Sentinel-1 based Inland water dynamics Mapping System (SIMS). (March 2022)

• Record Type: Journal Article
• Authors: Soman, Manu K.; Indu, J.
• Is Part Of: Environmental modelling & software. Volume 149(2022)
• Publication Date: 2022-03
• Subjects: Sentinel-1 -- SWOT -- Inland water dynamics -- Web application -- Python -- Google earth engine; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105305
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This work introduces Sentinel-1 based Inland water dynamics Mapping System (SIMS), an open-source web application developed to enable automated mapping of inland water dynamics using Sentinel-1 radar imagery. SIMS relies on a novel framework built using Python and Google Earth Engine. The underlying algorithm involves a simple binary thresholding technique and an outlier removal method tailored to perform efficiently across complicated flow regimes. Results can be downloaded as numerical data or as time-series of shapefiles representing the variation of inland water extents. Exported geospatial datasets aid the pre-launch study of future Surface Water and Ocean Topography (SWOT) mission which is expected to deliver hydrological measurements at unprecedented spatial resolutions. Classification metrics are evaluated at 20 validation sites across the globe using Sentinel-2 based Modified Normalized Difference Water Index (MNDWI) images as reference. Results indicated high overall accuracy ranging from 84.16% to 99.47% for lakes and 87.23%–98.96% for rivers. Highlights: A new open-source web app for mapping dynamic inland water extents is presented. Application is programmed in Python using Sentinel-1 data from Google Earth Engine. Backend algorithm involves a novel framework configurable for rivers and lakes. Derived outputs can be exported as time series of surface water extent shapefiles. Results have huge potential to improve the pre-launch study of future SWOT mission.