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

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 oce...

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Bibliographic Details
Published in:Journal of the Indian Society of Remote Sensing 2021, Vol.49 (6), p.1419-1425
Main Authors: Jayaram, Chiranjivi, Pavan Kumar, J., Udaya Bhaskar, T. V. S., Bhavani, I. V. G., Prasad Rao, T. D. V., Nagamani, P. V.
Format: Article
Language:English
Subjects:
Annual variations
Carbon cycle
Chlorophyll
Color
Earth and Environmental Science
Earth Sciences
Empirical analysis
Food chains
Ocean color
Oceans
Orthogonal functions
Reconstruction
Remote sensing
Remote Sensing/Photogrammetry
Research Article
Satellites
Spatial data
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Summary: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.
ISSN:0255-660X
0974-3006
DOI:10.1007/s12524-021-01317-6