Assessing the Water Quality of Indian Sundarban Estuaries using Remote Sensing Techniques

Abstract Remote Sensing for water quality assessment is a newly emerging field. Although numerous studies have been conducted to evaluate the correlation between the Digital Number (DN) of bands and water quality parameters (WQP), further studies are required to make this methodology a robust low-cost technique for water quality assessment. With a brief case study of spatio-temporal analysis of WQPs of the Sundarban estuary, the current work explores the possibility of multispectral remote sensing for large-scale water quality monitoring. The study is about establishing empirical relationships between DN values (single, multiple or combination of many bands) with a limited number of in-situ measurements of WQPs such as Chlorophyll algae, Turbidity, pH, Salinity and Euphotic Depth. The study also assesses seasonal variation of WQPs for the period 2013-2014. Stepwise regressions have been performed to select the best predictors of each WQPs; afterwards, simple or multiple regression has been performed according to the result of stepwise regression. Temporal variation has been assessed for summer and winter using those predicted maps. The predictors of Chlorophyll-a, Euphotic Depth, pH, Salinity, Turbidity are B4/B5; B4/B6; B5/B7; B5/B7; B6/B7 for summer and B3/B5; B2/B3, B4; B1, B3/B6; (B2/B5); (B2/B5) for the winter. Three best model estimates are pH and chlorophyll-a of both the seasons with RMSEs of 0.202865, 0.059793, 0.477288568, and 0.224603275905043 respectively. Higher chlorophyll-a, pH and turbidity found in Hooghly estuary in both seasons, at Matla, Thakuran, Raimangal and Harinbhanga higher salinity found in both season and higher euphotic depth found in summer.