Bangka strait salinity prediction using landsat 9 oli image data
DOI:
https://doi.org/10.35335/mandiri.v14i1.447Keywords:
Bangka Strait, Landsat 9 OLI image, Multiple Linear Regression, Rrs, SalinityAbstract
Salinity is an important parameter because it affects the environment, such as water quality, growth, and development of aquatic vegetation and various animal species. Conventional water quality monitoring is still ineffective, so it is necessary to utilize technology in monitoring water quality, including water Salinity is an important parameter because it affects the environment, such as water quality, growth, and development of aquatic vegetation and various animal species. Conventional water quality monitoring is still ineffective, so it is necessary to utilize technology in monitoring water quality, including water salinity. Utilization of remote sensing is often used to study salinity both on a small scale and a global scale. Therefore, the author conducted a study to predict salinity in the Bangka Strait using the RRS (Remote Sensing Reflectance) method. The data used are Landsat 9 OLI image data downloaded from the USGS website and in situ salinity data in the Bangka Strait sea. The Landsat 9 OLI image data used is level 2 Surface Reflectance (SR), which is ready for analysis without additional processing by the user. The data obtained were processed using multiple linear regression analysis with Rrs as the independent variable and in situ salinity as the dependent variable. Salinity prediction models are divided into three groups based on the image recording date, namely Rrs 1 for the Landsat 9 OLI image recording on May 9, 2024, Rrs 2 for July 28, 2024, and Rrs 3 for the image recording on September 28, 2023. Multiple linear regression analysis produces R² values for each model of 0.81662874, 0.8170285, and 0.8136894. These R² results indicate that the three models, Rrs 1, Rrs 2, and Rrs 3, are included in the very good criteria in predicting salinity. To choose the best of the three models, by considering the results of the validity test. The NMAE validity test for Rrs 1, Rrs 2, and Rrs 3 is 10.10152, 10.37618, and 8.88680. Meanwhile, the RMSE values are 2.41327, 2.41064, and 2.43253. Therefore, it can be determined that the Rrs 2 model is the best in predicting salinity because it has the highest R² value, namely 0.8170285, and the smallest RMSE, namely 2.41064.
References
Abdelmalik, K. W. (2018). Role of statistical remote sensing for Inland water quality parameters prediction. Egyptian Journal of Remote Sensing and Space Science, 21(2), 193–200. https://doi.org/10.1016/j.ejrs.2016.12.002
Alatawi, A. S. (2022). A Testbed for Investigating the Effect of Salinity and Turbidity in the Red Sea on White-LED-Based Underwater Wireless Communication. Applied Sciences (Switzerland), 12(18). https://doi.org/10.3390/app12189266
Ang, Y., Shafri, H. Z. M., Lee, Y. P., Bakar, S. A., Abidin, H., Hashim, S. J., Samad, M. N., Che’ya, N. N., Hassan, M. R., Lim, H. S., Abdullah, R., Yusup, Y., Muhammad, S. A., Yin, T. S., & Gibril, M. B. A. (2025). Block-scale Oil Palm Yield Prediction Using Machine Learning Approaches Based on Landsat and MODIS Satellite Data. Journal of Advanced Research in Applied Sciences and Engineering Technology, 45(1), 90–107. https://doi.org/10.37934/araset.45.1.90107
Ansari, M., & Akhoondzadeh, M. (2020). Mapping water salinity using Landsat-8 OLI satellite images (Case study: Karun basin located in Iran). Advances in Space Research, 65(5), 1490–1502. https://doi.org/10.1016/j.asr.2019.12.007
