Development of Chlorophyll-$a$ Algorithm for Ocean Colour Monitor Onboard OCEANSAT-2 Satellite

Abstract

An empirical chlorophyll algorithm has been developed using the coincident in situ chlorophyll-a and remote sensing reflectance Rrs measurements from global ocean waters. The basic data set used for developing the algorithm was obtained by merging the bio-optical data from the global NASA bio-Optical Marine Algorithm Data (NOMAD) (~2438 spectra from ~3000 stations) and from the waters of the northern Arabian Sea (~159 spectra) collected by the Space Applications Centre, Ahmedabad, India. The chlorophyll-a concentration ranged from 0.01 to 50.0 mg ldr m-3 for the data set used. Regression analysis between chlorophyll-a concentration and remote sensing reflectance in different bands and a combination of band ratios was performed. Algorithms using modified cubic polynomial (MCP) regression of Rrs ratios with chlorophyll-a concentration showed good estimates of chlorophyll-a in full range of 0.01 to 50.0 mg ldr m-3 of the merged data set. However, the best results were obtained by using MCP regression between maximum band ratio (MBR) of Rrs (443, 490, 510 nm)/Rrs 555 nm with chlorophyll-a concentration having an r2 of 0.96 and rms error of 0.12 for log-transformed data. The developed MBR-based algorithm named Ocean Colour Monitor (OCM)-2 chlorophyll algorithm was compared with the OC4v4 algorithm routinely used the for Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data processing. For the used data set, OC4v4 algorithm overestimated chlorophyll-a concentration for > 5.0 mg ldr m-3 and yielded an r2 of 0.90 with rms error of 0.23, when compared to the newly developed OCM-2 chlorophyll algorithm. It is proposed to use this OCM-2 chlorophyll algorithm with OCEANSAT-2 OCM data to be launched in the third quarter of the year 2008 by the Indian Space Research Organisation.