A Hybrid Chlorophyll a Estimation Method for Oligotrophic and Mesotrophic Reservoirs Based on Optical Water Classification
Low- and medium-resolution satellites have been a relatively mature platform for inland eutrophic water classification and chlorophyll a concentration (Chl-a) retrieval algorithms. However, for oligotrophic and mesotrophic waters in small- and medium-sized reservoirs, problems of low satellite resol...
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MDPI AG
2023-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/8/2209 |
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| author | Xiaoyan Dang Jun Du Chao Wang Fangfang Zhang Lin Wu Jiping Liu Zheng Wang Xu Yang Jingxu Wang |
| author_facet | Xiaoyan Dang Jun Du Chao Wang Fangfang Zhang Lin Wu Jiping Liu Zheng Wang Xu Yang Jingxu Wang |
| author_sort | Xiaoyan Dang |
| collection | DOAJ |
| description | Low- and medium-resolution satellites have been a relatively mature platform for inland eutrophic water classification and chlorophyll a concentration (Chl-a) retrieval algorithms. However, for oligotrophic and mesotrophic waters in small- and medium-sized reservoirs, problems of low satellite resolution, insufficient water sampling, and higher uncertainty in retrieval accuracy exist. In this paper, a hybrid Chl-a estimation method based on spectral characteristics (i.e., remote sensing reflectance (Rrs)) classification was developed for oligotrophic and mesotrophic waters using high-resolution satellite Sentinel-2 (A and B) data. First, 99 samples and quasi-synchronous Sentinel-2 satellite data were collected from four small- and medium-sized reservoirs in central China, and the usability of the Sentinel-2 Rrs data in inland oligotrophic and mesotrophic waters was verified by accurate atmospheric correction. Second, a new optical classification method was constructed based on different water characteristics to classify waters into clear water, phytoplankton-dominated water, and water dominated by phytoplankton and suspended matter together using the thresholds of Rrs490/Rrs560 and Rrs665/Rrs560. The proposed method has a higher classification accuracy compared to other classification methods, and the band-ratio algorithm is simpler and more effective for satellite sensors without NIR bands. Third, given the sensitivity of the empirical method to water variability and the ease of development and implementation, a nonlinear least squares fitted one-dimensional nonlinear function was established based on the selection of the best-fitting spectral indices for different optical water types (OWTs) and compared with other Chl-a estimation algorithms. The validation results showed that the hybrid two-band method had the highest accuracy with squared correlation coefficient, root mean squared difference, mean absolute percentage error, and bias of 0.85, 2.93, 32.42%, and −0.75 mg/m<sup>3</sup>, respectively, and the results of the residual values further validated the applicability and reliability of the model. Finally, the performance of the classification and estimation algorithms on the four reservoirs was evaluated to obtain images mapping the Chl-a in the reservoirs. In conclusion, this study improves the accuracy of Chl-a estimation for oligotrophic and mesotrophic waters by combining a new classification algorithm with a two-band hybrid model, which is an important contribution to solving the problem of low resolution and high uncertainty in the retrieval of Chl-a in oligotrophic and mesotrophic waters in small- and medium-sized reservoirs and has the potential to be applied to other optically similar oligotrophic and mesotrophic lakes and reservoirs using similar spectrally satellite sensors. |
| first_indexed | 2024-03-11T04:34:35Z |
| format | Article |
| id | doaj.art-fb5f08b5bce14caf803a2331f0925309 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:34:35Z |
| publishDate | 2023-04-01 |
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| spelling | doaj.art-fb5f08b5bce14caf803a2331f09253092023-11-17T21:13:32ZengMDPI AGRemote Sensing2072-42922023-04-01158220910.3390/rs15082209A Hybrid Chlorophyll a Estimation Method for Oligotrophic and Mesotrophic Reservoirs Based on Optical Water ClassificationXiaoyan Dang0Jun Du1Chao Wang2Fangfang Zhang3Lin Wu4Jiping Liu5Zheng Wang6Xu Yang7Jingxu Wang8Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, ChinaInstitute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, ChinaInstitute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaSchool of Computer and Information Engineering, Henan University, Kaifeng 475004, ChinaChinese Academy of Surveying and Mapping, Beijing 