Remote sensing monitoring of fluorescent dissolved organic matter in Admiralty Bay: fusion of multi-source signal removal and machine learning
Fluorescent dissolved organic matter (fDOM), a fluorescent component of dissolved organic matter (DOM), plays a crucial role in tracing pollution pathways in marine environments. While remote sensing has been used to monitor fDOM changes, the impact of multi-source interference has often been overlo...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Science of Remote Sensing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000665 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849730286831534080 |
|---|---|
| author | Ruiwu Zhang Ruru Deng Jun Ying Jiayi Li Yu Guo Junying Yang Cong Lei |
| author_facet | Ruiwu Zhang Ruru Deng Jun Ying Jiayi Li Yu Guo Junying Yang Cong Lei |
| author_sort | Ruiwu Zhang |
| collection | DOAJ |
| description | Fluorescent dissolved organic matter (fDOM), a fluorescent component of dissolved organic matter (DOM), plays a crucial role in tracing pollution pathways in marine environments. While remote sensing has been used to monitor fDOM changes, the impact of multi-source interference has often been overlooked, limiting the accuracy of inversion results. In this study, based on fDOM measurements from Admiralty Bay and from the perspective of optical physical mechanisms, we eliminated atmospheric effects, surface reflection, solar-induced fluorescence (SIF), Raman scattering, and particle absorption from remote sensing reflectance (Rrs(λ)). This preprocessing improved the stability of Rrs(λ), enhancing the reliability of subsequent fDOM inversion. Based on the corrected reflectance, three sensitive wavelengths highly correlated with fDOM were selected. Five machine learning models—Random Forest (RF), XGBoost, Classification and Regression Trees (CART), Gradient Boosting Regression (GBR), and AdaBoost—were then applied for fDOM inversion, with XGBoost achieving the best performance. The inversion results revealed that fDOM concentrations in Admiralty Bay were highest in the western and coastal areas, gradually increasing toward the center, exhibiting a locally clustered distribution. This study demonstrates the effectiveness of combining physical and data-driven methods for fDOM inversion, providing a foundation for long-term monitoring of dissolved organic matter in polar marine environments. |
| format | Article |
| id | doaj-art-dc40cc200b7641eb8fdf7540fe736a8b |
| institution | DOAJ |
| issn | 2666-0172 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Science of Remote Sensing |
| spelling | doaj-art-dc40cc200b7641eb8fdf7540fe736a8b2025-08-20T03:08:55ZengElsevierScience of Remote Sensing2666-01722025-12-011210026010.1016/j.srs.2025.100260Remote sensing monitoring of fluorescent dissolved organic matter in Admiralty Bay: fusion of multi-source signal removal and machine learningRuiwu Zhang0Ruru Deng1Jun Ying2Jiayi Li3Yu Guo4Junying Yang5Cong Lei6School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, ChinaSchool of Geography and Planning, Sun Yat-sen University, Guangzhou, 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China; Guangdong Engineering Research Center of Water Environment Remote Sensing Monitoring, Guangzhou, 510275, China; Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Guangzhou, 510275, China; Corresponding author. School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510006, China.Institute of Ecological Civilization and Institute of Carbon Neutrality, Zhejiang A&F University, Hangzhou, 311300, China; College of Landscape Architecture and Architecture, Zhejiang A&F University, Hangzhou, 311300, ChinaSchool of Geography and Planning, Sun Yat-sen University, Guangzhou, 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, ChinaSchool of Geography and Planning, Sun Yat-sen University, Guangzhou, 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, ChinaSchool of Geography and Planning, Sun Yat-sen University, Guangzhou, 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, ChinaSchool of Geography and Planning, Sun Yat-sen University, Guangzhou, 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, ChinaFluorescent