Boutin, J., Reul, N., Koehler, J., Martin, A., Catany, R., Guimbard, S., Rouffi, F., Vergely, J. L., Arias, M., Chakroun, M., Corato, G., Estella-Perez, V., Hasson, A., Josey, S., Khvorostyanov, D., Kolodziejczyk, N., Mignot, J., Olivier, L., Reverdin, G., … Mecklenburg, S. (2021). Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies. Journal of Geophysical Research: Oceans, 126(11), 1–28. https://doi.org/10.1029/2021JC017676
Ciancia, E., Campanelli, A., Colonna, R., Palombo, A., Pascucci, S., Pignatti, S., & Pergola, N. (2023). Improving Colored Dissolved Organic Matter (CDOM) Retrievals by Sentinel2-MSI Data through a Total Suspended Matter (TSM)-Driven Classification: The Case of Pertusillo Lake (Southern Italy). Remote Sensing, 15(24). https://doi.org/10.3390/rs15245718
Dewangan, L. (2023). The Exact Measurement of Pi. International Journal for Research in Applied Science and Engineering Technology, 11(8), 2217–2233. https://doi.org/10.22214/ijraset.2023.55555
Fitri, N. L., Pangaribuan, D., & Yuniati, T. (2024). Pengaruh Free Cash Flow, Investment Opportunity Set, Dan Struktur Modal Terhadap Kebijakan Dividen Pada Perusahaan Food and Beverage. SENTRI: Jurnal Riset Ilmiah, 3(2), 740–760. https://doi.org/10.55681/sentri.v3i2.2324
Fkih, F. (2022). Similarity measures for Collaborative Filtering-based Recommender Systems: Review and experimental comparison. Journal of King Saud University - Computer and Information Sciences, 34(9), 7645–7669. https://doi.org/10.1016/j.jksuci.2021.09.014
Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. Geoscientific Model Development, 15(14), 5481–5487. https://doi.org/10.5194/gmd-15-5481-2022
Hoffman, S. (2021). Estimation of prediction error in regression air quality models. Energies, 14(21). https://doi.org/10.3390/en14217387
Jin, H., Fang, S., & Chen, C. (2023). Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series. Remote Sensing, 15(20), 1–21. https://doi.org/10.3390/rs15204986
Keer, M., Lohiya, H., & Chouhan, S. (2023). Goodness of Fit for Linear Regression using R squared and Adjusted R-Squared. International Journal of Research Publication and Reviews Journal Homepage: Www.Ijrpr.Com, 4(3), 2431–2439. www.ijrpr.com
Kim, H.-Y. (2019). Statistical notes for clinical researchers: simple linear regression 3 – residual analysis. Restorative Dentistry & Endodontics, 44(1), 1–8. https://doi.org/10.5395/rde.2019.44.e11
Li, J. (2017). Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what? PLoS ONE, 12(8), 1–16. https://doi.org/10.1371/journal.pone.0183250
Meng, H., Zhang, J., & Zheng, Z. (2022). Retrieving Inland Reservoir Water Quality Parameters Using Landsat 8-9 OLI and Sentinel-2 MSI Sensors with Empirical Multivariate Regression. International Journal of Environmental Research and Public Health, 19(13). https://doi.org/10.3390/ijerph19137725
Nafizah, Jaelani, L. M., & Winarso, G. (2016). Garam 3. Jurnal Tehnik Its, 5(September).
Novoa, S., Doxaran, D., Ody, A., Vanhellemont, Q., Lafon, V., Lubac, B., & Gernez, P. (2017). Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels coastal waters. Remote Sensing, 9(1). https://doi.org/10.3390/rs9010061
Octaviana, A., Prasetyo, Y., & Amarrohman, F. J. (2020). Analisis perubahan nilai total suspended solid tahun 2016 Dan 2019 menggunakan citra sentinel 2a (Studi Kasus : Banjir Kanal Timur, Semarang). Jurnal Geodesi Undip, 9(2), 167–176.
