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

Sea surface nitrate (SSN) plays an important role in assessing phytoplankton growth and new production in the ocean. Field sampling of SSN data is important, but limited by data quantity both spatially and temporally. Satellite remote sensing can contribute through providing spatial and temporal data to such assessments. During the past 30 years many studies have been published focusing on SSN retrievals from satellites to a greater or less extent. In this study, we reviewed the progresses of SSN estimation from satellites in both open ocean and coastal waters. Because of the lack of electromagnetic properties of SSN, satellite retrievals of SSN were most realized by developing relationships between SSN and related environmental variables (e.g., sea surface temperature, chlorophyll-a concentration, sea surface salinity), using traditional empirical regressions and novel machine learning techniques. We synthesized most of the peer-reviewed studies for both open and coastal oceans, in terms of study areas, model inputs, regression formulas, and model uncertainties. In general, regional SSN algorithms were most developed in coastal oceans with upwelling or river discharges. The published SSN algorithms had varying uncertainties with a wide range of 0.83-6.87 μmol/L, and the uncertainties were significantly reduced in recent studies, with more field measurements available and better understanding of the physical and biogeochemical processes in driving nitrate dynamics.