Satellite-Derived Relative Bathymetry Mapping of Kopa Dam Using Sentinel-2 Level-2A Imagery and GIS Techniques
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Abstract
Satellite-derived bathymetry (SDB) provides a remote-sensing approach for characterizing spatial variations in water depth-related information without requiring complete conventional hydrographic coverage. This study developed a relative satellite-derived bathymetry map of Kopa Dam, Tafa Local Government Area, Niger State, Nigeria, using Sentinel-2 Level-2A imagery and ArcGIS 10.8. Sentinel-2 Bands 2 and 3, representing the blue and green portions of the visible spectrum, were used to derive a logarithmic spectral-ratio called Relative Bathymetry Index (RBI). The RBI is an uncalibrated, unitless spectral index representing relative bathymetric variation rather than measured water depth in metres. A water mask was developed and applied to restrict the RBI to the water body, after which descriptive statistics were calculated. The RBI was subsequently classified into four relative bathymetric classes and processed using a Majority Filter to improve spatial coherence. The resulting RBI ranged from 0.386808 to 1.000235, with a mean of 0.777053, standard deviation of 0.058869, and range of 0.613426. The final classification contained 37,845 valid water pixels, equivalent to 378.45 ha at 10-m spatial resolution. Moderate depth class was dominant, covering 249.60 ha (65.95%), followed by deep zone class with 127.29 ha (33.63%), while very shallow and shallow class accounted for 0.02 ha (0.01%) and 1.54 ha (0.41%), respectively. The study demonstrates the usefulness of Sentinel-2 multispectral imagery and GIS-based spectral-ratio processing for preliminary spatial characterization of relative bathymetric variation in an inland water body. Future studies should incorporate field-based bathymetric calibration to enable conversion of the RBI into quantitative water-depth estimates.
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Copyright (c) 2026 Musa Abdulmumin, Abubakar Sunusi, Dauda Waziri Abdullahi, Abubakar Zalkiflu Idris (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Ashphaq, M., Srivastava, P. K., & Mitra, D. (2021). Review of near-shore satellite derived bathymetry: Classification and account of five decades of coastal bathymetry research. Journal of Ocean Engineering and Science, 6(4), 340–359. https://doi.org/10.1016/j.joes.2021.02.006 DOI: https://doi.org/10.1016/j.joes.2021.02.006
Bernardis, M., Nardini, R., Apicella, L., Demarte, M., Guideri, M., Federici, B., Quarati, A., & De Martino, M. (2023). Use of ICESat-2 and Sentinel-2 open data for the derivation of bathymetry in shallow waters: Case studies in Sardinia and in the Venice Lagoon. Remote Sensing, 15(11), 2944. https://doi.org/10.3390/rs15112944 DOI: https://doi.org/10.3390/rs15112944
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., & Bargellini, P. (2012). Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120, 25–36. https://doi.org/10.1016/j.rse.2011.11.026 DOI: https://doi.org/10.1016/j.rse.2011.11.026
European Space Agency. (2015). Sentinel-2 user handbook. European Space Agency.
Figliomeni, F. G., & Parente, C. (2024). Methods for satellite derived bathymetry from Sentinel-2 images: A comparison. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-4/W9-2024, 165–173. https://doi.org/10.5194/isprs-archives-XLVIII-4-W9-2024-165-2024 DOI: https://doi.org/10.5194/isprs-archives-XLVIII-4-W9-2024-165-2024
Gafoor, F. A., Al-Shehhi, M. R., Cho, C.-S., & Ghedira, H. (2022). Gradient boosting and linear regression for estimating coastal bathymetry based on Sentinel-2 images. Remote Sensing, 14(19), 5037. https://doi.org/10.3390/rs14195037 DOI: https://doi.org/10.3390/rs14195037
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 DOI: https://doi.org/10.1364/AO.17.000379
Ma, Y., Xu, N., Liu, Z., Yang, B., Yang, F., Wang, X.-H., & Li, S. (2020). Satellite-derived bathymetry using the ICESat-2 lidar and Sentinel-2 imagery datasets. Remote Sensing of Environment, 250, 112047. https://doi.org/10.1016/j.rse.2020.112047 DOI: https://doi.org/10.1016/j.rse.2020.112047
McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714 DOI: https://doi.org/10.1080/01431169608948714
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 DOI: https://doi.org/10.14710/jkt.v26i1.16050
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of the Third Earth Resources Technology Satellite-1 Symposium (NASA SP-351, pp. 309–317). NASA.
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, Part 2), 547–556. https://doi.org/10.4319/lo.2003.48.1_part_2.0547 DOI: https://doi.org/10.4319/lo.2003.48.1_part_2.0547
Vrdoljak, L., & Kilić Pamuković, J. (2022). Assessment of atmospheric correction processors and spectral bands for satellite-derived bathymetry using Sentinel-2 data in the Middle Adriatic. Hydrology, 9(12), 215. https://doi.org/10.3390/hydrology9120215 DOI: https://doi.org/10.3390/hydrology9120215
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