51 / 2023-07-24 15:08:07

Remote estimation of sea surface nitrate from ocean color

sea surface nitrate, satellite remote sensing, SST, Chlorophyll, northwest Pacific

Sea Surface nitrate (SSN) plays an important role in assessing phytoplankton growth and new production in the ocean, yet it has been a challenging task to estimate SSN from satellites due to its complex and varying relationship with different environmental proxies. We addressed this problem for the northwest Pacific by developing a stacking-random-forest (SRF) based SSN retrieval algorithm for Moderate Resolution Imaging Spectroradiometer (MODIS). It allows estimating SSN from daily sea surface temperature (SST) and Chlorophyll-a concentration (Chl) at a spatial resolution of 4 km. The model was constrained with extensive historical field SSN observations. For SSN ranging between 0.0005 and 25.88 μmol/kg (N=3452), the model had a root mean square difference of 1.34 μmol/kg (5.3%) and coefficient of determination of 0.92 with little bias. Further independent model validation and sensitivity tests demonstrated the validity of the SRF algorithm in retrieving SSN. Using this novel SSN data record from MODIS, for the first time, we investigated the SSN interannual variabilities and trends from satellite remote sensing.