PhA-MOE: Enhancing Hyperspectral Retrievals for Phytoplankton Absorption Using Mixture-of-Experts.
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| Title: | PhA-MOE: Enhancing Hyperspectral Retrievals for Phytoplankton Absorption Using Mixture-of-Experts. |
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| Authors: | Wang, Weiwei1 (AUTHOR), Liu, Bingqing2 (AUTHOR), Gao, Song3 (AUTHOR), Li, Jiang1,2 (AUTHOR), Zhou, Yueling2,3 (AUTHOR), Zhang, Songyang3 (AUTHOR) songyang.zhang@louisiana.edu, Ding, Zhi1 (AUTHOR) |
| Source: | Remote Sensing. Jun2025, Vol. 17 Issue 12, p2103. 31p. |
| Subjects: | Ocean color, Absorption coefficients, Artificial intelligence, Territorial waters, Remote sensing |
| Abstract: | As a key component of inherent optical properties (IOPs) in ocean color remote sensing, phytoplankton absorption coefficient ( a p h y ), especially in hyperspectral, greatly enhances our understanding of phytoplankton community composition (PCC). The recent launches of NASA's hyperspectral missions, such as EMIT and PACE, have generated an urgent need for hyperspectral algorithms for studying phytoplankton. Retrieving a p h y from ocean color remote sensing in coastal waters has been extremely challenging due to complex optical properties. Traditional methods often fail under these circumstances, while improved machine-learning approaches are hindered by data scarcity, heterogeneity, and noise from data collection. In response, this study introduces a novel machine learning framework for hyperspectral retrievals of a p h y based on the mixture-of-experts (MOEs), named PhA-MOE. Various preprocessing methods for hyperspectral training data are explored, with the combination of robust and logarithmic scalers identified as optimal. The proposed PhA-MOE for a p h y prediction is tailored to both past and current hyperspectral missions, including EMIT and PACE. Extensive experiments reveal the importance of data preprocessing and improved performance of PhA-MOE in estimating a p h y as well as in handling data heterogeneity. Notably, this study marks the first application of a machine learning–based MOE model to real PACE-OCI hyperspectral imagery, validated using match-up field data. This application enables the exploration of spatiotemporal variations in a p h y within an optically complex estuarine environment. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 186260907 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: PhA-MOE: Enhancing Hyperspectral Retrievals for Phytoplankton Absorption Using Mixture-of-Experts. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Weiwei%22">Wang, Weiwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Bingqing%22">Liu, Bingqing</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Song%22">Gao, Song</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Jiang%22">Li, Jiang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Yueling%22">Zhou, Yueling</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Songyang%22">Zhang, Songyang</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> songyang.zhang@louisiana.edu</i><br /><searchLink fieldCode="AR" term="%22Ding%2C+Zhi%22">Ding, Zhi</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2025, Vol. 17 Issue 12, p2103. 31p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Ocean+color%22">Ocean color</searchLink><br /><searchLink fieldCode="DE" term="%22Absorption+coefficients%22">Absorption coefficients</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Territorial+waters%22">Territorial waters</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As a key component of inherent optical properties (IOPs) in ocean color remote sensing, phytoplankton absorption coefficient ( a p h y ), especially in hyperspectral, greatly enhances our understanding of phytoplankton community composition (PCC). The recent launches of NASA's hyperspectral missions, such as EMIT and PACE, have generated an urgent need for hyperspectral algorithms for studying phytoplankton. Retrieving a p h y from ocean color remote sensing in coastal waters has been extremely challenging due to complex optical properties. Traditional methods often fail under these circumstances, while improved machine-learning approaches are hindered by data scarcity, heterogeneity, and noise from data collection. In response, this study introduces a novel machine learning framework for hyperspectral retrievals of a p h y based on the mixture-of-experts (MOEs), named PhA-MOE. Various preprocessing methods for hyperspectral training data are explored, with the combination of robust and logarithmic scalers identified as optimal. The proposed PhA-MOE for a p h y prediction is tailored to both past and current hyperspectral missions, including EMIT and PACE. Extensive experiments reveal the importance of data preprocessing and improved performance of PhA-MOE in estimating a p h y as well as in handling data heterogeneity. Notably, this study marks the first application of a machine learning–based MOE model to real PACE-OCI hyperspectral imagery, validated using match-up field data. This application enables the exploration of spatiotemporal variations in a p h y within an optically complex estuarine environment. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs17122103 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 2103 Subjects: – SubjectFull: Ocean color Type: general – SubjectFull: Absorption coefficients Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Territorial waters Type: general – SubjectFull: Remote sensing Type: general Titles: – TitleFull: PhA-MOE: Enhancing Hyperspectral Retrievals for Phytoplankton Absorption Using Mixture-of-Experts. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Weiwei – PersonEntity: Name: NameFull: Liu, Bingqing – PersonEntity: Name: NameFull: Gao, Song – PersonEntity: Name: NameFull: Li, Jiang – PersonEntity: Name: NameFull: Zhou, Yueling – PersonEntity: Name: NameFull: Zhang, Songyang – PersonEntity: Name: NameFull: Ding, Zhi IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 17 – Type: issue Value: 12 Titles: – TitleFull: Remote Sensing Type: main |
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