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Abstract We report on observed trend anomalies in climate-relevant global ocean biogeochemical properties, as derived from satellite ocean color measurements, that show a substantial decline in phytoplankton carbon concentrations following eruptions of the submarine volcano Hunga Tonga-Hunga Ha’apai in January 2022. The anomalies are seen in remotely-sensed ocean color data sets from multiple satellite missions, but not in situ observations, thus suggesting that the observed anomalies are a result of ocean color retrieval errors rather than indicators of a major shift in phytoplankton carbon concentrations. The enhanced concentration of aerosols in the stratosphere following the eruptions results in a violation of some fundamental assumptions in the processing algorithms used to obtain marine biogeochemical properties from satellite radiometric observations, and it is demonstrated through radiative transfer simulations that this is the likely cause of the anomalous trends. We note that any future stratospheric aerosol disturbances, either natural or geoengineered, may lead to similar artifacts in satellite ocean color and other remote-sensing measurements of the marine environment, thus confounding our ability to track the impact of such events on ocean ecosystems.
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This content will become publicly available on June 1, 2026
PhA-MOE: Enhancing Hyperspectral Retrievals for Phytoplankton Absorption Using Mixture-of-Experts
As a key component of inherent optical properties (IOPs) in ocean color remote sensing, phytoplankton absorption coefficient (aphy), 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 aphy 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 aphy 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 aphy 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 aphy 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 aphy within an optically complex estuarine environment.
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- PAR ID:
- 10637576
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 17
- Issue:
- 12
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 2103
- Subject(s) / Keyword(s):
- hyperspectral imaging phytoplankton absorption mixture of experts artificial intelligence for geoscience
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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