Spatiotemporal analysis of ocean primary productivity in Bohai Sea estimated using improved DINEOF reconstructed MODIS data

Abstract

In this study, a novel multiple spatiotemporal data interpolating empirical orthogonal function (MS-DINEOF) method was employed to solve the problem of missing remote sensing data in the estimation of ocean primary productivity (OPP). The scheme was integrated with a vertically generalized productivity model (VGPM) for estimating OPP. First, a new time-scale feature was defined for effectively preserving spatiotemporal characteristics during the reconstruction of missing remote sensing data. The proposed algorithm, which integrates MS-DINEOF for reconstructing sea surface temperature, chlorophyll-a concentration, photosynthetically active radiation, and diffuse attenuation coefficient at 490 nm data, with VGPM for OPP estimation, was implemented for the Bohai Sea from 2010 to 2021. The main results are as follows: (1) The root mean square error values of the reconstructed data were all less than 0.1, and the absolute error values of the estimated OPP were even smaller. The quality of the reconstructed data using the MS-DINEOF algorithm was high, both for overall and local data. (2) The OPP in the Bohai Sea exhibited obvious seasonal fluctuations. (3) The spatial distribution of OPP exhibited regional characteristics over time. Specifically, OPP in the Bohai Sea showed a decreasing trend from the coastal sea to the distant sea during the periods 2010–2014, 2015–2019, and 2020–2021. The OPPs were higher in the coastal areas than in Bohai Bay and Laizhou Bay and gradually decreased from the coastal sea to the distant sea in July and August during 2015–2019.