Optimizing Chlorophyll-a Concentration Inversion in Coastal Waters Using SVD and Deep Learning Approach
Remote sensing of chlorophyll-a (Chl-a) concentrations in coastal waters is of great importance for the assessment of the marine ecological conditions. However, due to the complex water body optical properties, accurate selection of optimal feature bands is limited, which poses a great challenge for...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10759649/ |
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| author | Lili Zhan Yongxin Xu Jinshan Zhu Zhangshuo Liu |
| author_facet | Lili Zhan Yongxin Xu Jinshan Zhu Zhangshuo Liu |
| author_sort | Lili Zhan |
| collection | DOAJ |
| description | Remote sensing of chlorophyll-a (Chl-a) concentrations in coastal waters is of great importance for the assessment of the marine ecological conditions. However, due to the complex water body optical properties, accurate selection of optimal feature bands is limited, which poses a great challenge for high-precision retrieval. The purpose of this research is to address the problem of high-precision retrieval of the Chl-a concentration in small coastal waters. In this article, a singular value decomposition and deep neural network (SVD-DNN) Chl-a inversion model for Hong Kong coastal waters were constructed. Other machine learning methods, such as random forest (RF) and the support vector machine (SVM) are also used to establish the inversion models for the comparison. At the same time, a comparative analysis was performed with Chl-a retrieval models created using a feature selection method based on the correlation of band combinations. These models are validated using the Landsat 8 OLI and synchronously measured Chl-a dataset (N = 149 samples). Results show that the developed SVD-DNN model has the best retrieval accuracy [mean R = 0.90, root mean square error (RMSE) = 1.21, mean absolute error (MAE) = 1.05], outperforming the SVD-RF and SVD-SVM models. The SVD-DNN model shows superior retrieval performance when the Chl-a concentration is below 6 micrograms per liter (RMSE = 0.66, MAE = 0.67). The proposed model also shows better temporal generalization ability in 2013, 2014, and 2016 compared to the other models. This study demonstrates that the developed and validated SVD-DNN model has excellent robustness and generalization ability and can be used in combination with Landsat data for long-term retrieval of Chl-a across different time series. |
| format | Article |
| id | doaj-art-854a099b997d42e799aaa0c334098aad |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-854a099b997d42e799aaa0c334098aad2024-12-06T00:00:27ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-011890792010.1109/JSTARS.2024.349522110759649Optimizing Chlorophyll-a Concentration Inversion in Coastal Waters Using SVD and Deep Learning ApproachLili Zhan0Yongxin Xu1Jinshan Zhu2https://orcid.org/0000-0003-0102-7603Zhangshuo Liu3https://orcid.org/0009-0005-1821-7077College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, ChinaRemote sensing of chlorophyll-a (Chl-a) concentrations in coastal waters is of great importance for the assessment of the marine ecological conditions. However, due to the complex water body optical properties, accurate selection of optimal feature bands is limited, which poses a great challenge for high-precision retrieval. The purpose of this research is to address the problem of high-precision retrieval of the Chl-a concentration in small coastal waters. In this article, a singular value decomposition and deep neural network (SVD-DNN) Chl-a inversion model for Hong Kong coastal waters were constructed. Other machine learning methods, such as random forest (RF) and the support vector machine (SVM) are also used to establish the inversion models for the comparison. At the same time, a comparative analysis was performed with Chl-a retrieval models created using a feature selection method based on the correlation of band combinations. These models are validated using the Landsat 8 OLI and synchronously measured Chl-a dataset (N = 149 samples). Results show that the developed SVD-DNN model has the best retrieval accuracy [mean R = 0.90, root mean square error (RMSE) = 1.21, mean absolute error (MAE) = 1.05], outperforming the SVD-RF and SVD-SVM models. The SVD-DNN model shows superior retrieval performance when the Chl-a concentration is below 6 micrograms per liter (RMSE = 0.66, MAE = 0.67). The proposed model also shows better temporal generalization ability in 2013, 2014, and 2016 compared to the other models. This study demonstrates that the developed and validated SVD-DNN model has excellent robustness and generalization ability and can be used in combination with Landsat data for long-term retrieval of Chl-a across different time series.https://ieeexplore.ieee.org/document/10759649/Chlorophyllclass II waterremote sensing inversionsingular value decomposition (SVD) |
| spellingShingle | Lili Zhan Yongxin Xu Jinshan Zhu Zhangshuo Liu Optimizing Chlorophyll-a Concentration Inversion in Coastal Waters Using SVD and Deep Learning Approach IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Chlorophyll class II water remote sensing inversion singular value decomposition (SVD) |
| title | Optimizing Chlorophyll-a Concentration Inversion in Coastal Waters Using SVD and Deep Learning Approach |
| title_full | Optimizing Chlorophyll-a Concentration Inversion in Coastal Waters Using SVD and Deep Learning Approach |
| title_fullStr | Optimizing Chlorophyll-a Concentration Inversion in Coastal Waters Using SVD and Deep Learning Approach |
| title_full_unstemmed | Optimizing Chlorophyll-a Concentration Inversion in Coastal Waters Using SVD and Deep Learning Approach |
| title_short | Optimizing Chlorophyll-a Concentration Inversion in Coastal Waters Using SVD and Deep Learning Approach |
| title_sort | optimizing chlorophyll a concentration inversion in coastal waters using svd and deep learning approach |
| topic | Chlorophyll class II water remote sensing inversion singular value decomposition (SVD) |
| url | https://ieeexplore.ieee.org/document/10759649/ |
| work_keys_str_mv | AT lilizhan optimizingchlorophyllaconcentrationinversionincoastalwatersusingsvdanddeeplearningapproach AT yongxinxu optimizingchlorophyllaconcentrationinversionincoastalwatersusingsvdanddeeplearningapproach AT jinshanzhu optimizingchlorophyllaconcentrationinversionincoastalwatersusingsvdanddeeplearningapproach AT zhangshuoliu optimizingchlorophyllaconcentrationinversionincoastalwatersusingsvdanddeeplearningapproach |