AUTHOR=Wang Yongquan , Liu Huizeng , Wu Guofeng TITLE=Satellite retrieval of oceanic particulate organic nitrogen concentration JOURNAL=Frontiers in Marine Science VOLUME=9 YEAR=2022 URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2022.943867 DOI=10.3389/fmars.2022.943867 ISSN=2296-7745 ABSTRACT=

Over the past several decades, satellite ocean color remote sensing has greatly improved our understanding of the biogeochemical properties of the global ocean. 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. However, few studies have focused on the satellite retrieval of oceanic PON concentrations. With an attempt to fill the gap, this study aimed to explore the feasibility of retrieving oceanic PON concentrations from remote sensing data, determine the bio-optical proxies for satellite PON retrievals, 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) Level-3 products were used. Three different types of models were tested: (1) apparent optical property (AOP)-based models, 2) inherent optical property (IOP)-based models, and 3) biological property-based models. Results showed that ocean color remote sensing could be used for oceanic PON concentration retrieval in the global ocean, and AOP-based models calibrated produced better fitting performance than the other two types; models based on blue-to-green band ratio (BG) and normalized difference nitrogen index (NDNI) produced comparable and better fitting and validation performance; and IOP-based and biological property-based models produced lower but also acceptable performance. With the PON models developed, the monthly variations of PON concentrations in the global ocean were also explored. In further studies, PON models will be used to explore oceanic PON spatiotemporal variations and the underlying driving forces.