Ocean iron fertilization (OIF) is a proposed geoengineering strategy to enhance the biological pump, a natural process by which carbon dioxide (CO2) is transported from the surface ocean to the deep sea. The underlying principle is straightforward: by introducing iron—a limiting nutrient in vast oceanic regions—phytoplankton growth is stimulated, leading to increased photosynthetic carbon fixation. A fraction of this carbon is subsequently exported to the deep ocean via sinking organic matter, effectively sequestering it from the atmosphere for extended periods.
While OIF has been explored in small-scale experiments, scaling it up requires robust monitoring to assess its efficacy and potential ecological impacts. Traditional methods, such as ship-based sampling, are limited in spatial and temporal coverage. Satellite hyperspectral imaging offers a powerful alternative, enabling continuous, high-resolution observations of phytoplankton blooms and carbon export dynamics in iron-enriched zones.
Hyperspectral imaging (HSI) captures reflected solar radiation across hundreds of narrow spectral bands, allowing for detailed discrimination of oceanographic features. Unlike multispectral sensors, which measure broad wavelength ranges, HSI provides fine spectral resolution that enhances the detection of phytoplankton pigments, dissolved organic matter, and particulate carbon.
Carbon export efficiency (Cee)—the fraction of primary production that sinks to depth—is critical for assessing sequestration potential. Satellite-derived proxies for Cee include:
Conducted in 1999, SOIREE demonstrated that iron addition could stimulate diatom-dominated blooms. Retrospective analysis of SeaWiFS data revealed a Chl-a increase from 0.2 to 1.5 mg/m³ within two weeks. However, export efficiency remained low (~5%), likely due to zooplankton grazing and particle remineralization.
LOHAFEX targeted a silicate-limited region, resulting in a mixed phytoplankton community. MODIS-Aqua detected bloom expansion over 300 km² but minimal deep carbon flux, underscoring the importance of nutrient co-limitations.
Convolutional neural networks (CNNs) are being trained on hyperspectral datasets to automate bloom detection and carbon flux prediction. For example, a 2022 study achieved 89% accuracy in classifying iron-stimulated blooms using Sentinel-3 OLCI data.
Satellite observations alone cannot assess ecosystem impacts, such as shifts in microbial communities or potential hypoxia from decaying blooms. In situ validation remains essential.
Cloud cover and orbital mechanics limit daily global coverage. Geostationary sensors (e.g., GOCI-II) partially address this but lack hyperspectral capability.
Atmospheric correction over Case-II waters (influenced by sediments/CDOM) introduces errors in Chl-a retrievals. Hybrid approaches combining radiative transfer models and empirical algorithms show promise.
A holistic monitoring framework for OIF should merge:
International regulations under the London Convention/London Protocol currently restrict large-scale OIF. Robust satellite-based monitoring could inform future guidelines by providing transparency and accountability for experimental deployments.
Satellite hyperspectral imaging represents a transformative tool for assessing the carbon sequestration potential of ocean iron fertilization. By quantifying phytoplankton responses and export efficiency across spatial scales, it bridges the gap between small-scale experiments and global climate solutions. However, technological advancements must be coupled with ecological prudence to ensure that OIF, if ever deployed at scale, is both effective and environmentally sound.