Remote Sensing of Particle Absorption Coefficient of Pigments Using a Two-Stage Framework Integrating Optical Classification and Machine Learning
The particle absorption coefficient of pigments (<i>a</i><sub>ph</sub>(λ)), a critical indicator of phytoplankton spectral absorption properties, is essential for bio-optical models and water quality monitoring. To enhance the accuracy of <i>a</i><sub>ph<...
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| author | Xietian Xia Shaohua Lei Hui Lu Zenghui Xu Xiang Li Xing Chen Niancheng Hong Jie Xu Kun Shi Jiacong Huang |
| author_facet | Xietian Xia Shaohua Lei Hui Lu Zenghui Xu Xiang Li Xing Chen Niancheng Hong Jie Xu Kun Shi Jiacong Huang |
| author_sort | Xietian Xia |
| collection | DOAJ |
| description | The particle absorption coefficient of pigments (<i>a</i><sub>ph</sub>(λ)), a critical indicator of phytoplankton spectral absorption properties, is essential for bio-optical models and water quality monitoring. To enhance the accuracy of <i>a</i><sub>ph</sub>(λ) retrieval in complex aquatic environments, this study proposes a novel two-stage framework integrating optical classification and machine learning regression. Focusing on inland waters—key areas for eutrophication monitoring—we first developed an intelligent clustering method combining Kernel Principal Angle-based Component (KPAC) dimensionality reduction and Chameleon Swarm Algorithm (CSA)-optimized k-medoids to classify water bodies into four optical types based on hyperspectral reflectance features. Subsequently, an XGBoost regression model with L1-norm feature selection was applied to inversely derive <i>a</i><sub>ph</sub>(440), <i>a</i><sub>ph</sub>(555), <i>a</i><sub>ph</sub>(675), and <i>a</i><sub>ph</sub>(709) for each class. Experimental results demonstrated that optical classification significantly improved inversion accuracy: the determination coefficients R<sup>2</sup> all exceeded 0.9 in classified datasets, with RMSE reduced by up to 93.1% compared to unclassified scenarios. This indicates that the strategy based on optical classification and regression inversion can effectively enhance the accuracy of pigment particle absorption coefficient inversions. In summary, this study, with the central objective of accurately measuring the pigment particle absorption coefficient, successfully developed a comprehensive set of optical classification and regression inversion methods applicable to various aquatic environments. This new scientific approach and powerful tool provide a means for monitoring and interpreting the pigment particle absorption characteristics in water bodies using remote sensing technology. |
| format | Article |
| id | doaj-art-65c4fcdc3ccb4af2b3bc93f9fce05fe9 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-65c4fcdc3ccb4af2b3bc93f9fce05fe92025-08-20T01:56:45ZengMDPI AGRemote Sensing2072-42922025-05-011710175610.3390/rs17101756Remote Sensing of Particle Absorption Coefficient of Pigments Using a Two-Stage Framework Integrating Optical Classification and Machine LearningXietian Xia0Shaohua Lei1Hui Lu2Zenghui Xu3Xiang Li4Xing Chen5Niancheng Hong6Jie Xu7Kun Shi8Jiacong Huang9China Construction Power and Environment Engineering Co., Ltd., Nanjing 210012, ChinaNational Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, ChinaDepartment of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing 100084, ChinaChina Construction Power and Environment Engineering Co., Ltd., Nanjing 210012, ChinaChina Construction Power and Environment Engineering Co., Ltd., Nanjing 210012, ChinaChina Construction Power and Environment Engineering Co., Ltd., Nanjing 210012, ChinaChina Construction Power and Environment Engineering Co., Ltd., Nanjing 210012, ChinaYangtze River Basin Ecological Environment Monitoring and Scientific Research Center, Yangtze River Basin Ecological Environment Supervision and Administration Bureau, Ministry of Ecology and Environment, Wuhan 430010, ChinaKey Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, ChinaKey Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, ChinaThe particle absorption coefficient of pigments (<i>a</i><sub>ph</sub>(λ)), a critical indicator of phytoplankton spectral absorption properties, is essential for bio-optical models and water quality monitoring. To enhance the accuracy of <i>a</i><sub>ph</sub>(λ) retrieval in complex aquatic environments, this study proposes a novel two-stage framework integrating optical classification and machine learning regression. Focusing on inland waters—key areas for eutrophication monitoring—we first developed an intelligent clustering method combining Kernel Principal Angle-based Component (KPAC) dimensionality reduction and Chameleon Swarm Algorithm (CSA)-optimized k-medoids to classify water bodies into four optical types based on hyperspectral reflectance features. Subsequently, an XGBoost regression model with L1-norm feature selection was applied to inversely derive <i>a</i><sub>ph</sub>(440), <i>a</i><sub>ph</sub>(555), <i>a</i><sub>ph</sub>(675), and <i>a</i><sub>ph</sub>(709) for each class. Experimental results demonstrated that optical classification significantly improved inversion accuracy: the determination coefficients R<sup>2</sup> all exceeded 0.9 in classified datasets, with RMSE reduced by up to 93.1% compared to unclassified scenarios. This indicates that the strategy based on optical classification and regression inversion can effectively enhance the accuracy of pigment particle absorption coefficient inversions. In summary, this study, with the central objective of accurately measuring the pigment particle absorption coefficient, successfully developed a comprehensive set of optical classification and regression inversion methods applicable to various aquatic environments. This new scientific approach and powerful tool provide a means for monitoring and interpreting the pigment particle absorption characteristics in water bodies using remote sensing technology.https://www.mdpi.com/2072-4292/17/10/1756inherent optical propertiesphytoplankton absorptionparticle absorption coefficientwater optical classificationmachine learning regressioninland water monitoring |
| spellingShingle | Xietian Xia Shaohua Lei Hui Lu Zenghui Xu Xiang Li Xing Chen Niancheng Hong Jie Xu Kun Shi Jiacong Huang Remote Sensing of Particle Absorption Coefficient of Pigments Using a Two-Stage Framework Integrating Optical Classification and Machine Learning Remote Sensing inherent optical properties phytoplankton absorption particle absorption coefficient water optical classification machine learning regression inland water monitoring |
| title | Remote Sensing of Particle Absorption Coefficient of Pigments Using a Two-Stage Framework Integrating Optical Classification and Machine Learning |
| title_full | Remote Sensing of Particle Absorption Coefficient of Pigments Using a Two-Stage Framework Integrating Optical Classification and Machine Learning |
| title_fullStr | Remote Sensing of Particle Absorption Coefficient of Pigments Using a Two-Stage Framework Integrating Optical Classification and Machine Learning |
| title_full_unstemmed | Remote Sensing of Particle Absorption Coefficient of Pigments Using a Two-Stage Framework Integrating Optical Classification and Machine Learning |
| title_short | Remote Sensing of Particle Absorption Coefficient of Pigments Using a Two-Stage Framework Integrating Optical Classification and Machine Learning |
| title_sort | remote sensing of particle absorption coefficient of pigments using a two stage framework integrating optical classification and machine learning |
| topic | inherent optical properties phytoplankton absorption particle absorption coefficient water optical classification machine learning regression inland water monitoring |
| url | https://www.mdpi.com/2072-4292/17/10/1756 |
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