Sentinel-1 based Inland water dynamics Mapping System (SIMS)
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...
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| Published in | Environmental modelling & software : with environment data news Vol. 149; p. 105305 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Elsevier Ltd
01.03.2022
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1364-8152 1873-6726 |
| DOI | 10.1016/j.envsoft.2022.105305 |
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| 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.
•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. |
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| AbstractList | 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. 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. |
| ArticleNumber | 105305 |
| Author | Soman, Manu K. Indu, J. |
| Author_xml | – sequence: 1 givenname: Manu K. orcidid: 0000-0001-7069-8393 surname: Soman fullname: Soman, Manu K. organization: Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India – sequence: 2 givenname: J. orcidid: 0000-0003-4276-6138 surname: Indu fullname: Indu, J. email: indusj@civil.iitb.ac.in organization: Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India |
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| SubjectTerms | 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 |
| Title | Sentinel-1 based Inland water dynamics Mapping System (SIMS) |
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