Estimation of coastal waters turbidity using Sentinel-2 imagery

Abstract

Turbidity is an important water quality parameter and an indicator of water pollution. Marine remote sensing techniques has become a useful tool for mapping of turbidity at coastal waters. The advantage of using remote sensing for water quality analysis is its ability to obtain synoptic data from the entire study area to produce continuous surface data, can shows detailed spatial variability and periodically. The empirical modeling has been applied in this study to formulate the mathematical relationship between coastal waters turbidity with Sentinel-2 reflectance. This study integrated field survey and image processing. Measurement of in-situ turbidity was done in accordance with imagery acquisition time. Imageries used for this study were Sentinel-2 level-2A. The mathematical relationship was obtained by multiple linear regression model between turbidity and Sentinel-2 reflectance. A mathematical model has been developed in Sentinel-2 imagery and successfully applied to obtain surface turbidity. Estimated turbidity derived from Sentinel-2 imagery is very close to observed turbidity so the proposed model can be used to retrieve turbidity of coastal waters.

Keywords

• coastal waters
• turbidity
• Sentinel-2
• reflectance
• empirical modeling
• multiple linear regression