Quality monitoring of inland water bodies using Google Earth Engine

Author:

Sherjah P. Y.12ORCID,Sajikumar N.13,Nowshaja P. T.1

Affiliation:

1. a Department of Civil Engineering, Government Engineering College, Thrissur, Kerala, India

2. b A. P. J. Abdul Kalam Technological University, Thiruvananthapuram, Kerala, India

3. c WRPM Consultants, Thrissur, Kerala 80010, India

Abstract

Abstract Regular quality monitoring of inland water bodies is vital for identifying the areas with deteriorating water quality. Satellite remote sensing has been used for obtaining long-term trends that require the processing of many images. The computational load of processing a large number of satellite imageries can be eased by utilizing the cloud computing capabilities of Google Earth Engine (GEE). The present study explores the possibility of using the GEE platform for mapping the Trophic State Index (TSI) of an inland water body. The bottom of atmosphere (BOA) reflectance retrieved by the SIAC algorithm (used in the GEE platform) is assessed for its accuracy. The algorithm could retrieve only BOA reflectance at bands B3 and B4 of Sentinel 2L1C (S2) with reasonable accuracy. The study has identified the Normalized Difference of B3 and B4 bands of S2 (i.e., ND34) as the tool for mapping TSI of a water body using GEE. TSI from six imageries of three lakes was estimated with a mean error <17%. The capability of GEE as a rapid water quality monitoring tool is demonstrated by displaying the temporal and spatial variations of water quality across Vembanad Lake for the period 2016–2021.

Publisher

IWA Publishing

Subject

Atmospheric Science,Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering,Water Science and Technology

Reference32 articles.

1. Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2020

2. Automated method for monitoring water quality using landsat imagery;Water (Switzerland),2016

3. Comparison of satellite reflectance algorithms for estimating turbidity and cyanobacterial concentrations in productive freshwaters using hyperspectral aircraft imagery and dense coincident surface observations;Journal of Great Lakes Research,2019

4. A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans;Progress in Oceanography,2014

5. Google earth engine tools for long-Term spatiotemporal monitoring of Chlorophyll-a concentrations;Open Water Journal,2021

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