Abstract
Chlorophyll-a can be used as a proxy for phytoplankton and thus is an essential water quality parameter. The presence of phytoplankton in the ocean causes selective absorption of light by chlorophyll-a pigment resulting in change of the ocean color that can be identified by ocean colour remote sensing. The accuracy of chlorophyll-a concentration (Chl-a) estimated from remote sensing sensors depends on the bio-optical algorithm used for the retrieval in specific regional waters. In this work, it is attempted to estimate Chl-a from two currently active satellite sensors with relatively good spatial resolutions considering ocean applications. Suitability of two standard bio-optical Ocean Colour (OC) Chlorophyll algorithms, OC-2 (2-band) and OC-3 (3-band) in estimating Chl-a for turbid waters of the northern coastal Bay of Bengal is assessed. Validation with in-situ data showed that OC-2 algorithm gives an estimate of Chl-a with a better correlation of 0.795 and least bias of 0.35 mg/m3. Further, inter-comparison of Chl-a retrieved from the two sensors, Landsat-8 OLI and Sentinel-2 MSI was also carried out. The variability of Chl-a during winter, pre-monsoon and post-monsoon seasons over the study region were inter-compared. It is observed that during pre-monsoon and post-monsoon seasons, Chl-a from MSI is over estimated compared to OLI. This work is a preliminary step towards estimation of Chl-a in the coastal oceans utilizing available better spatially resolved sensors.
Highlights
Ocean color remote sensing involves estimation of the concentration of substances in ocean by measuring variations in spectral quality of the water surface (IOCCG, 2000)
Two bio-optical algorithms, Ocean Color (OC)-2 and OC-3 were used to assess the performance of Chl-a estimation from fairly good-spatial resolution sensors Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Imager (MSI)
Results showed that the OC-2 algorithm was able to estimate Chl-a in the northern coastal region reasonably well and was performing better than OC-3 algorithm for OLI
Summary
Ocean color remote sensing involves estimation of the concentration of substances in ocean by measuring variations in spectral quality of the water surface (IOCCG, 2000). Ocean color is determined by the incident light interactions with substances such as suspended sediments, chromophoric or colored dissolved organic matter (CDOM) and Chlorophyll-a concentration (Chl-a) present in the water. These substances present in the ocean surface waters are quantified by the water leaving radiance measured in the visible portion of the electromagnetic radiation. The spatial variability of the phytoplankton distribution indicated by Chl-a ocean color data has aided in the analysis of biological productivity as well as efficient identification of the potential fishing zones
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