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Chisom O. Emeghiebo

Publications and source records attributed to Chisom O. Emeghiebo.

2 recordsLinked to original sources

Rapid intensification of Karenia brevis red tides after hurricane Milton revealed by PACE-OCI hyperspectral unmixing

Estuarine and coastal systems, shaped by transient physical–biological interactions and recurrent harmful algal blooms, often undergo episodic shifts in phytoplankton community composition (PCC). We present PhytoUnmix , a deep learning spectral unmixing framework applied to NASA’s hyperspectral PACE (Plankton, Aerosol, Cloud, Ocean Ecosystem) mission, to resolve PCC dynamics during a red tide bloom following Hurricane Milton (2024) on the West Florida Shelf, USA. PACE-derived PCC, together with upper-ocean physical observations, revealed contrasting phytoplankton community patterns across distinct post-hurricane hydrographic conditions: offshore wind-curl induced upwelling exhibited higher relative abundance of diatoms, whereas nearshore regions affected by northerly winds and Ekman transport along with enhanced upwelling were associated with coastal Karenia brevis bloom. For the first time, synergistic observations using PACE and Surface Water and Ocean Topography (SWOT) showed a rapid rise in dinoflagellates nearshore, with concurrent declines in diatoms and chlorophytes, while cryptophytes co-increased. These transient shifts likely reflected not only physical stirring but also biological responses, as freshwater inputs increased nutrient availability, and coincided with intensified red tides and spatially heterogeneous phytoplankton responses, such as, initial increases in diatoms and cyanobacteria followed by Karenia brevis dominance. Together, PACE and SWOT provide unprecedented capacity to resolve physical–biological linkages in coastal ecosystems.

Florida↗

Hyperspectral retrieval of phytoplankton absorption and community composition from NASA’s PACE-OCI in estuarine–coastal waters using a hybrid framework combining mixture-of-experts and Variational Autoencoder

Retrieving the phytoplankton absorption coefficient (a phy ; m−1), one of the most spectrally rich inherent optical properties, remains challenging in optically complex coastal waters worldwide. Leveraging NASA's new hyperspectral mission, PACE, we introduce Hyper-MoE-VAE, a deep-learning architecture that integrates a Mixture-of-Experts with a Variational Autoencoder to retrieve high-dimensional a phy and subsequent estimation of phytoplankton community composition (PCC) from PACE-OCI hyperspectral remote sensing reflectance (R rs ). Pre-trained on global hyperspectral bio-optical datasets and fine-tuned using regional field R rs –a phy pairings from inland– estuarine–coastal waters, Hyper-MoE-VAE demonstrated strong transferability and effective adaptation across regions. Validation with in-situ Rrs showed accurate aphy retrievals in Lake Erie (NRMSE = 0.12, ε = 17.10), Lake Pontchartrain (NRMSE = 0.11, ε = 37.12), and the Barataria–Terrebonne Estuary (NRMSE = 0.14, ε = 38.89). Using same-day PACE-OCI Level 2 Rrs, the model achieved comparable performance in Lake Erie (NRMSE = 0.19, ε = 55.19), Lake Pontchartrain (NRMSE = 0.14, ε = 51.39), and the Barataria–Terrebonne Estuary (NRMSE = 0.17, ε = 47.92). Hyper-MoE-VAE derived PACE-OCI hyperspectral aphy was further decomposed against mass-specific absorption spectra to estimate group-specific contributions to total chlorophyll a. The resulting PCC showed strong agreement with HPLC–CHEMTAX in Lake Erie ( R 2 = 0.692) and Gulf estuarine–coastal systems ( R 2 = 0.732). Monte Carlo noise experiments further revealed group-dependent sensitivities, with diatoms and dinoflagellates showing moderate susceptibility to noise, while cyanobacteria and cryptophytes exhibited narrow uncertainty distributions. These results demonstrate Hyper-MoE-VAE's capability for regional, operational water-quality monitoring with PACE-OCI and its adaptability to current and future hyperspectral missions.

Great Lakes, Lake Pontchartrain↗