Sea Surface Salinity (SSS) Prediction Using Landsat 8 OLI Image Data in The Bangka Strait Waters with Five Prediction Model Combinations

Khoirun Nisa; Gentio Harsono; Sukendra Martha; Dangan Waluyo

doi:10.52562/injoes.2025.1738

Authors

Khoirun Nisa Sensing Technology Study Program, Faculty of Engineering and Technology, Indonesian Defense University, Bogor 16810, Indonesia
Gentio Harsono Sensing Technology Study Program, Faculty of Engineering and Technology, Indonesian Defense University, Bogor 16810, Indonesia
Sukendra Martha Sensing Technology Study Program, Faculty of Engineering and Technology, Indonesian Defense University, Bogor 16810, Indonesia
Dangan Waluyo Sensing Technology Study Program, Faculty of Engineering and Technology, Indonesian Defense University, Bogor 16810, Indonesia

DOI:

https://doi.org/10.52562/injoes.2025.1738

Keywords:

Combination, Landsat 8 OLI image, Multiple Linear Regression, Rrs, Salinity

Abstract

Salinity is the most important parameter for controlling the biological components of ecosystems, seas, and estuaries, which also control the components that make up an ecosystem. Conventional water quality monitoring is considered inaccurate and inefficient in terms of energy and time. Therefore, research is needed to predict sea surface salinity as a type of water quality monitoring using remote sensing reflectance or Remote Sensing Reflectance (RRS) from Landsat imagery. The Landsat image data used is level 2 Surface Reflectance (SR), which is ready to use without additional processing by the user, whereas previous research required corrections to the image data to obtain Surface Reflectance image data. This study aims to determine the performance of the prediction model produced by using five combinations of Landsat image bands. The data used are Landsat 8 OLI image data (recording date 05 August 2024) downloaded from the USGS website and in situ salinity data in the Bangka Strait sea (09 March 2025), as many as 5 samples that can be used. The obtained data were processed using multiple linear regression analysis with Rrs as the independent variable and in situ salinity as the dependent variable. The salinity prediction model consisted of five band combinations. The analysis produced R² values for each model combination of 0.8166287408, 0.935603228, 0.820745745, 0.869209652, and 0.574027060. The RMSE validity tests for combination 1, combination 2, combination 3, combination 4, and combination 5 were 2.41327, 1.43012, 2.38602, 2.03811, and 3.67817. Then for the NMAE value, namely 10.10152205%, 5.32713015%, 9.58011308%, 8.8868031%, and 14.51012574%. The combination rankings that have the best prediction performance are combination 2, combination 4, combination 3, combination 1, and combination 5. So the best model in predicting seawater salinity is the combination of the 2 prediction models, with its constituent band components being band 1, band 2, and band 4.

Downloads

Download data is not yet available.

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

Abdul Wahid, A., & Arunbabu, E. (2022). Forecasting water quality using seasonal ARIMA model by integrating in-situ measurements and remote sensing techniques in Krishnagiri reservoir, India. Water Practice and Technology, 17(5), 1230–1252. https://doi.org/10.2166/wpt.2022.046

Almeida, R. A., Pereira, S. B., & Pinto, D. B. F. (2018). Calibration and validation of the SWAT hydrological model for the Mucuri river basin. Engenharia Agricola, 38(1), 55–63. https://doi.org/10.1590/1809-4430eng.agric.v38n1p55-63/2018

Andrade, C. (2019). The P value and statistical significance: Misunderstandings, explanations, challenges, and alternatives. Indian Journal of Psychological Medicine, 41(3), 210–215. https://doi.org/10.4103/IJPSYM.IJPSYM_193_19

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

Arafah, F., Taufik, M., & Jaelani, L. M. (2015). Analisis Parameter Kualitas Air Laut di Perairan Kabupaten Sumenep untuk Pembuatan Peta Sebaran Potensi Ikan Pelagis (Studi Kasus: Total Suspended Solid (TSS)). Seminar Nasional Aplikasi Teknologi Prasarana Wilayah, 21–30. http://dx.doi.org/10.13140/RG.2.1.4871.8801

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

Dimitriadou, S., & Nikolakopoulos, K. G. (2022). Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece. Hydrology, 9(7). https://doi.org/10.3390/hydrology9070124

Enggal, T. W., Bukhori, M., & Sudaryanti, D. (2019). Analisa Bauran Pemasaran Yang Mempengaruhi Keputusan Pembelian Baju di Beberapa Departement Store di Kota Malang. Jurnal Ilmiah Bisnis Dan Ekonomi Asia, 13(2), 61–70. https://doi.org/10.32812/jibeka.v13i2.116

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

Hafeez, S., Wong, M. S., Abbas, S., & Asim, M. (2022). Evaluating Landsat-8 and Sentinel-2 Data Consistency for High Spatiotemporal Inland and Coastal Water Quality Monitoring. Remote Sensing, 14(13). https://doi.org/10.3390/rs14133155

Hidayat, M. N., Wafdan, R., Ramli, M., Muchlisin, Z. A., & Rizal, S. (2023). Relationship between chlorophyll-a, sea surface temperature and sea surface salinity. Global Journal of Environmental Science and Management, 9(3), 389–402. https://doi.org/10.22035/gjesm.2023.03.03

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

Jauza, N. A., & Albina, M. (2025). Model dan Pendekatan Penelitian Kuantitatif?: Kajian Filosofis , Metodologis , dan Aplikatif. 104–111. https://doi.org/10.61104/qb.v2i1.280

