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The role of COVID-19 pandemic lockdown in improving air quality was reported extensively for land regions globally. However, limited studies have explored these over oceanic areas close to high anthropogenic activities and emissions. The Bay of Bengal (BoB) basin is one such region adjacent to the highly populated South Asian region. We find that Aerosol Optical Depth (AOD) over the BoB declined by as much as 0.1 or 30% during the peak lockdown of April 2020 compared to long-term climatology during 2003-2019. Simultaneously, the sea surface temperature (SST) rose by 0.5-1.5°C over the central and north-western parts of the BoB with an average increase of 0.83°C. We show that up to 30% of this observed warming is attributable to reduced atmospheric aerosols. The study highlights the importance of anthropogenic emissions reduction due to COVID lockdown on short-term changes to SST over ocean basins with implications to regional weather.

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 color 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 Color (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 toward estimation of Chl-a in the coastal oceans utilizing available better spatially resolved sensors.