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

• Published in: Environmental modelling & software : with environment data news Vol. 149; p. 105305
• Main Authors: Soman, Manu K., Indu, J.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.03.2022; Elsevier Science Ltd
• Subjects: Algorithms; Applications programs; automation; computer software; Google earth engine; Hydrologic measurement; Hydrology; Inland water dynamics; Inland waters; Internet; Lakes; Mapping; Outliers (statistics); Python; radar; Radar imaging; Sentinel-1; shapefile; Surface water; SWOT; time series analysis; topography; Web application; Inland water dynamics; Google earth engine; Sentinel-1; Web application; SWOT; Python
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2022.105305

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. •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.