Reconstructing Global Ocean Color Data at High Temporal Resolution Using an Improved DINEOF Algorithm

Global studies on phytoplankton phenology remain challenging primarily because of sparse observations. The Data Interpolation Empirical Orthogonal Function (DINEOF) algorithm has been used successfully to reconstruct datasets of geophysical and biological variables such as sea surface temperature (SST) and Chlorophyll a (Chl a). We propose an improved version of DINEOF, DINEOF+, based on a comprehensive validation that show DINEOF+ is an appropriate method for studying chlorophyll a (Chl a) concentrations in the ocean. Error analysis reveals that 75 percent of missing data (PMD) is a reasonable threshold for applying DINEOF+ to reconstruct incomplete datasets. DINEOF+ improves accuracy over the original DINEOF by integrating connectivity filter that further reduces the errors caused by missing data. We successfully apply DINEOF+ to the OC-CCI global daily Chl a dataset. We find that the recovery rate varies across ocean basins and years. In oligotrophic waters, the daily data coverage increased by 40–50% during the period from 2003 to 2020. Using DINEOF+ allows us to obtain a significantly higher temporal resolution of global Chl a data, which will improve understanding of marine phytoplankton dynamics in response to changing environments.