, Septa Erik Prabawa, Reynalda Anindia Mawarni
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA). that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