100830, ChinaInstitute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, ChinaInstitute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, ChinaInstitute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, ChinaLow- and medium-resolution satellites have been a relatively mature platform for inland eutrophic water classification and chlorophyll a concentration (Chl-a) retrieval algorithms. However, for oligotrophic and mesotrophic waters in small- and medium-sized reservoirs, problems of low satellite resolution, insufficient water sampling, and higher uncertainty in retrieval accuracy exist. In this paper, a hybrid Chl-a estimation method based on spectral characteristics (i.e., remote sensing reflectance (Rrs)) classification was developed for oligotrophic and mesotrophic waters using high-resolution satellite Sentinel-2 (A and B) data. First, 99 samples and quasi-synchronous Sentinel-2 satellite data were collected from four small- and medium-sized reservoirs in central China, and the usability of the Sentinel-2 Rrs data in inland oligotrophic and mesotrophic waters was verified by accurate atmospheric correction. Second, a new optical classification method was constructed based on different water characteristics to classify waters into clear water, phytoplankton-dominated water, and water dominated by phytoplankton and suspended matter together using the thresholds of Rrs490/Rrs560 and Rrs665/Rrs560. The proposed method has a higher classification accuracy compared to other classification methods, and the band-ratio algorithm is simpler and more effective for satellite sensors without NIR bands. Third, given the sensitivity of the empirical method to water variability and the ease of development and implementation, a nonlinear least squares fitted one-dimensional nonlinear function was established based on the selection of the best-fitting spectral indices for different optical water types (OWTs) and compared with other Chl-a estimation algorithms. The validation results showed that the hybrid two-band method had the highest accuracy with squared correlation coefficient, root mean squared difference, mean absolute percentage error, and bias of 0.85, 2.93, 32.42%, and −0.75 mg/m<sup>3</sup>, respectively, and the results of the residual values further validated the applicability and reliability of the model. Finally, the performance of the classification and estimation algorithms on the four reservoirs was evaluated to obtain images mapping the Chl-a in the reservoirs. In conclusion, this study improves the accuracy of Chl-a estimation for oligotrophic and mesotrophic waters by combining a new classification algorithm with a two-band hybrid model, which is an important contribution to solving the problem of low resolution and high uncertainty in the retrieval of Chl-a in oligotrophic and mesotrophic waters in small- and medium-sized reservoirs and has the potential to be applied to other optically similar oligotrophic and mesotrophic lakes and reservoirs using similar spectrally satellite sensors.https://www.mdpi.com/2072-4292/15/8/2209chlorophyll a concentrationSentinel-2oligotrophic and mesotrophic wateroptical water typesremote sensing reflectance |
| spellingShingle | Xiaoyan Dang Jun Du Chao Wang Fangfang Zhang Lin Wu Jiping Liu Zheng Wang Xu Yang Jingxu Wang A Hybrid Chlorophyll a Estimation Method for Oligotrophic and Mesotrophic Reservoirs Based on Optical Water Classification Remote Sensing chlorophyll a concentration Sentinel-2 oligotrophic and mesotrophic water optical water types remote sensing reflectance |
| title | A Hybrid Chlorophyll a Estimation Method for Oligotrophic and Mesotrophic Reservoirs Based on Optical Water Classification |
| title_full | A Hybrid Chlorophyll a Estimation Method for Oligotrophic and Mesotrophic Reservoirs Based on Optical Water Classification |
| title_fullStr | A Hybrid Chlorophyll a Estimation Method for Oligotrophic and Mesotrophic Reservoirs Based on Optical Water Classification |
| title_full_unstemmed | A Hybrid Chlorophyll a Estimation Method for Oligotrophic and Mesotrophic Reservoirs Based on Optical Water Classification |
| title_short | A Hybrid Chlorophyll a Estimation Method for Oligotrophic and Mesotrophic Reservoirs Based on Optical Water Classification |
| title_sort | hybrid chlorophyll a estimation method for oligotrophic and mesotrophic reservoirs based on optical water classification |
| topic | chlorophyll a concentration Sentinel-2 oligotrophic and mesotrophic water optical water types remote sensing reflectance |
| url | https://www.mdpi.com/2072-4292/15/8/2209 |
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