Estimating particulate organic nitrogen concentrations in the surface ocean from ocean color remote sensing data

Yongquan Wang; Huizeng Liu; Guofeng Wu

doi:10.1117/12.2651527

20 January 2023 Estimating particulate organic nitrogen concentrations in the surface ocean from ocean color remote sensing data

Yongquan Wang, Huizeng Liu, Guofeng Wu

Author Affiliations +

Proceedings Volume 12561, AOPC 2022: Atmospheric and Environmental Optics; 1256106 (2023) https://doi.org/10.1117/12.2651527
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China

Abstract

Particulate organic nitrogen (PON) refers to the nitrogen contained in biological or other debris particles, and it plays important roles in the ecosystem functions and biogeochemical processes of the marine biology. This study aimed to explore the feasibility of retrieving oceanic PON concentration from ocean color remote sensing data, determine the bio-optical proxy for satellite PON retrieval, and develop satellite oceanic PON retrieval models for the global ocean. In situ PON data collected over the global ocean and MODerate-resolution Imaging Spectroradiometer (MODIS) Aqua Level-3 products were used. Three different types of models were tested: 1) apparent optical properties (AOPs)-based models, 2) inherent optical properties (IOPs)-based models, and 3) biological properties-based models. Results indicated that ocean color remote sensing could be used for oceanic PON concentration retrieval in the global ocean, and AOPs-based models calibrated produced better fitting performance than the other two types. In further studies, PON models will be used to produce the global ocean PON concentration products, and explore oceanic PON spatiotemporal variations and the underlying driving forces.

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Yongquan Wang, Huizeng Liu, and Guofeng Wu "Estimating particulate organic nitrogen concentrations in the surface ocean from ocean color remote sensing data", Proc. SPIE 12561, AOPC 2022: Atmospheric and Environmental Optics, 1256106 (20 January 2023); https://doi.org/10.1117/12.2651527

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KEYWORDS

Data modeling

Performance modeling

Nitrogen

Remote sensing

Satellites

Coastal modeling

MODIS

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