Total suspended solid distribution mapping using sentinel-2A imagery in Ketapang Waters, South Lampung

Ahmad Al Fadel; Ellis Nurjuliasti Ningsih; Tengku Zia Ulqodry

doi:10.36706/jlso.14.1.2025.711

Authors

Ahmad Al Fadel Graduate Program of Environmental Sciences, Graduate School, Universitas Sriwijaya, Jl. Padang Selasa No. 524, Bukit Besar, Palembang 30139, South Sumatra, Indonesia
Ellis Nurjuliasti Ningsih Department of Marine Science, Faculty of Mathematics and Natural Sciences, Universitas Sriwijaya, Jl. Raya Palembang-Prabumulih Km. 32, Ogan Ilir 30862, South Sumatera, Indonesia
Tengku Zia Ulqodry Department of Marine Science, Faculty of Mathematics and Natural Sciences, Universitas Sriwijaya, Jl. Raya Palembang-Prabumulih Km. 32, Ogan Ilir 30862, South Sumatera, Indonesia

DOI:

https://doi.org/10.36706/jlso.14.1.2025.711

Keywords:

coastal water, Ketapang Waters, remote sensing, sentinel-2, total suspended solid

Abstract

The distribution of total suspended solids (TSS) in coastal waters significantly affects water turbidity and light penetration, which affects aquatic ecosystems. The research aimed to compare the accuracy of the Laili (2015) and Jaelani (2016) algorithms using Sentinel-2A imagery to map the distribution of TSS in Ketapang Waters, South Lampung. Polynomial regression analysis and validation tests using R² and RMSE were performed to assess accuracy. The results showed that the Laili algorithm performed better, achieving an R² value of 0.9723 and a lower RMSE of 0.639, with TSS concentrations ranging from 17.26 to 22.90 mg/L. The derived third-order polynomial regression model y = -0.0228x³ + 1.3401x² - 25.16x + 170.08 effectively predicted TSS concentrations. Spatial distribution analysis showed higher TSS levels near the coastline, likely due to sediment input from human activities and natural hydrodynamic processes, which gradually decreased towards the offshore area. These findings demonstrate the potential of the Laili algorithm for remote sensing-based water quality monitoring in dynamic coastal environments. Future research should include seasonal variations and explore the integration of multiple algorithms to improve the accuracy of TSS estimation and better understand temporal fluctuations in coastal sediment dynamics.

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