Phytoplankton community composition impacts food webs, climate, and fisheries on regional and global scales, and can be assessed at coarse taxonomic resolution from biomarker pigments measured using high-performance liquid chromatography (HPLC). Presently, satellite ocean color provides unprecedented coverage of the global surface ocean and offers reliable estimates of bulk biological properties; however, existing multispectral sensors have limited ability to provide information about phytoplankton community composition. Satellite ocean color at hyperspectral resolution (e.g., NASA's upcoming Plankton, Aerosol, Cloud, and ocean Ecosystem sensor, PACE) is expected to improve estimates of phytoplankton community composition from space. Phytoplankton impact ocean color via contributions to absorption and fluorescence (through phytoplankton pigments) and scattering, especially on narrow spectral scales (5–100 nm). Here, a global open ocean dataset of concurrent HPLC pigments and hyperspectral remote sensing reflectance (Rrs(λ)) observations is used to model phytoplankton pigment composition from optical data. Phytoplankton pigments are reconstructed from Rrs(λ) using optimized principal components regression modeling. This work demonstrates that thirteen phytoplankton pigments, representing five phytoplankton pigment groups (e.g., diatoms, dinoflagellates, haptophytes, green algae, and cyanobacteria), can be modeled from hyperspectral Rrs(λ). Spectral information needed to model each phytoplankton pigment concentration is found throughout the entire visible spectrum and the model results are best at high spectral resolution (≤5 nm). The resulting model recreates observed relationships among pigment concentrations, providing support for the designation of five pigment-based phytoplankton groups for the global open ocean. This work represents a step toward developing robust, global spectral models for phytoplankton pigment composition. However, more high-quality data from a wide range of ecosystems and environments are still needed to achieve this goal.