Data reconstruction of daily MODIS chlorophyll-a concentration and spatio-temporal variations in the Northwestern Pacific.

Sea surface chlorophyll-a concentration (Chl-a) is a key proxy for phytoplankton biomass. Spatio-temporal continuous Chl-a data are important to understand the mechanisms of chlorophyll occurrence and development and track phytoplankton changes. However, the greatest challenge in utilizing daily Chl-a data is massive missing pixels due to orbital position and cloud coverage. This study proposes the application of a spatial filling method using the machine learning-based Extreme Gradient Boosting (BST) to reconstruct missing pixels of daily MODIS Chl-a data from 2007 to 2018. The approach is applied to different trophic biogeographical subregions of the Northwestern Pacific where it has complex phytoplankton dynamics and frequent data missing. Various environmental variables are taken into consideration, including meteorological forcing, geographic and topographic features, and oceanic physical components. The BST-reconstructed Chl-a (BST Chl-a) is validated using in-situ Chl-a measurements, VIIRS and Himawari-8 Chl-a products. The results show that the BST model is highly adaptive in reconstructing Chl-a data, and it performs well in pelagic, offshore and coastal with the best performance in pelagic. BST Chl-a improves coverage without significant quality degradation compared to the original MODIS Chl-a. BST Chl-a agrees better with in-situ data than that of MODIS, with CC of 0.742, RMSE of 0.247, MAE of 0.202 and Bias of 0.089. Cross-satellite validation using VIIRS and Himawari-8 Chl-a also shows promising results with the CC of 0.861 and 0.765, respectively, suggesting the high accuracy of BST Chl-a. The inter-annual trend of BST Chl-a decreases in coastal and increases in offshore and pelagic. BST Chl-a images present similar spatial patterns to MODIS Chl-a under different missing rates, with gradual decreases from coastal to pelagic. It indicates that phytoplankton bloom patterns can be identified by daily BST Chl-a images.