The combination of satellite images Sentinel-1 and Sentinel-2 for the Spatio-temporal changes monitoring assessment in surface water

Remote Sensing & GIS for Environmental Monitoring

Authors

First and Last Name Academic degree E-mail Affiliation
Ľubomír Kseňak Ph.D. lubomir.ksenak [at] tuke.sk Technical University of Košice
Košice, Slovakia
Katarína Pukanská Ph.D. katarina.pukanska [at] tuke.sk Technical University of Košice
Košice, Slovakia
Karol Bartoš Ph.D. karol.bartos [at] tuke.sk Technical University of Košice
Košice, Slovakia
Jakub Šveda No jakub.sveda [at] tuke.sk Technical University of Košice
Košice, Slovakia

I and my co-authors (if any) authorize the use of the Paper in accordance with the Creative Commons CC BY license

First published on this website: 12.08.2022 - 13:35
Abstract 

This study presents the possibility of using SAR satellite data for long-term monitoring of changes in the surface water, combined with optical multispectral images Sentinel-2. Also, it aims to demonstrate the suitability of satellite SAR and multispectral data implementation for watercourses mapping caused by inundation processes in their catchment area. The Sentinel-1 image processing procedures used assess the relevancy of using a vertical-vertical (VV) polarization configuration for documenting water bodies. The extracting process of water bodies is based on the "Otsu" determination of threshold values.

Keywords 
Remote Sensing
Surface Water
Sentinel-1
Sentinel-2
References 
  1. Bayanudin, A.A.; Jatmiko, R.H. [2016] Orthorectification of Sentinel-1 SAR (Synthetic Aperture Radar) Data in Some Parts of South-eastern Sulawesi Using Sentinel-1 Toolbox. IOP Conf. Ser. Earth Environ. Sci., 47, 012007.
  2. Chapman, B.; McDonald, K.; Shimada, M.; Rosenqvist, A.; Schroeder, R.; Hess, L. [2015] Mapping Regional Inundation with Spaceborne L-Band SAR. Remote Sens., 7, 5440–5470.
  3. Chen, F.; Chen, X.; Van de Voorde, T.; Roberts, D.; Jiang, H.; Xu, W. [2020] Open water detection in urban environments using high spatial resolution remote sensing imagery. Remote Sens. Environ., 242, 111706.
  4. Clement, M.; Kilsby, C.; Moore, P. [2017] Multi-temporal synthetic aperture radar flood mapping using change detection. J. Flood Risk Manag., 11, 152–168.
  5. Dabiri, Z.; Hölbling, D.; Abad, L.; Helgason, J.K.; Sæmundsson, Þ.; Tiede, D. [2020] Assessment of Landslide-Induced Geomorphological Changes in Hítardalur Valley, Iceland, Using Sentinel-1 and Sentinel-2 Data. Appl. Sci., 10, 5848.
  6. Du, Y.; Zhang, Y.; Ling, F.; Wang, Q.; Li, W.; Li, X. [2016] Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band. Remote Sens., 8, 354.
  7. Evans, T.L.; Costa, M.; Tomas, W.M.; Camilo, A.R. [2014] Large-scale habitat mapping of the Brazilian Pantanal wetland: A synthetic aperture radar approach. Remote Sens. Environ., 155, 89–108
  8. Feyisa, G.L.; Meilby, H.; Fensholt, R.; Proud, S.R. [2014] Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sens. Environ., 140, 23–35.
  9. Filipponi, F. [2019] Sentinel-1 GRD Preprocessing Workflow. Proceedings, 18, 11.
  10. Jiang, W.; He, G.; Pang, Z.; Guo, H.; Long, T.; Ni, Y. [2020] Surface water map of China for 2015 (SWMC-2015) derived from Landsat 8 satellite imagery. Remote Sens. Lett., 11, 265–273.
  11. Lee, J.S.; Pottier, E. [2017] Polarimetric Radar Imaging, 1st ed.; CRC Press, Taylor & Francis Group: Boca Raton, FL, USA, p. 398.
  12. McFeeters, S.K. [1996] The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features. Int. J. Remote Sens., 17, 1425–1432.
  13. Pekel, J.F.; Cottam, A.; Gorelick, N.; Belward, A.S. [2016] High-resolution mapping of global surface water and its long-term changes. Nature, 540, 418–422.
  14. Pulvirenti, L.; Pierdicca, N.; Chini, M.; Guerriero, L. [2013] Monitoring Flood Evolution in Vegetated Areas Using COSMO-SkyMed Data: The Tuscany 2009 Case Study. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 6, 1807–1816.
  15. Rahman, M.R.; Thakurb, P.K. [2018] Detecting, mapping and analysing of flood water propagation using synthetic aperture radar (SAR) satellite data and GIS: A case study from the Kendrapara District of Orissa State of India. Egypt. J. Remote Sens. Space Sci., 21, 37–41.
  16. Rao, P.; Rao, K.; Kubo, S. [2019] Proceedings of International Conference on Remote Sensing for Disaster Management; Springer: Cham, Switzerland.
  17. Ryu, J.; Won, J.; Min, K. [2002] Waterline extraction from Landsat TM data in a tidal flatA case study in Gomso Bay, Korea. Remote Sens. Environ., 83, 442–456.
  18. Schlaffer, S.; Chini, M.; Dettmering, D.; Wagner, W. [2016] Mapping Wetlands in Zambia Using Seasonal Backscatter Signatures Derived from ENVISAT ASAR Time Series. Remote Sens., 6, 1807–1816.
  19. Sun, F.; Sun, W.; Chen, J.; Gong, P. [2012] Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery. Int. J. Remote Sens., 33, 6854–6875.
  20. Tran, H.; Nguyen, P.; Ombadi, M.; Hsu, K.; Sorooshian, S.; Andreadis, K. [2019] Improving Hydrologic Modeling Using Cloud-Free MODIS Flood Maps. J. Hydrometeorol., 20, 2203–2214.
  21. User Guides—Sentinel-1 SAR—Acquisition Modes—Sentinel Online. Available online: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/acquisi....
  22. Xu, H. [2006] Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens., 27, 3025–3033.
  23. Yang, X.; Qin, Q.; Grussenmeyer, P.; Koehl, M. [2018] Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery. Remote Sens. Environ., 219, 259–270.
  24. Yang, X.; Zhao, S.; Qin, X.; Zhao, N.; Liang, L. [2017] Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening. Remote Sens., 9, 596.
  25. Yue, H.; Li, Y.; Qian, J.X.; Liu, Y. [2020] A new accuracy evaluation method for water body extraction. Int. J. Remote Sens., 41, 7311–7342.