dissolved organic matter (fDOM), a fluorescent component of dissolved organic matter (DOM), plays a crucial role in tracing pollution pathways in marine environments. While remote sensing has been used to monitor fDOM changes, the impact of multi-source interference has often been overlooked, limiting the accuracy of inversion results. In this study, based on fDOM measurements from Admiralty Bay and from the perspective of optical physical mechanisms, we eliminated atmospheric effects, surface reflection, solar-induced fluorescence (SIF), Raman scattering, and particle absorption from remote sensing reflectance (Rrs(λ)). This preprocessing improved the stability of Rrs(λ), enhancing the reliability of subsequent fDOM inversion. Based on the corrected reflectance, three sensitive wavelengths highly correlated with fDOM were selected. Five machine learning models—Random Forest (RF), XGBoost, Classification and Regression Trees (CART), Gradient Boosting Regression (GBR), and AdaBoost—were then applied for fDOM inversion, with XGBoost achieving the best performance. The inversion results revealed that fDOM concentrations in Admiralty Bay were highest in the western and coastal areas, gradually increasing toward the center, exhibiting a locally clustered distribution. This study demonstrates the effectiveness of combining physical and data-driven methods for fDOM inversion, providing a foundation for long-term monitoring of dissolved organic matter in polar marine environments.http://www.sciencedirect.com/science/article/pii/S2666017225000665Fluorescent dissolved organic matterOptical signalRemote sensing inversionWater reflectivityMachine learning model |
| spellingShingle | Ruiwu Zhang Ruru Deng Jun Ying Jiayi Li Yu Guo Junying Yang Cong Lei Remote sensing monitoring of fluorescent dissolved organic matter in Admiralty Bay: fusion of multi-source signal removal and machine learning Science of Remote Sensing Fluorescent dissolved organic matter Optical signal Remote sensing inversion Water reflectivity Machine learning model |
| title | Remote sensing monitoring of fluorescent dissolved organic matter in Admiralty Bay: fusion of multi-source signal removal and machine learning |
| title_full | Remote sensing monitoring of fluorescent dissolved organic matter in Admiralty Bay: fusion of multi-source signal removal and machine learning |
| title_fullStr | Remote sensing monitoring of fluorescent dissolved organic matter in Admiralty Bay: fusion of multi-source signal removal and machine learning |
| title_full_unstemmed | Remote sensing monitoring of fluorescent dissolved organic matter in Admiralty Bay: fusion of multi-source signal removal and machine learning |
| title_short | Remote sensing monitoring of fluorescent dissolved organic matter in Admiralty Bay: fusion of multi-source signal removal and machine learning |
| title_sort | remote sensing monitoring of fluorescent dissolved organic matter in admiralty bay fusion of multi source signal removal and machine learning |
| topic | Fluorescent dissolved organic matter Optical signal Remote sensing inversion Water reflectivity Machine learning model |
| url | http://www.sciencedirect.com/science/article/pii/S2666017225000665 |
| work_keys_str_mv | AT ruiwuzhang remotesensingmonitoringoffluorescentdissolvedorganicmatterinadmiraltybayfusionofmultisourcesignalremovalandmachinelearning AT rurudeng remotesensingmonitoringoffluorescentdissolvedorganicmatterinadmiraltybayfusionofmultisourcesignalremovalandmachinelearning AT junying remotesensingmonitoringoffluorescentdissolvedorganicmatterinadmiraltybayfusionofmultisourcesignalremovalandmachinelearning AT jiayili remotesensingmonitoringoffluorescentdissolvedorganicmatterinadmiraltybayfusionofmultisourcesignalremovalandmachinelearning AT yuguo remotesensingmonitoringoffluorescentdissolvedorganicmatterinadmiraltybayfusionofmultisourcesignalremovalandmachinelearning AT junyingyang remotesensingmonitoringoffluorescentdissolvedorganicmatterinadmiraltybayfusionofmultisourcesignalremovalandmachinelearning AT conglei remotesensingmonitoringoffluorescentdissolvedorganicmatterinadmiraltybayfusionofmultisourcesignalremovalandmachinelearning |