Finding Optimal Spatial Window: The Influence of Size on Remote-Sensing-Based Chl-a Prediction in Small Reservoirs

Abstract

This study investigates the optimal spatial window size for estimating chlorophyll-a (Chl-a) concentrations using Sentinel-2 imagery in small reservoirs of Extremadura, Spain. While remote-sensing techniques have proven valuable for water quality monitoring, the influence of pixel window size on estimation accuracy remains understudied, particularly for smaller water bodies. We analyzed 94 atmospherically corrected Sentinel-2 images using the C2RCC processor, corresponding to 32 reservoirs, and compared the results with in situ measurements collected between 2017 and 2022. Our methodology explored window sizes ranging from 1×1 pixels to 20×20 pixels, employing various statistical estimators. Performance was assessed using root-mean-square relative error, mean absolute percentage error, and Spearman's correlation coefficient (ρ). Results show that window sizes between 5×5 and 9×9 pixels yielded optimal Chl-a estimation accuracy. The Cmax estimator consistently outperformed other methods across different window sizes, particularly for mesotrophic and eutrophic waters. Notably, larger window sizes improved correlation with in situ data but showed diminishing returns beyond 9×9 pixels. This study contributes to refining remote-sensing methodologies for inland water quality monitoring, particularly for small- to medium-sized reservoirs. Our findings suggest that careful consideration of spatial window size and statistical estimators can enhance the accuracy of Chl-a concentration predictions, potentially improving water resource management in regions with diverse aquatic ecosystems.