Remote sensing estimates of global sea surface nitrate: Methodology and validation.

Area of Science:

• Oceanography
• Biogeochemistry
• Remote Sensing

Background:

• Sea surface nitrate (SSN) is vital for oceanic productivity and carbon cycle studies.
• Developing accurate remote sensing algorithms for SSN is challenging due to its lack of optical properties and complex environmental interactions.
• Existing methods often rely on data-driven models lacking mechanistic understanding.

Purpose of the Study:

• To develop and validate a novel, widely applicable remote sensing inversion algorithm for estimating monthly average SSN on a global scale.
• To incorporate photosynthetically active radiation (PAR) into SSN estimation, recognizing its role in nitrate biogeochemical processes.
• To improve the mechanistic understanding of factors influencing SSN spatiotemporal dynamics.

Main Methods:

• An empirical algorithm was developed using monthly climatology data from the World Ocean Atlas 2018 (WOA18) for nitrate.
• The algorithm integrates estimated monthly sea surface temperature (SST) and PAR from MODIS, and mixed layer depth (MLD) from HYCOM.
• Global monthly average SSN was calculated on a 1° by 1° resolution grid.

Main Results:

• The study found that PAR potentially influences SSN concentrations.
• Validation with extensive measured nitrate data (N=12,846, 2018-2023) demonstrated high prediction accuracy (R²=0.93, RMSE=3.12 μmol/L, MAE=2.22 μmol/L).
• Independent validation and sensitivity tests confirmed the algorithm's reliability for SSN retrieval.

Conclusions:

• The developed algorithm provides a robust and accurate method for estimating global sea surface nitrate concentrations.
• This approach enhances the capability for large-scale oceanic productivity and carbon cycle research.
• The inclusion of PAR in the model offers new insights into nitrate biogeochemistry.