An Absorption-Based Model with Dynamic BIOMES for Improving Satellite Estimates of Global Ocean Net Primary Production for Carbon Cycling and Environmental Change Studies

Accurate and well-characterized regional, basin and global scale measurements of oceanic Net Primary Production (NPP) are critical for understanding the role and response of ocean ecosystems to rising atmospheric CO2 levels and global warming. Currently global NPP estimates from satellite observations, which underpin global ocean carbon cycling and environment studies, continue to suffer from substantial uncertainties due to several methodological and observational limitations. These include: (1) the reliance on satellite-derived phytoplankton biomass fields generated using algorithms that do not consistently achieve high accuracy across all regional and global scales; (2) limited measurements of phytoplankton photosynthetic quantum yields (ϕ), which are currently obtained primarily from research vessel observations; and (3) the lack of adequate methods for scaling local in-situ ϕ measurements to regional and basin-wide scales. To address these challenges, we have utilized the Absorption-based Productivity Model (AbPM), which leverages the inherent optical absorption properties of phytoplankton derived from remotely sensed reflectance, rather than relying on phytoplankton biomass as an input. Additionally, we apply a novel bio-optical classification framework, the Bio-Optical Measurement and Evaluation System (BIOMES) to scale sparse in-situ estimates of ϕ for deriving global maps of NPP. Finally, using a global collection of in-situ NPP datasets we assess the performance of AbPM and those more widely used biomass-based models.