Rahma, A. A., Adrianto, D., & Malik, K. (2024). Pemodelan Numerik Arus Pasang Surut 2D Menggunakan Software Mike 21 (Studi Kasus Selat Bangka). Jurnal Hidrografi Indonesia, 4(2), 87–94. https://doi.org/10.62703/jhi.v4i2.36
Rossi, V. M., Longhitano, S. G., Olariu, C., & Chiocci, F. L. (2023). Straits and seaways: controls, processes and implications in modern and ancient systems. Geological Society Special Publication, 523(1), 1–15. https://doi.org/10.1144/SP523-2022-271
Sahbeni, G. (2021). Soil salinity mapping using Landsat 8 OLI data and regression modeling in the Great Hungarian Plain. SN Applied Sciences, 3(5), 1–13. https://doi.org/10.1007/s42452-021-04587-4
Surbakti, H., Nurjaya, I. W., Bengen, D. G., & Prartono, T. (2022). Kontribusi Massa Air Tawar dari Estuari Banyuasin ke Perairan Selat Bangka pada Musim Peralihan II. Positron, 12(1), 29. https://doi.org/10.26418/positron.v12i1.53035
Tuc, E., Akbas, S. O., & Babagiray, G. (2025). Reliability and Validity Analysis of Correlations on Strength and Consolidation Parameters for Ankara Clay and Proposal for a New Correlation. Arabian Journal for Science and Engineering, 50(11), 8107–8126. https://doi.org/10.1007/s13369-024-09181-5
Turner, J. S., Friedrichs, C. T., & Friedrichs, M. A. M. (2021). Long-Term Trends in Chesapeake Bay Remote Sensing Reflectance: Implications for Water Clarity. Journal of Geophysical Research: Oceans, 126(12). https://doi.org/10.1029/2021JC017959
Wulandari, S. A., Sucipto, A., Rosyady, A. F., Ardana, M. D. R., Cahyono, O. D. P., & Khomarudin, A. N. (2024). Rancang Bangun Sistem Monitoring Kualitas Air Untuk Mendeteksi Keadaan Tidak Normal atau Penyakit Pada Tambak Ikan Mujaer Menggunakan Fuzzy Logic Mamdani Berbasis Mobile. Technologica, 3(1), 42–54. https://doi.org/10.55043/technologica.v3i1.153
Yan, L. (2024). Ocean salinity. Nature Climate Change, 14(4), 309. https://doi.org/10.1038/s41558-024-01987-3
Yang, H., Kong, J., Hu, H., Du, Y., Gao, M., & Chen, F. (2022). A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges. Remote Sensing, 14(8). https://doi.org/10.3390/rs14081770
Yanny. (2024). Pengukuran Kualitas Air Sumur (pH, TDS, Salinitas) di Desa Matsa Halmahera Utara. Jurnal Pengabdian Kepada Masyarakat, 1, 20–26.
Yoshii, K., Takayanagi, H., Miyajima, T., Reuning, L., Yamamoto, K., & Iryu, Y. (2025). Origin of interstitial water beneath the continental shelf offshore northwestern Australia: insights from hydrogen and oxygen isotope compositions. Progress in Earth and Planetary Science, 12(1). https://doi.org/10.1186/s40645-025-00722-6
Zhao, L., Li, Z., & Qu, L. (2022). Forecasting of Beijing PM2.5 with a hybrid ARIMA model based on integrated AIC and improved GS fixed-order methods and seasonal decomposition. Heliyon, 8(12), e12239. https://doi.org/10.1016/j.heliyon.2022.e12239
U.S. Geological Survey. (2024). Landsat-9 imagery of Bangka Strait area [satellite image data]. EarthExplorer. Retrieved on February 27, 2025, from https://earthexplorer.usgs.gov
U.S. Geological Survey. (2025). usgs. https://.usgs.gov. Retrieved on July 17, 2025.
U.S. Geological Survey. (n.d.). Landsat Collection 2 Level-2 science products. U.S. Department of the Interior. Retrieved July 29, 2025, from https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Khoirun Nisa, Gentio Harsono, Sukendra Martha, Dangan Waluyo
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
