Revisiting the Intraseasonal Variability of Chlorophyll-a in the Adjacent Luzon Strait With a New Gap-Filled Remote Sensing Data Set

Abstract

In the northern South China Sea of western Pacific Ocean during winter, clouds, sun glint, and other factors block optic sensors, leading to a high missing rate and hence a major concern in ocean color products such as the chlorophyll-a (CHL) data. These constraints inhibit the understanding of CHL variabilities at short (< seasonal) scales. Here, we introduce a new gap-filling method to reconstruct data gaps in a daily CHL remote sensing product. We applied discrete cosine transform with penalized least square (DCT-PLS) approach in the adjacent Luzon Strait, yielding a 15-year full-coverage daily 4-km CHL product. Against the cross-validation set and an independent observational data set collected from 34 cruises, evaluations suggest that DCT-PLS has outperformed the widely applied classical data-interpolating empirical orthogonal function (DINEOF) method. Besides, the DCT-PLS method is characterized by more efficient computation. The complete CHL product was analyzed with a particular focus on the intraseasonal (~30–60 days) control on the winter bloom by the Madden-Julian Oscillation (MJO). The MJO’s local signature on the CHL presents asymmetry. The CHL peaks at the late phases of MJO events, which could be explained by the relaxation after the MJO-induced wind strengthening. This gap-filling approach can be promisingly applied in other remote sensing gap-filling problems, which could shed light on the short-term variability of biological and physical dynamics in the ocean.