Comparative Accuracy of Satellite-Derived Bathymetry Using Random Forest, Multiple Linear Regression, and Van Hengel and Spitzer Algorithm
DOI:
https://doi.org/10.70561/geocelebes.v10i1.43582Keywords:
Multiple Linear Regression, Random Forest, Satellite-Derived Bathymetry, Van Hengel and Spitzer AlgorithmAbstract
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.
References
Ashphaq, M., Srivastava, P. K., & Mitra, D. (2024). Satellite ‐ Derived Bathymetry in Dynamic Coastal Geomorphological Environments Through Machine. Earth and Space Science, 11(7), 1–23. https://doi.org/10.1029/2024EA003554
BIG. (2021). Peraturan Badan Informasi Geospasial Republik Indonesia Nomor 18 Tahun 2021 Tentang Tata Cara Penyelenggaraan Informasi Geospasial. Badan Informasi Geospasial. https://peraturan.bpk.go.id/Details/217091/peraturan-big-no-18-tahun-2021
Bramante, J. F., Raju, D. K., & Sin, T. M. (2013). Multispectral derivation of bathymetry in Singapore’s shallow, turbid waters. International Journal of Remote Sensing, 34(6), 2070–2088. https://doi.org/10.1080/01431161.2012.734934
Caballero, I., & Stumpf, R. P. (2019). Preliminary Assessment of Turbidity and Chlorophyll Impact on Bathymetry Derived from Sentinel-2A and Sentinel-3A Satellites in South Florida. Remote Sensing, 11(6), 645. https://doi.org/10.3390/rs11060645
Caballero, I., & Stumpf, R. P. (2020a). Atmospheric correction for satellite-derived bathymetry in the Caribbean waters : from a single image to multi-temporal approaches using Sentinel-2A/B. Optics Express, 28(8), 11742–11766. https://doi.org/10.1364/OE.390316
Caballero, I., & Stumpf, R. P. (2020b). Towards Routine Mapping of Shallow Bathymetry in Environments with Variable Turbidity : Contribution of Sentinel-2A/B Satellites Mission. Remote Sensing, 12(3), 451. https://doi.org/10.3390/rs12030451
Cahalane, C., Magee, A., Monteys, X., Casal, G., Hanafin, J., & Harris, P. (2019). A comparison of Landsat 8, RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry. Remote Sensing of Environment, 233, 111414. https://doi.org/10.1016/j.rse.2019.111414
Chybicki, A., Sosnowski, P., Kulawiak, M., Bieliński, T., Korlub, W., Łubniewski, Z., Kempa, M., & Parzuchowski, J. (2023). Study of various machine learning approaches for Sentinel-2 derived bathymetry. PLoS ONE, 18(9), e0291595. https://doi.org/10.1371/journal.pone.0291595
Dewi, R. S., & Rizaldy, A. (2021). Accuracy Assessment of Satellite Derived Bathymetry Model for Depth Extraction in Sorong Shallow Water Area Accuracy Assessment of Satellite Derived Bathymetry Model for Depth Extraction in Sorong Shallow Water Area. IOP Conference Series: Earth and Environmental Science, 925, 012053. https://doi.org/10.1088/1755-1315/925/1/012053
Dewi, R. S., Rizaldy, A., Hartanto, P., & Suprajaka, S. (2021). Assesing The Accuracy of Shallow Water Depth Estimation by Using Multispectral Satellite Images. Geographia Technica, 16(Special Issue), 180–197. http://dx.doi.org/10.21163/GT_2021.163.14
Evagorou, E., Mettas, C., Agapiou, A., Themistocleous, K., & Hadjimitsis, D. (2019). Bathymetric maps from multi-temporal analysis of Sentinel-2 data: the case study of Limassol, Cyprus. Advances in Geosciences, 45, 397–407, https://doi.org/10.5194/adgeo-45-397-2019
Fang, S., Wu, Z., Wu, S., Chen, Z., Shen, W., & Mao, Z. (2024). Enhancing Water depth inversion accuracy in turbid coastal environments using random forest and coordinate attention mechanisms. Frontiers in Marine Science, 11, 1471695. https://doi.org/10.3389/fmars.2024.1471695
Gao, J. (2009). Bathymetric mapping by means of remote sensing: Methods, accuracy and limitations. Progress in Physical Geography, 33(1), 103–116. https://doi.org/10.1177/0309133309105657
Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. Geoscientific Model Development, 15, 5481–5487. https://doi.org/10.5194/gmd-15-5481-2022
Hu, Q., Li, J., & Cheng, L. (2025). Satellite-derived bathymetry based on iterative fusion of active lidar photons and passive pseudo- photons. International Journal of Digital Earth, 18(1), 2543568. https://doi.org/10.1080/17538947.2025.2543568
IHO. (2020). International Hydrographic Organization Standards for Hydrographic Surveys. 377.
