Monitoring Coastal Water Turbidity Using Sentinel2—A Case Study in Los Angeles - Yuwei Kong; Karina Jimenez; Christine M. Lee; Sophia Winter; Jasmine Summers-Evans; Albert Cao; Massimiliano Menczer; Rachel Han; Cade Mills; Savannah McCarthy; Kierstin Blatzheim; Jennifer A. Jay (Remote Sensing)
Los Angeles coastal waters are an ecologically important marine habitat and a famed recreational area for tourists. Constant surveillance is essential to ensure compliance with established health standards and to address the persistent water quality challenges in the region. Remotely sensed datasets are increasingly being applied toward improved detection of water quality by augmenting monitoring programs with spatially intensive and accessible data. This study evaluates the potential of satellite remote sensing to augment traditional monitoring by analyzing the relationship between in situ and satellite-derived turbidity data. Field measurements were performed from July 2021 to March 2024 to build synchronous matchup datasets consisting of satellite and field data. Correlation analysis indicated a positive relationship between satellite-derived and field-measured turbidity (R2 = 0.451). Machine learning models were assessed for predictive accuracy, with the random forest model achieving the highest performance (R2 = 0.632), indicating its robustness in modeling complex turbidity patterns. Seasonal trends revealed higher turbidity during wet months, likely due to stormwater runoff from the Ballona Creek watershed. Despite limitations from cloud cover and spatial resolution, the findings suggest that integrating satellite data with machine learning can enhance large-scale, efficient turbidity monitoring in coastal waters.