Estimation of Chlorophyll-a Concentrations in Lanalhue Lake Using Sentinel-2 MSI Satellite Images
Inland water is fundamental for the conservation of flora and fauna and is a source of drinking water for humans; therefore, monitoring its quality and ascertaining its status is essential for making decisions in water resources management. As traditional measuring methods present limitations in mon...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/22/5647 |
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| author | Francisca Barraza-Moraga Hernán Alcayaga Alonso Pizarro Jorge Félez-Bernal Roberto Urrutia |
| author_facet | Francisca Barraza-Moraga Hernán Alcayaga Alonso Pizarro Jorge Félez-Bernal Roberto Urrutia |
| author_sort | Francisca Barraza-Moraga |
| collection | DOAJ |
| description | Inland water is fundamental for the conservation of flora and fauna and is a source of drinking water for humans; therefore, monitoring its quality and ascertaining its status is essential for making decisions in water resources management. As traditional measuring methods present limitations in monitoring with high spatial and temporal coverage, using satellite images to have greater control over lake observation can be a handy tool and have satisfactory results. The study of chlorophyll-a (Chl-a) has been widely used to ascertain the quality of the inland aquatic environment using remote sensing, but in general, it depends on the local conditions of the water body. In this study, the suitability of the Sentinel-2 MSI sensor for Chl-a estimation in a lake in south-central Chile is tested. An empirical approach is proposed, applying multiple linear regressions, comparing the efficiency and performance with L1C and L2A products, separating the equations constructed with spring-summer and fall-winter data, and restricting Chl-a ranges to those measured in the field to generate these regressions. The algorithms combining spectral bans proved to have a good correlation with Chl-a measured in the field, generally resulting in R<sup>2</sup> greater than 0.87 and RMSE and MAE with errors less than 6 μg L<sup>−1</sup>. The spatial distribution of Chl-a concentrations at the study site was obtained based on the proposed equations. |
| first_indexed | 2024-03-09T18:01:52Z |
| format | Article |
| id | doaj.art-406d89b986874dd1a82d96caf8c7a40f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:01:52Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-406d89b986874dd1a82d96caf8c7a40f2023-11-24T09:48:13ZengMDPI AGRemote Sensing2072-42922022-11-011422564710.3390/rs14225647Estimation of Chlorophyll-a Concentrations in Lanalhue Lake Using Sentinel-2 MSI Satellite ImagesFrancisca Barraza-Moraga0Hernán Alcayaga1Alonso Pizarro2Jorge Félez-Bernal3Roberto Urrutia4Civil Engeenering School, Universidad Diego Portales, Santiago 8370109, ChileCivil Engeenering School, Universidad Diego Portales, Santiago 8370109, ChileCivil Engeenering School, Universidad Diego Portales, Santiago 8370109, ChileEnvironmental Science Faculty, Centro EULA-Chile, Universidad de Concepción, Concepción 4070386, ChileEnvironmental Science Faculty, Centro EULA-Chile, Universidad de Concepción, Concepción 4070386, ChileInland water is fundamental for the conservation of flora and fauna and is a source of drinking water for humans; therefore, monitoring its quality and ascertaining its status is essential for making decisions in water resources management. As traditional measuring methods present limitations in monitoring with high spatial and temporal coverage, using satellite images to have greater control over lake observation can be a handy tool and have satisfactory results. The study of chlorophyll-a (Chl-a) has been widely used to ascertain the quality of the inland aquatic environment using remote sensing, but in general, it depends on the local conditions of the water body. In this study, the suitability of the Sentinel-2 MSI sensor for Chl-a estimation in a lake in south-central Chile is tested. An empirical approach is proposed, applying multiple linear regressions, comparing the efficiency and performance with L1C and L2A products, separating the equations constructed with spring-summer and fall-winter data, and restricting Chl-a ranges to those measured in the field to generate these regressions. The algorithms combining spectral bans proved to have a good correlation with Chl-a measured in the field, generally resulting in R<sup>2</sup> greater than 0.87 and RMSE and MAE with errors less than 6 μg L<sup>−1</sup>. The spatial distribution of Chl-a concentrations at the study site was obtained based on the proposed equations.https://www.mdpi.com/2072-4292/14/22/5647remote sensingchlorophyll-awater qualitymultiple linear regressionSentinel-2Google Earth Engine |
| spellingShingle | Francisca Barraza-Moraga Hernán Alcayaga Alonso Pizarro Jorge Félez-Bernal Roberto Urrutia Estimation of Chlorophyll-a Concentrations in Lanalhue Lake Using Sentinel-2 MSI Satellite Images Remote Sensing remote sensing chlorophyll-a water quality multiple linear regression Sentinel-2 Google Earth Engine |
| title | Estimation of Chlorophyll-a Concentrations in Lanalhue Lake Using Sentinel-2 MSI Satellite Images |
| title_full | Estimation of Chlorophyll-a Concentrations in Lanalhue Lake Using Sentinel-2 MSI Satellite Images |
| title_fullStr | Estimation of Chlorophyll-a Concentrations in Lanalhue Lake Using Sentinel-2 MSI Satellite Images |
| title_full_unstemmed | Estimation of Chlorophyll-a Concentrations in Lanalhue Lake Using Sentinel-2 MSI Satellite Images |
| title_short | Estimation of Chlorophyll-a Concentrations in Lanalhue Lake Using Sentinel-2 MSI Satellite Images |
| title_sort | estimation of chlorophyll a concentrations in lanalhue lake using sentinel 2 msi satellite images |
| topic | remote sensing chlorophyll-a water quality multiple linear regression Sentinel-2 Google Earth Engine |
| url | https://www.mdpi.com/2072-4292/14/22/5647 |
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