IHO. (2024). International Hydrographic Organization Guidance to Satellite-Derived Bathymetry © Copyright International Hydrographic Organization 2024. 377.
Kim, M., Danielson, J., Storlazzi, C., & Park, S. (2024). Physics-Based Satellite-Derived Bathymetry (SDB) Using Landsat OLI Images. Remote Sensing, 16(5), 843. https://doi.org/10.3390/rs16050843
Kwon, J-Y., Shin, H-K., Kim, D-H., Lee, H-G., Bouk, J-K., Kim, J-H., & Kim, T-H. (2024). Estimation of shallow bathymetry using Sentinel-2 satellite data and random forest machine learning : a case study for Cheonsuman, Hallim, and Samcheok Coastal Seas. Journal of Applied Remote Sensing, 18(1), 014522. https://doi.org/10.1117/1.JRS.18.014522
Lee, S-J., Kim, H-S., Yun, H-S., & Lee, S-H. (2025). Satellite-Derived Bathymetry Using Sentinel-2 and Airborne Hyperspectral Data : A Deep Learning Approach with Adaptive Interpolation. Remote Sensing, 17(15), 2594. https://doi.org/10.3390/rs17152594
Liu, Z., Liu, H., Ma, Y., Ma, X., Yang, J., Jiang, Y., & Li, S. (2024). Exploring the Most Effective Information for Satellite-Derived Bathymetry Models in Different Water Qualities. Remote Sensing, 16(13), 2371. https://doi.org/10.3390/rs16132371
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
Mabula, M. J., Kisanga, D., & Pamba, S. (2023). Application of machine learning algorithms and Sentinel-2 satellite for improved bathymetry retrieval in Lake Victoria, Tanzania. Egyptian Journal of Remote Sensing and Space Science, 26(3), 619–627. https://doi.org/10.1016/j.ejrs.2023.07.003
Manessa, M. D. M., Kanno, A., Sekine, M., Haidar, M., Yamamoto, K., Imai, T., & Higuchi, T. (2016). Satellite-Derived Bathymetry Using Random Forest Algorithm and Worldview-2 Imagery. Geoplanning: Journal of Geomatics and Planning, 3(2), 117–126. https://doi.org/10.14710/geoplanning.3.2.117-126
Mudiyanselage, S. S. J. D., Wilkinson, B., & Lecours, V. (2022). Satellite-derived bathymetry using machine learning and optimal Sentinel-2 imagery in South- West Florida coastal waters. GIScience & Remote Sensing, 59(1), 1143–1158. https://doi.org/10.1080/15481603.2022.2100597
Munawaroh, M., Wicaksono, P., Farda, N. M., Lumban-Gaol, Y., Khakhim, N., & Kamal, M. (2024). Performance test of clean-coastal-water composite sentinel 2A image for shallow water bathymetry mapping. Remote Sensing Applications: Society and Environment, 35, 101212. https://doi.org/10.1016/j.rsase.2024.101212
Nisa, S. Q., Karang, I. W. G. A., Putra, I. D. N. N., Setiawan, K. T., & Aziz, K. (2023). Perbandingan Akurasi Metode Empiris untuk Pemetaan Batimetri Perairan Benoa, Bali, Menggunakan Citra Satelit SPOT. Journal of Marine Research and Technology, 6(1), 60. https://doi.org/10.24843/jmrt.2023.v06.i01.p09
Nugraha, A. Y., Prayudha, B., Ibrahim, A. L., & Riyadi, N. (2017). Pemetaan Batimetri di Perairan Dangkal menggunakan Data Penginderaan Jauh Spot-7 (Studi Kasus Lembar-Lombok). Jurnal Chart Datum, 3(2), 61–80. https://doi.org/10.37875/chartdatum.v3i2.120
Pahlevan, N., Sarkar, S., Franz, B. A., Balasubramanian, S. V, & He, J. (2017). Remote Sensing of Environment Sentinel-2 MultiSpectral Instrument (MSI) data processing for aquatic science applications: Demonstrations and validations. Remote Sensing of Environment, 201, 47–56. https://doi.org/10.1016/j.rse.2017.08.033
