Bingqing Liu Jiang Li Yuanheng Xiong Eurico J. D'Sa Melissa Millman Baustian Xiaodong Zhang Brice K. Grunert Chisom O. Emeghiebo Cassie Glasspie Xu Yuan Xingyu Bai 2026 <p>Retrieving the phytoplankton absorption coefficient (a_<i>phy</i>; 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_<i>phy</i>&nbsp;and subsequent estimation of phytoplankton community composition (PCC) from PACE-OCI hyperspectral remote sensing reflectance (R_<i>rs</i>). Pre-trained on global hyperspectral bio-optical datasets and fine-tuned using regional field R_<i>rs</i>–a_<i>phy</i>&nbsp;pairings from inland– estuarine–coastal waters, Hyper-MoE-VAE demonstrated strong transferability and effective adaptation across regions. Validation with in-situ Rrs&nbsp;showed accurate aphy&nbsp;retrievals in Lake Erie (NRMSE&nbsp;=&nbsp;0.12, ε = 17.10), Lake Pontchartrain (NRMSE&nbsp;=&nbsp;0.11, ε = 37.12), and the Barataria–Terrebonne Estuary (NRMSE&nbsp;=&nbsp;0.14, ε = 38.89). Using same-day PACE-OCI Level 2 Rrs, the model achieved comparable performance in Lake Erie (NRMSE&nbsp;=&nbsp;0.19, ε = 55.19), Lake Pontchartrain (NRMSE&nbsp;=&nbsp;0.14, ε = 51.39), and the Barataria–Terrebonne Estuary (NRMSE&nbsp;=&nbsp;0.17, ε = 47.92). Hyper-MoE-VAE derived PACE-OCI hyperspectral aphy&nbsp;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 (<i>R</i>²= 0.692) and Gulf estuarine–coastal systems (<i>R</i>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.</p> application/pdf 10.1016/j.rse.2026.115327 en Elsevier 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 article