Performance of Semianalytical Algorithm and Associated Inherent Optical Properties in Coastal Waters of North Western Bay of Bengal

Abstract

The present study was carried out with an aim to assess two semianalytical algorithms, Garver–Siegel–Maritorena (GSM) and Generalized IOP (GIOP) and their association with Inherent Optical Properties (IOP) such as phytoplankton absorption coefficient (aph), detritus absorption (adg) and particulate backscattering (bbp). Ten years of Moderate Imaging Spectroradiometer-Aqua (MODISA) data was processed to retrieve Chlorophyll_a (Chl_a) from GSM and GIOP. Subsequently the Rrs from MODISA was inverted to compute aph, adg and bbp. Both the algorithms showed similar trend at an annual scale. However in magnitude, Chl_a from GSM was three times higher than that from GIOP. Further the Chl_a retrieved from GSM was closer to in situ values. The variability of aph and adg was similar at long-term time scale. The peak observed in variability of aph and adg during pre-monsoon was due to the occurrence of bloom whereas the peak during monsoon was due to nutrient discharge from river. The observed peak in bbp during monsoon can be also attributed to the increasing concentration of total particulate matter from river as well as from bottom re-suspension. The poor association of IOP with Chl_a explains the limited accuracy of satellite retrieved Chl_a from these semianalytical algorithms. Hence it is require generating IOP at the regional scale and tuning the semianalytical model for better accuracy.