Monitoring Coastal Water Turbidity Using Sentinel2—A Case Study in Los Angeles
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...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/201 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Yuwei Kong |
| collection | DOAJ |
| description | 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 (R<sup>2</sup> = 0.451). Machine learning models were assessed for predictive accuracy, with the random forest model achieving the highest performance (R<sup>2</sup> = 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. |
| format | Article |
| id | doaj-art-9858a0553fb8472ab33a202c889b3bdf |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-9858a0553fb8472ab33a202c889b3bdf2025-01-24T13:47:42ZengMDPI AGRemote Sensing2072-42922025-01-0117220110.3390/rs17020201Monitoring Coastal Water Turbidity Using Sentinel2—A Case Study in Los AngelesYuwei Kong0Karina Jimenez1Christine M. Lee2Sophia Winter3Jasmine Summers-Evans4Albert Cao5Massimiliano Menczer6Rachel Han7Cade Mills8Savannah McCarthy9Kierstin Blatzheim10Jennifer A. Jay11Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USADepartment of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91011, USAInstitute of the Environment and Sustainability, University of California, Los Angeles, CA 90025, USAInstitute of the Environment and Sustainability, University of California, Los Angeles, CA 90025, USAInstitute of the Environment and Sustainability, University of California, Los Angeles, CA 90025, USAInstitute of the Environment and Sustainability, University of California, Los Angeles, CA 90025, USAInstitute of the Environment and Sustainability, University of California, Los Angeles, CA 90025, USAInstitute of the Environment and Sustainability, University of California, Los Angeles, CA 90025, USAInstitute of the Environment and Sustainability, University of California, Los Angeles, CA 90025, USAInstitute of the Environment and Sustainability, University of California, Los Angeles, CA 90025, USADepartment of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USALos 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 (R<sup>2</sup> = 0.451). Machine learning models were assessed for predictive accuracy, with the random forest model achieving the highest performance (R<sup>2</sup> = 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.https://www.mdpi.com/2072-4292/17/2/201remote sensingwater qualitycoastal waterturbidity |
| spellingShingle | 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 Monitoring Coastal Water Turbidity Using Sentinel2—A Case Study in Los Angeles Remote Sensing remote sensing water quality coastal water turbidity |
| title | Monitoring Coastal Water Turbidity Using Sentinel2—A Case Study in Los Angeles |
| title_full | Monitoring Coastal Water Turbidity Using Sentinel2—A Case Study in Los Angeles |
| title_fullStr | Monitoring Coastal Water Turbidity Using Sentinel2—A Case Study in Los Angeles |
| title_full_unstemmed | Monitoring Coastal Water Turbidity Using Sentinel2—A Case Study in Los Angeles |
| title_short | Monitoring Coastal Water Turbidity Using Sentinel2—A Case Study in Los Angeles |
| title_sort | monitoring coastal water turbidity using sentinel2 a case study in los angeles |
| topic | remote sensing water quality coastal water turbidity |
| url | https://www.mdpi.com/2072-4292/17/2/201 |
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