Estimation of sea surface nitrate from space: Current status and future potential

Area of Science:

• Oceanography
• Remote Sensing
• Biogeochemistry

Background:

• Sea surface nitrate (SSN) is vital for marine phytoplankton growth and oceanic new production.
• In-situ measurements of SSN are spatially and temporally limited, necessitating alternative data sources.
• Satellite remote sensing offers a valuable tool for broad spatial and temporal SSN assessment.

Purpose of the Study:

• To review and synthesize the progress of satellite-based sea surface nitrate (SSN) estimation techniques.
• To compare SSN retrieval methods in both open ocean and coastal environments.
• To analyze the evolution of SSN algorithms, their uncertainties, and influencing factors.

Main Methods:

• Comprehensive literature review of peer-reviewed studies on satellite SSN retrievals over the past 30 years.
• Synthesis of studies based on geographical areas (open vs. coastal oceans), input variables, regression models, and reported uncertainties.
• Analysis of empirical regression and machine learning approaches used for SSN estimation.

Main Results:

• Satellite SSN estimation primarily relies on empirical relationships between SSN and correlated environmental variables (e.g., sea surface temperature, chlorophyll-a, salinity).
• Regional SSN algorithms are more developed in coastal areas influenced by upwelling or riverine inputs.
• Published SSN algorithms exhibit a wide range of uncertainties (0.83–6.87 μmol/L), with recent studies showing significant reductions.

Conclusions:

• Satellite remote sensing is a critical tool for assessing sea surface nitrate dynamics, complementing sparse in-situ data.
• Advancements in algorithms and increased field data have led to reduced uncertainties in satellite-derived SSN.
• Further research integrating physical and biogeochemical processes can improve the accuracy of global SSN monitoring.