Prasetya, M. I., Siregar, V. P., & Agus, S. B. (2023). Comparison of Satellite-Derived Bathymetry Algorithm Accuracy Using Sentinel-2 Multispectral Satellite Image. Jurnal Kelautan Tropis, 26(1), 113–125. https://doi.org/10.14710/jkt.v26i1.16050
Purkis, S. J. (2019). Remote Sensing Coral Reefs. In Encyclopedia of Ocean Sciences, Third Edition: Volume 1-5 (3rd ed., Vols. 1–5). Elsevier Inc. https://doi.org/10.1016/B978-0-12-409548-9.10813-9
Pushparaj, J., & Hegde, A. V. (2017). Estimation of bathymetry along the coast of Mangaluru using Landsat-8 imagery. Journal of Ocean and Climate: Science, Technology and Impacts, 8(2), 71–83. https://doi.org/10.1177/1759313116679672
Quang, N. H., Banno, M., & Ha, N-T. (2025). Satellite derived bathymetry using empirical and machine learning approaches: a case study in the highly dynamic coastal water. Coastal Engineering Journal, 67(2), 232–251. https://doi.org/10.1080/21664250.2024.2445418
Richardson, G., Knudby, A., Wu, Y., & Ansari, M. (2025). A case study comparing approaches to mask satellite-derived bathymetry. Discover Geoscience, 3, 103. https://doi.org/10.1007/s44288-025-00219-1
Safi’i, A. N., & Dewi, R. S. (2020). Uji Akurasi Metode Berbasis Citra Satelit untuk Ekstraksi Data Batimetri. Teknik, 41(2), 142–151. https://doi.org/10.14710/teknik.v0i0.29516
Sagawa, T., Yamashita, Y., Okumura, T., & Yamanokuchi, T. (2019). Shallow Water Bathymetry Derived by Machine Learning and Multitemporal Satellite Images. International Geoscience and Remote Sensing Symposium (IGARSS), 8222–8225. https://doi.org/10.1109/IGARSS.2019.8899043
Sharr, M. B., Parrish, C. E., & Jung, J. (2024). International Journal of Applied Earth Observation and Geoinformation Automated classification of valid and invalid satellite derived bathymetry with random forest. International Journal of Applied Earth Observation and Geoinformation, 129, 103796. https://doi.org/10.1016/j.jag.2024.103796
Syaiful, S. N., Helmi, M., Widada, S., Widiaratih, R., Subardjo, P., & Suryoputro, A. A. D. (2019). Analisis Digital Citra Satelit Worldview-2 untuk Ekstraksi Kedalaman Perairan Laut di Sebagian Perairan Pulau Parang, Kepulauan Karimunjawa, Provinsi Jawa Tengah. Indonesian Journal of Oceanography, 1(1), 36–43. https://doi.org/10.14710/ijoce.v1i1.6262
Van Hengel, W., & Spitzer, D. (1991). Multi-temporal water depth mapping by means of landsat TM. International Journal of Remote Sensing, 12(4), 703–712. https://doi.org/10.1080/01431169108929687
Wei, C., Zhao, Q., Lu, Y., & Fu, D. (2021). Assessment of Empirical Algorithms for Shallow Water Bathymetry Using Multi-Spectral Imagery of Pearl River Delta Coast, China. Remote Sensing, 13(16), 3123. https://doi.org/10.3390/rs13163123
Xie, C., Chen, P., Zhang, Z., & Pan, D. (2023). Satellite-derived bathymetry combined with Sentinel-2 and ICESat-2 datasets using machine learning. Frontiers in Earth Science, 11, 1111817. https://doi.org/10.3389/feart.2023.1111817
Ye, M., Yang, C., Zhang, X., Li, S., Peng, X., Li, Y., & Chen, T. (2024). Shallow Water Bathymetry Inversion Based on Machine Learning Using ICESat-2 and Sentinel-2 Data. Remote Sensing, 16(23), 4603. https://doi.org/10.3390/rs16234603
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