Beyond chlorophyll: machine learning estimates of diagnostic phytoplankton pigments from multispectral ocean colour data
2026-08-24 • Machine Learning
Machine Learning
AI summaryⓘ
The authors looked at how well machine learning can estimate important pigments in phytoplankton from satellite data, beyond just measuring chlorophyll-a, which is commonly used but limited. They used a large global dataset combining pigment measurements with satellite reflectance data and found that models using multiple spectral bands predicted pigment levels better than those using chlorophyll-a alone. However, the improvement varied depending on the pigment, especially if it was closely related to chlorophyll-a or not. Their work shows that machine learning can help us better understand phytoplankton communities from space.
PhytoplanktonChlorophyll-aAccessory pigmentsOcean colour remote sensingMachine learningRandom ForestMultispectral reflectanceHigh Performance Liquid Chromatography (HPLC)Pigment concentrationBiogeochemical processes
Authors
David Moffat, Angus Laurenson, Victor Martinez-Vicente, Gemma Kulk, Xuerong Sun, Robert J. W. Brewin, Shubha Sathyendranath
Abstract
Phytoplankton play a central role in marine ecosystems and the global carbon cycle, with different groups contributing differently to ocean biogeochemical processes. While standard techniques exist for monitoring phytoplankton concentration from ocean-colour data, their community composition remains difficult to observe at large scales. Chlorophyll-a, widely available from satellite ocean-colour observations, is commonly used as a measure of phytoplankton biomass but provides limited information on taxonomic composition. Accessory pigments, some of which are diagnostic of important phytoplankton groups, offer additional information on community structure, but their retrieval from ocean-colour data is challenging because of limited spectral resolution and strong covariance with chlorophyll-a. In this study, we evaluate machine learning methods for estimating diagnostic pigment concentrations from multispectral satellite observations. Using a global dataset of 33,640 High Performance Liquid Chromatography (HPLC) measurements matched with ESA Ocean Colour Climate Change Initiative (OC-CCI) reflectance data, we compare Random Forest and TabPFN models trained on multispectral reflectance with baseline models using chlorophyll-a alone. A temporally stratified validation scheme is employed to reduce the effects of autocorrelation. Results show that multispectral models consistently outperform approaches based solely on satellite-derived chlorophyll-a, demonstrating that ocean-colour reflectance contains additional information relevant to pigment discrimination. Improvements vary by pigment, with those strongly correlated with chlorophyll-a showing limited gains, while others exhibit substantial improvement. These findings highlight the potential of machine learning to extract ecologically relevant information from satellite data beyond conventional chlorophyll-based approaches.