SPATIAL AND TEMPORAL ANALYSIS OF CHLOROPHYLL-A USING SENTINEL-2 DATA AT SUTAMI RESERVOIR, INDONESIA

A water quality monitoring program is generally performed using a direct measurement method, which requires substantial efforts and resources. One of the options to minimize these efforts is utillize satelite imagery for extracting water quality parameters, such as Chlorophyll-a (Chl-a) concentration. Sentinel-2 with its vast multi-spectral imagery is capable and broadly used for retrieving Chl-a concentration. This study provides validation of five Chl-a retrieval algorithm to identify the best-fitting model for Sutami Reservoir in Indonesia. The Chl-a model predictor was developed using three preprocessed Sentinel-2 images taken over Sutami Reservoir calibrated with 17 in-situ datasets from 3 sampling location where Chl-a ranged from 0.10 mg/m³ to 1.57 mg/m³ and mean of 0.81 mg/m³. Five algorithm of different Chl-a Band Ratio Predictor was evaluated with multiple validation parameters. One of the models built incorporating algorithm which use Band B4, B5, and B6 for Chl-a retrieval achieved Pearson R coefficient of 0.91, coefficient of determination (R²) = 0.82, Normalized Root Mean Square Error (NRMSE) = 0.14, and Nash-Sutcliffe Efficiency (NSE) = 0.81. This model evaluation has shown better accuracy compared to the other four algorithms. The results indicate promising potential of developing a model for accurate Chl-a retrievals utilizing Sentinel-2 satellite imagery, providing valuable tool for remote water quality monitoring system in Sutami Reservoir, thus minimizing the needs of extensive onsite sampling which then can be used only for local calibration.