A Review of Machine Learning Applications in Ocean Color Remote Sensing
Ocean color remote sensing technology has proven to be an indispensable tool for monitoring ocean conditions, as it has consistently provided critical data on global ocean optical properties, color, and biogeochemical parameters over several decades. With the rapid advancement of artificial intellig...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/10/1776 |
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| author | Zhenhua Zhang Peng Chen Siqi Zhang Haiqing Huang Yuliang Pan Delu Pan |
| author_facet | Zhenhua Zhang Peng Chen Siqi Zhang Haiqing Huang Yuliang Pan Delu Pan |
| author_sort | Zhenhua Zhang |
| collection | DOAJ |
| description | Ocean color remote sensing technology has proven to be an indispensable tool for monitoring ocean conditions, as it has consistently provided critical data on global ocean optical properties, color, and biogeochemical parameters over several decades. With the rapid advancement of artificial intelligence, the integration of machine learning (ML) models into ocean color remote sensing has become a significant focus within the scientific community. This article provides a comprehensive review of the current status and challenges associated with ML models in ocean color remote sensing, assessing their applications in atmospheric correction, color inversion, carbon cycle analysis, and data reconstruction. This review highlights the advancements made in applying ML techniques, such as neural networks and deep learning, to improve data accuracy, enhance resolution, and enable more precise predictions of oceanic phenomena. Despite challenges such as model generalization and computational complexity, ML has significant potential for enhancing our understanding of marine ecosystems, facilitating real-time monitoring, and supporting global climate models. |
| format | Article |
| id | doaj-art-31f6b4c70b9e4a4db190ed204efd817a |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-31f6b4c70b9e4a4db190ed204efd817a2025-08-20T01:56:38ZengMDPI AGRemote Sensing2072-42922025-05-011710177610.3390/rs17101776A Review of Machine Learning Applications in Ocean Color Remote SensingZhenhua Zhang0Peng Chen1Siqi Zhang2Haiqing Huang3Yuliang Pan4Delu Pan5Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), No. 1119, Haibin Road Nansha District, Guangzhou 511458, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Guangzhou), No. 1119, Haibin Road Nansha District, Guangzhou 511458, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Guangzhou), No. 1119, Haibin Road Nansha District, Guangzhou 511458, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Guangzhou), No. 1119, Haibin Road Nansha District, Guangzhou 511458, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Guangzhou), No. 1119, Haibin Road Nansha District, Guangzhou 511458, ChinaSouthern Marine Science and Engineering Guangdong Laboratory (Guangzhou), No. 1119, Haibin Road Nansha District, Guangzhou 511458, ChinaOcean color remote sensing technology has proven to be an indispensable tool for monitoring ocean conditions, as it has consistently provided critical data on global ocean optical properties, color, and biogeochemical parameters over several decades. With the rapid advancement of artificial intelligence, the integration of machine learning (ML) models into ocean color remote sensing has become a significant focus within the scientific community. This article provides a comprehensive review of the current status and challenges associated with ML models in ocean color remote sensing, assessing their applications in atmospheric correction, color inversion, carbon cycle analysis, and data reconstruction. This review highlights the advancements made in applying ML techniques, such as neural networks and deep learning, to improve data accuracy, enhance resolution, and enable more precise predictions of oceanic phenomena. Despite challenges such as model generalization and computational complexity, ML has significant potential for enhancing our understanding of marine ecosystems, facilitating real-time monitoring, and supporting global climate models.https://www.mdpi.com/2072-4292/17/10/1776ocean color remote sensingmachine learningdeep learningoptical oceanographyatmospheric correctionbio-optical properties |
| spellingShingle | Zhenhua Zhang Peng Chen Siqi Zhang Haiqing Huang Yuliang Pan Delu Pan A Review of Machine Learning Applications in Ocean Color Remote Sensing Remote Sensing ocean color remote sensing machine learning deep learning optical oceanography atmospheric correction bio-optical properties |
| title | A Review of Machine Learning Applications in Ocean Color Remote Sensing |
| title_full | A Review of Machine Learning Applications in Ocean Color Remote Sensing |
| title_fullStr | A Review of Machine Learning Applications in Ocean Color Remote Sensing |
| title_full_unstemmed | A Review of Machine Learning Applications in Ocean Color Remote Sensing |
| title_short | A Review of Machine Learning Applications in Ocean Color Remote Sensing |
| title_sort | review of machine learning applications in ocean color remote sensing |
| topic | ocean color remote sensing machine learning deep learning optical oceanography atmospheric correction bio-optical properties |
| url | https://www.mdpi.com/2072-4292/17/10/1776 |
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