Mapping surface-water area using time series landsat imagery on Google Earth Engine: a case study of Telangana, India

Abstract

The extent of surface-water spread influences the hydrogeology and ecology of waterbodies. Remote sensing technology provides spatial and temporal datasets which aid in mapping the dynamics of surface waterbodies at the regional and global scale. In the present study, temporal changes in the surface area of waterbodies in Telangana, India, were monitored using indices like normalized difference vegetation index, normalized difference water index and modified NDWI and machine learning algorithms like a random forest using Landsat-8 data. Google Earth Engine cloud computing platform was used for processing earth observation data, based on the time series images of Landsat and compared with real-time groundwater levels. The results showed a significant increase (P < 0.01) in both surface-water area and groundwater levels in Telangana, especially after 2015, which we hypothesize could be due to the specialized water conservation project being implemented by the Government of Telangana since 2015.