Estimation of Chlorophyll-a Concentration from Landsat 9 Satellite Imagery for Surface Water Quality Monitoring in Urban Management
DOI:
https://doi.org/10.5281/zenodo.18186393Keywords:
Landsat 9, Chl-a, regression, Day River basin, environmental remote sensingAbstract
This study presents a method for estimating chlorophyll-a (Chl-a) concentrations in surface water of the Day River basin using Landsat 9 satellite imagery. The satellite data were acquired in June 2025 and processed using a spectral band ratio between the near-infrared (NIR) and red (RED) bands. A total of 25 surface water samples were collected simultaneously during field surveys to develop and calibrate a simple linear regression model linking the spectral index to measured Chl-a concentrations. The results indicate that the regression model achieved a high coefficient of determination (R² = 0.86), with low estimation error and strong statistical significance (p < 0.001). The spatial distribution map of Chl-a concentrations shows higher values concentrated in the middle and downstream sections of the basin, reflecting nutrient accumulation processes and an increased risk of eutrophication during the early rainy season. The findings demonstrate the effectiveness of remote sensing applications for rapid surface water quality monitoring, supporting environmental management and urban water quality assessment.
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