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.

The Ross Sea is the most productive marginal sea in the Southern Ocean and plays an important role in carbon cycling. However, limited sampling of Chlorophyll‐a (Chl) and particulate organic carbon (POC) concentrations from research expeditions constrains our understanding of the biogeochemical processes there. Satellites provide a useful tool for synoptic mapping of surface water properties on regional and global scales, yet the general applicability of the published algorithms in the Ross Sea is poorly known. Based on the data collected from 18 cruises in the past 20 years, we analyzed both the NASA standard and locally developed Chl and POC algorithms applicable to the Ross Sea. Our results show that Chl and POC are markedly underestimated using the NASA standard algorithms, with root mean square difference (RMSD) of 4.72 mg m⁻³ and 218.0 mg m⁻³, and mean bias of −3.48 mg m⁻³ and −159.1 mg m⁻³, for a wide range of Chl (0.42–16.3 mg m⁻³) and POC (46.8–812 mg m⁻³). Similar poor performances were also found for other algorithms applicable in the Ross Sea. We locally tuned both Chl and POC algorithms, and found that the Rrs667‐based approach showed the most robust performances in retrieving both Chl and POC, with improved RMSD of 2.86 mg m⁻³ and 129.7 mg m⁻³, and limited biases. Our results show that the algal bloom signals in the Ross Sea in terms of Chl and POC are significantly greater than previously determined. More field observations will further constrain the locally tuned algorithms.

Phytoplankton in the northwest Pacific plays an important role in absorbing atmospheric CO₂ and promoting the ocean carbon cycle. However, our knowledge on the long-term interannual variabilities of the phytoplankton biomass in this region is quite limited. In this study, based on the Chlorophyll-a concentration (Chl-a) time series observed from ocean color satellites of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) in the period of 1997–2020, we investigated the variabilities of Chl-a on both seasonal and interannual scales, as well as the long-term trends. The phytoplankton Chl-a showed large spatial dynamics with a general decreasing pattern poleward. The seasonal phytoplankton blooms dominated the seasonal characteristics of Chl-a, with spring and fall blooms identified in subpolar waters and single spring blooms in subtropical seas. On interannual scales, we found a Chl-a increasing belt in the subpolar oceans from the marginal sea toward the northeast open ocean waters, with positive trends (~0.02 mg m⁻³ yr⁻¹, on average) in Chl-a at significant levels (p < 0.05). In the subtropical gyre, Chl-a showed slight but significant negative trends (i.e., <−0.0006 mg m⁻³ yr⁻¹, at p < 0.05). The negative Chl-a trends in the subtropical waters tended to be driven by the surface warming, which could inhibit nutrient supplies from the subsurface and thus limit phytoplankton growth. For the subpolar waters, although the surface warming also prevailed over the study period, the in situ surface nitrate reservoir somehow showed significant increases in the targeted spots, indicating potential external nitrate supplies into the surface layer. We did not find significant connections between the Chl-a interannual variabilities and the climate indices in the study area. Environmental data with finer spatial and temporal resolutions will further constrain the findings.