Satellite-Estimation of the Global Ocean Primary Productivity via BGC-Argo Measurements

Abstract

Monitoring the global ocean net primary productivity (NPP) has been the primary objective of satellite ocean color remote sensing since the launch of the world's first satellite ocean color sensor in 1978. However, considerable discrepancies persist in current satellite NPP estimations, and the use of limited in situ data is one of the key challenges for accurate NPP estimation. Here, a global depth-resolved NPP profile (NPP_re) data set was constructed on the basis of 10 years of biogeochemical Argo (BGC-Argo) measurements and a tuned carbon-based productivity model (BGC_CbPM). On the basis of this data set and the XGBoost machine learning model, a global oceanic NPP_re remote sensing inversion model (XGBoost_CbPM) was established for NPP estimation from Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua data. Validation with 14 independent samples revealed a coefficient of determination of 0.87 and a mean absolute percentage deviation of 12.52% between the model predictions and in situ measurements. In addition, the results obtained via the XGBoost_CbPM model suitably agreed with the in situ measurements at two time series stations, namely BATS and HOT. In particular, the time series changes in the NPP derived by the XGBoost_CbPM model at the BATS and HOT stations were better than those of the original satellite products based on the carbon-based productivity model. More importantly, as a depth-resolved model, the XGBoost_CbPM model can provide NPP profiles that are superior to those of traditional NPP models, which can be used to estimate only the water column-integrated NPP. This study underscores the significant contribution of BGC-Argo measurements in enhancing the satellite estimation of the global ocean NPP.