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

Karch, J. (2020). Improving on adjusted R-squared. Collabra: Psychology, 6(1), 45. https://doi.org/10.1525/collabra.343

Lew, S., Gli?ska-Lewczuk, K., Burandt, P., Kulesza, K., Kobus, S., & Obolewski, K. (2022). Salinity as a Determinant Structuring Microbial Communities in Coastal Lakes. International Journal of Environmental Research and Public Health, 19(8). https://doi.org/10.3390/ijerph19084592

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

Li, P., Feng, Z., & Xiao, C. (2018). Acquisition probability differences in cloud coverage of the available Landsat observations over mainland Southeast Asia from 1986 to 2015. 8947. https://doi.org/10.1080/17538947.2017.1327619

Loewen, C. J. G., & Jackson, D. A. (2024). Salinization, warming, and loss of water clarity inhibit vertical mixing of small urban ponds. Limnology And Oceanography Letters, 9(2), 155–164. https://doi.org/10.1002/lol2.10367

Mamun, M., Hasan, M., & An, K. G. (2024). Advancing reservoirs water quality parameters estimation using Sentinel-2 and Landsat-8 satellite data with machine learning approaches. Ecological Informatics, 81(April), 102608. https://doi.org/10.1016/j.ecoinf.2024.102608

Mobley, C. D., Werdell, J., Franz, B., Ahmad, Z., & Bailey, S. (2016). Atmospheric Correction for Satellite Ocean Color Radiometry: A Tutorial and Documentation of the Algorithms Used by the NASA Ocean Biology Processing Group. http://www. NASA/TM–2016-217551. Document ID 20160011399

Mohan, S., Kumar, B., & Nejadhashemi, A. P. (2025). Integration of Machine Learning and Remote Sensing for Water Quality Monitoring and Prediction: A Review. Sustainability (Switzerland), 17(3). https://doi.org/10.3390/su17030998

Muff, S., Nilsen, E. B., O’Hara, R. B., & Nater, C. R. (2022). Rewriting results sections in the language of evidence. Trends in Ecology and Evolution, 37(3), 203–210. https://doi.org/10.1016/j.tree.2021.10.009

Muhsi, M., Sukojo, B. M., Taufik, M., Aji, P., & Jaelani, L. M. (2022). Estimation of Sea Surface Salinity Concentration from Landsat 8 OLI Data in The Strait of Madura, Indonesia. Forum Geografi, 36(2), 149–159. https://doi.org/10.23917/forgeo.v36i2.19941

Nafizah, N., Jaelani, L. M., & Winarso, G. (2016). Evaluasi Algoritma Wouthuyzen dan Son untuk Pendugaan Sea Surface Salinity (SSS) (Studi Kasus: Perairan Utara Pamekasan). Jurnal Teknik ITS, 5(2), 176-180.

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), 61. 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. https://doi.org/10.14710/pilars.%25v.%25i.%25Y.%25p

Ohtani, K., & Tanizaki, H. (2004). Exact Distributions of R2 and Adjusted R2 in a Linear Regression Model with Multivariate t Error Terms. Journal of the Japan Statistical Society, 34(1), 101–109. https://doi.org/10.14490/jjss.34.101

Pasaribu, R. P., Djari, A. A., Rahman, A., & Handayani, R. (2024). Penentuan Karakteristik Salinitas Menggunakan Conductivity Temperature Depth (Ctd) Di Perairan Pulau Sumba. Jurnal Kelautan Nasional, 19(2), 129. https://doi.org/10.15578/jkn.v19i2.13283

Quynh, B. D., Ha, H., Truong, T. X., & Luu, C. (2024). Geoinformatics for Spatial-Infrastructure Development in Earth and Allied Sciences. In Proceedings of GIS-IDEAS 2023 (Vol. 411). Springer Nature. https://doi.org/10.1007/978-3-031-71000-1

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

Rusli, H. A. R., Berd, I., Hutasoit, L. M., & Tanjung, D. A. (2023). The Tests Of pH, Conductivity, Total Dissolved Solids, Salinity And Turbidity in the Batang Arau River Surrounding, Padang City-West Sumatra. BIOLINK (Jurnal Biologi Lingkungan Industri Kesehatan), 10(1), 25-34. https://doi.org/10.31289/biolink.v10i1.9642

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

Sood, R., & Zhu, K. (2024). Improving Water Quality Time-Series Prediction in Hong Kong using Sentinel-2 MSI Data and Google Earth Engine Cloud Computing. arXiv preprint arXiv:2408.14010. http://dx.doi.org/10.48550/arXiv.2408.14010

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

Tran, T. V., Tran, D. X., Pham, H. V., Truong, T. V., Trinh, H. P., Nguyen, D. Q., Nguyen, B. A., & Nguyen, H. C. (2020). Exploring spatial relationship between electrical conductivity and spectral salinity indices in the Mekong delta. Journal of Environmental Science and Management, 23(1), 39–49. https://doi.org/10.47125/jesam/2020_1/05

U.S. Geological Survey. (2024). Landsat-8 imagery of Bangka Strait area [satellite image data]. EarthExplorer. Retrieved on February 27, 2025, from https://earthexplorer.usgs.gov

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

Zaka, A. R., & Sutopo, S. (2017). Analisis Faktor-Faktor Yang Mempengaruhi Kepuasan Pelanggan Pada LBB Antologi Semarang. Diponegoro Journal of Management, 6(3), 33-45.

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