Modeling chlorophyll-a and turbidity in river Ganga (India) using Landsat-8 OLI imagery

What is it about?

Develop algorithms based on Landsat 8 OLI imagery and in-situ observations for prediction of Chl-a and turbidity in river Ganga, India. Band reflectance from multispectral Landsat-8 OLI images of May and October 2016 were used for model development and validation along with simultaneous ground truth data, and December 2016 image was used to predict chlorophyll-a and turbidity. The band ratio of B3/B2 (R2= 0.69) and log (B6/B2) (R2= 0.64) proved to be the best applicable algorithms for estimating chlorophyll-a. The best algorithms for turbidity were log (B7/B1) (R2= 0.73) and subtraction combination of bands B2-B6, B4-B5 and B4-B7 (R2= 0.89) for estimating turbidity, based on band combinations tested. The algorithms were used to generate maps showing the chlorophyll-a and turbidity concentration distribution in the study area. The predicted and estimated Chl-a and turbidity were then tested for accuracy assessment using the December 2016 dataset.

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Why is it important?

Rivers being one of the most complex ecosystems are highly variable both spatially and temporally. Chlorophyll-a (Chl-a) is considered one of the primary indicator for water quality and as a measure of river productivity, while turbidity in rivers is a measure of suspended organic matter. Monitoring of river parameters is challenging, demanding tremendous efforts and resources. Numerous algorithms have been developed in the past for deriving environmental parameters such as chlorophyll-a and turbidity from remote sensing imagery. However, most of these algorithms were focused on the lentic systems. There is paucity in such algorithms or methods from which water quality variables can be predicted using remotely sensed imagery.

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