Yongquan Wang

Accurate satellite retrieval of oceanic particulate organic nitrogen (PON) concentrations could contribute to a better and more comprehensive understanding of global marine biogeochemical processes. However, no satisfactory satellite PON retrieval model could be found in the literature. With an attempt to develop applicable PON models, large diverse matchups of synchronous global oceanic in-situ PON measurements and ocean color satellite data were used to develop PON retrieval models using the Gaussian Process Regression (GPR) method for SeaWiFS, Terra-MODIS, MERIS, Aqua-MODIS and SNPP-VIIRS, respectively. The GPR PON models were compared with polynomial PON models based on single bio-optical properties or band index, and further used to retrieve spatiotemporal variations of global oceanic PON concentrations. Combined with the satellite-derived particulate organic carbon (POC) products, the possibility of deriving the particulate organic carbon to nitrogen ratio (POC:PON) was further explored. Results showed that GPR PON models, with R2, RMSE and MAPE ranging from 0.76 to 0.87, 0.15 to 0.18 and 9.19% to 12.59%, respectively, had a comparable performance for both Case-1 and Case-2 waters and outperformed the polynomial PON models. The global PON concentrations derived from different satellite missions were generally consistent, with their mean relative differences all less than 14.70% between Aqua-MODIS and the other four missions. The GPR-derived POC:PON was acceptable, with most relative errors within ±40% compared to in-situ POC:PON. The applicable satellite PON retrieval models and the readily available satellite PON products should be helpful for studying oceanic PON dynamics and ecological processes in marine biogeochemical cycles.

Accurate atmospheric correction (AC) is one fundamental and essential step for successful ocean colour remote-sensing applications. Currently, most ACs and the associated ocean colour remote-sensing applications are restricted to solar zenith angles (SZAs) lower than 70°. The ACs under high SZAs present degraded accuracy or even failure problems, rendering the satellite retrievals of water quality parameters more challenging. Additionally, the complexity of the bio-optical properties of the coastal waters and the presence of complex aerosols add to the difficulty of AC. To address this challenge, this study proposed an AC algorithm based on extreme gradient boosting (XGBoost) for optically complex waters under high SZAs. The algorithm presented in this research has been developed using pairs of Geostationary Ocean Colour Imager (GOCI) high-quality noontime remote-sensing reflectance (Rrs) and the Rayleigh-corrected reflectance (ρ_rc) derived from the Ocean Colour–Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) in the morning (08:55 LT) and at dusk (15:55 LT). The algorithm was further examined using the daily GOCI images acquired in the morning and at dusk, and the hourly (total suspended sediment) TSS concentration was also obtained based on the atmospherically corrected GOCI data. The results showed that: (i) the model produced an accurate fitting performance (R² ≥ 0.90, RMSD ≤ 0.0034 sr⁻¹); (ii) the model had a high validation accuracy with an independent dataset (R² = 0.92–0.97, MAPD = 8.2–26.81% and quality assurance (QA) score = 0.9–1); and (iii) the model successfully retrieved more valid Rrs for GOCI images under high SZAs and enhanced the accuracy and coverage of TSS mapping. This algorithm has great potential to be applied to AC for optically complex waters under high SZAs, thus increasing the frequency of available observations in a day.