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Comparative Accuracy of Satellite-Derived Bathymetry Using Random Forest, Multiple Linear Regression, and Van Hengel and Spitzer Algorithm
JURNAL GEOCELEBES
ISSN 2579-5546, 2579-5821
Published 2026-04-01
Publisher Departemen Geofisika, FMIPA - Universitas Hasanuddin, Makassar
Language en
Abstract
Bathymetry mapping using conventional methods faces limitations in shallow waters. With the development of remote sensing technology, satellite-derived bathymetry (SDB) emerges as an alternative by utilizing wavelengths that penetrate water and capture depth information. This study compares the performance of three empirical SDB methods: Random Forest (RF), Multiple Linear Regression (MLR), and the Van Hengel and Spitzer (VHS) algorithm. SPOT 6 ORTHO-level satellite imagery and depth data from single beam echosounder measurements were used to construct the depth models. Model accuracy was evaluated using root mean square error (RMSE), mean absolute error (MAE), and total vertical uncertainty (TVU). Results show that the RF method achieves the highest accuracy across most depth ranges (1–5 m, 5–10 m, and 10–15 m), while the VHS algorithm performs best at 0–1 m. At depths beyond 15 meters, MLR shows relatively better performance compared to other methods, although overall uncertainty remains high. Based on the coefficient of determination (R2), RF achieves the best result with a value of 0.610, followed by MLR with 0.462, and VHS with 0.313. These findings highlight the superior adaptability of the RF method in estimating bathymetry across varying depth zones using optical satellite imagery.
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Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/geocelebes/oai,
first harvested 3 Nov 2026,
last seen 3 Nov 2026.
Original metadata (oai_dc)
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<dc:title xml:lang="en">Comparative Accuracy of Satellite-Derived Bathymetry Using Random Forest, Multiple Linear Regression, and Van Hengel and Spitzer Algorithm</dc:title>
<dc:creator xml:lang="en">Apriliansyah, Fathurrahman</dc:creator>
<dc:creator xml:lang="en">Nuha, Muhammad Ulin</dc:creator>
<dc:creator xml:lang="en">Atmojo, Aulia Try</dc:creator>
<dc:creator xml:lang="en">Setiawan, Kuncoro Teguh</dc:creator>
<dc:creator xml:lang="en">Syafnur, Aswar</dc:creator>
<dc:subject xml:lang="en">Multiple Linear Regression</dc:subject>
<dc:subject xml:lang="en">Random Forest</dc:subject>
<dc:subject xml:lang="en">Satellite-Derived Bathymetry</dc:subject>
<dc:subject xml:lang="en">Van Hengel and Spitzer Algorithm</dc:subject>
<dc:description xml:lang="en">Bathymetry mapping using conventional methods faces limitations in shallow waters. With the development of remote sensing technology, satellite-derived bathymetry (SDB) emerges as an alternative by utilizing wavelengths that penetrate water and capture depth information. This study compares the performance of three empirical SDB methods: Random Forest (RF), Multiple Linear Regression (MLR), and the Van Hengel and Spitzer (VHS) algorithm. SPOT 6 ORTHO-level satellite imagery and depth data from single beam echosounder measurements were used to construct the depth models. Model accuracy was evaluated using root mean square error (RMSE), mean absolute error (MAE), and total vertical uncertainty (TVU). Results show that the RF method achieves the highest accuracy across most depth ranges (1–5 m, 5–10 m, and 10–15 m), while the VHS algorithm performs best at 0–1 m. At depths beyond 15 meters, MLR shows relatively better performance compared to other methods, although overall uncertainty remains high. Based on the coefficient of determination (R2), RF achieves the best result with a value of 0.610, followed by MLR with 0.462, and VHS with 0.313. These findings highlight the superior adaptability of the RF method in estimating bathymetry across varying depth zones using optical satellite imagery.</dc:description>
<dc:publisher xml:lang="en">Departemen Geofisika, FMIPA - Universitas Hasanuddin, Makassar</dc:publisher>
<dc:date>2026-04-01</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://journal.unhas.ac.id/index.php/geocelebes/article/view/43582</dc:identifier>
<dc:identifier>10.70561/geocelebes.v10i1.43582</dc:identifier>
<dc:source xml:lang="en">JURNAL GEOCELEBES; Vol. 10 No. 1: April 2026; 48–68</dc:source>
<dc:source>2579-5546</dc:source>
<dc:source>2579-5821</dc:source>
<dc:language>en</dc:language>
<dc:relation>https://journal.unhas.ac.id/index.php/geocelebes/article/view/43582/14256</dc:relation>
<dc:rights xml:lang="en">Copyright (c) 2026 JURNAL GEOCELEBES</dc:rights>
<dc:rights xml:lang="en">https://creativecommons.org/licenses/by-nc-sa/4.0</dc:rights>
</oai_dc:dc>
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APA 7
Apriliansyah, F., Nuha, M. U., Atmojo, A. T., Setiawan, K. T., & Syafnur, A. (2026). Comparative Accuracy of Satellite-Derived Bathymetry Using Random Forest, Multiple Linear Regression, and Van Hengel and Spitzer Algorithm. JURNAL GEOCELEBES, 10(1), 48–68. https://doi.org/10.70561/geocelebes.v10i1.43582
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IEEE
F. Apriliansyah, M. U. Nuha, A. T. Atmojo, K. T. Setiawan, and A. Syafnur, “Comparative Accuracy of Satellite-Derived Bathymetry Using Random Forest, Multiple Linear Regression, and Van Hengel and Spitzer Algorithm,” JURNAL GEOCELEBES, vol. 10, no. 1, pp. 48–68, 2026, doi: 10.70561/geocelebes.v10i1.43582.
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