Reconstruction of Gap-Free OCM-2 Chlorophyll-a Concentration Using DINEOF

Abstract

Primary productivity plays a pivotal role in the global carbon cycle and the marine food chain. Chlorophyll-a concentration (chl-a) is considered as a proxy for the biomass, which requires reliable estimation. Remote sensing of ocean colour provides a valuable source of chl-a over the global oceans. However, the ocean colour measurements are often hindered by the presence of clouds, thereby denying information on various spatial scales. Data Interpolating Empirical Orthogonal Function (DINEOF) is a robust technique to reconstruct the data in gaps. The present study demonstrates the application of DINEOF-based reconstruction of the chl-a concentration data derived from the Ocean Colour Monitor-2 (OCM-2) onboard Oceansat-2 satellite for the period 2016–2019 over the northern Indian Ocean. The reconstructed and raw chl-a (Level-2) from OCM-2 are compared and found to be comparing well with a correlation of 0.93 and 0.9 in the Arabian Sea and Bay of Bengal, respectively. The seasonal and inter-annual variability of chl-a over the study region is examined to showcase the application of reconstructed data for spatio-temporal analysis on different scales.