Bangka strait salinity prediction using landsat 9 oli image data - Khoirun Nisa; Harsono, Gentio; Martha, Sukendra; Waluyo, Dangan (Jurnal Mandiri IT, 2025)
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.