Remote Estimations of Total Alkalinity and Total Dissolved Inorganic Carbon in the Yellow and East China Seas Using Machine Learning Approach

The sea surface total alkalinity (A_T) and total dissolved inorganic carbon (C_T) are two essential carbonate variables to understand the marine carbon cycle, yet it has been a big challenge to retrieve A_T and C_T from space for the Yellow and East China Seas (YECS) owing to they are affected coupling by physical and biogeochemical processes and the heterogeneous coastal environments. To address these challenges, we developed multilayer perceptron neural network (MPNN)-based A_T and C_T models with the field measured environmental variables as the model predictors, and obtained a root mean square difference (RMSD) of 27.05 μmol/kg and coefficient of determination (R²) of 0.91 for A_T (N = 1,520) and a RMSD was 28.31 μmol/kg and R² was 0.88 for C_T (N = 513). Further, the MPNN-based model showed much promise in remotely retrieving surface A_T and C_T with the spatial resolution of ∼1 km in the YECS with a RMSD of 26.59 μmol/kg, R² of 0.76 for A_T and a RMSD of 37.14 μmol/kg, R² of 0.79 for C_T. Applying the MPNN-based model to the Moderate Resolution Imaging Spectroradiometer (MODIS) products, retrieved the monthly distributions of A_T and C_T over the past 20 years for the first time, demonstrated strong linkages to water masses circulations, upwelling and biological processes with seasonal cycles. Also, the interannual variations of A_T and C_T had significant relationships with the environmental proxies, as well as climate indices (North Pacific Gyre Oscillation). This work advances understanding of coastal carbon cycling and offers a valuable tool for large-scale, high spatial-temporal resolution monitoring of carbon dynamics.