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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17122103 |
