Where Sky Meets Sea: Integrated Aerosol and Ocean Color Retrieval from PACE SPEXone Multi-Angle Polarimetry
Abstract
Global aerosol and ocean color properties are simultaneously retrieved from multi-angle polarimetric measurements acquired by the SPEXone instrument aboard NASA’s PACE mission. These coupled retrievals are enabled by the FastMAPOL algorithm, which combines optimal estimation with deep neural network forward models to achieve efficient and accurate operational processing. Ocean remote-sensing reflectance (Rrs) is derived through multi-angle atmospheric correction, introducing an explicit angular dimension for characterizing aquatic optical properties. Since early 2024, both Level-2 and Level-3 global products have been generated in near real time and are publicly distributed through the NASA Earthdata archive.Validation against the Aerosol Robotic Network (AERONET) and associated AERONET-Ocean Color (OC) observations demonstrates robust performance for both atmospheric and oceanic retrievals. Aerosol optical depth (AOD) uncertainties follow approximately 0.01 + 0.15 AOD with mean biases smaller than 0.01. Retrieved (Rrs) exhibits uncertainties of approximately 0.001-0.002 sr-1 and biases on the order of 0.001 sr-1 across phytoplankton-dominated and optically complex waters. Partial spectral compensation between AOD and Rrs biases is observed. Global gridded analyses reveal realistic spatial patterns of aerosol type, absorption, and ocean color variability, along with indications of coupled aerosol–ocean uncertainty behavior. The angular variability of multi-angle Rrs is further examined across latitudes and seasons, including a closure assessment based on a self-consistent bidirectional reflectance correction.PACE multi-angle polarimetry thus establishes a new capability for fully integrated global aerosol and ocean color retrieval, enhancing our ability to characterize aerosol–ocean interactions and advance understanding of the coupled Earth system.
Fields of Study & Subjects
Marine and coastal ecosystems
Remote Sensing in Agriculture
Atmospheric aerosols and clouds
Publication details
- Journal / source
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Publication date
- 1 January 2026
- Document type
- Preprint