Sentinel-1 and-2 Based near Real Time Inland Excess Water Mapping for OptimizedWater Management

被引:16
|
作者
van Leeuwen, Boudewijn [1 ]
Tobak, Zalin [1 ]
Kovacs, Ferenc [1 ]
机构
[1] Univ Szeged, Dept Phys Geog & Geoinformat, Egyet U 2-6, H-6722 Szeged, Hungary
关键词
inland excess water; flood; water management; radar remote sensing; optical remote sensing; automation; DELINEATION;
D O I
10.3390/su12072854
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Changing climate is expected to cause more extreme weather patterns in many parts of the world. In the Carpathian Basin, it is expected that the frequency of intensive precipitation will increase causing inland excess water (IEW) in parts of the plains more frequently, while currently the phenomenon already causes great damage. This research presents and validates a new methodology to determine the extent of these floods using a combination of passive and active remote sensing data. The method can be used to monitor IEW over large areas in a fully automated way based on freely available Sentinel-1 and Sentinel-2 remote sensing imagery. The method is validated for two IEW periods in 2016 and 2018 using high-resolution optical satellite data and aerial photographs. Compared to earlier remote sensing data-based methods, our method can be applied under unfavorite weather conditions, does not need human interaction and gives accurate results for inundations larger than 1000 m(2). The overall accuracy of the classification exceeds 99%; however, smaller IEW patches are underestimated due to the spatial resolution of the input data. Knowledge on the location and duration of the inundations helps to take operational measures against the water but is also required to determine the possibilities for storage of water for dry periods. The frequent monitoring of the floods supports sustainable water management in the area better than the methods currently employed.
引用
收藏
页数:21
相关论文
共 50 条
  • [1] Sentinel-1 based Inland water dynamics Mapping System (SIMS)
    Soman, Manu K.
    Indu, J.
    [J]. ENVIRONMENTAL MODELLING & SOFTWARE, 2022, 149
  • [2] BUSHFIRE SEVERITY MAPPING USING SENTINEL-1 AND-2 IMAGERY
    Rahman, Shahriar
    Chang, Hsing-Chung
    Tomkins, Kerrie
    Hehir, Warwick
    [J]. IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 1114 - 1116
  • [3] Inland Water Body Mapping Using Multitemporal Sentinel-1 SAR Data
    Marzi, David
    Gamba, Paolo
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 : 11789 - 11799
  • [4] Monitoring direct drivers of small-scale tropical forest disturbance in near real-time with Sentinel-1 and-2 data
    Slagter, Bart
    Reiche, Johannes
    Marcos, Diego
    Mullissa, Adugna
    Lossou, Etse
    Pena-Claros, Marielos
    Herold, Martin
    [J]. REMOTE SENSING OF ENVIRONMENT, 2023, 295
  • [5] Fusion of Sentinel-1 and Sentinel-2 image time series for permanent and temporary surface water mapping
    Bioresita, Filsa
    Puissant, Anne
    Stumpf, Andre
    Malet, Jean-Philippe
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2019, 40 (23) : 9026 - 9049
  • [6] Sentinel-1 near real-time application for maritime situational awareness
    Detmar Krause
    Egbert Schwarz
    Sergey Voinov
    Heiko Damerow
    Daniel Tomecki
    [J]. CEAS Space Journal, 2019, 11 : 45 - 53
  • [7] Sentinel-1 near real-time application for maritime situational awareness
    Krause, Detmar
    Schwarz, Egbert
    Voinov, Sergey
    Damerow, Heiko
    Tomecki, Daniel
    [J]. CEAS SPACE JOURNAL, 2019, 11 (01) : 45 - 53
  • [8] Flood Mapping in Vegetated Areas Using an Unsupervised Clustering Approach on Sentinel-1 and-2 Imagery
    Landuyt, Lisa
    Verhoest, Niko E. C.
    Van Coillie, Frieke M. B.
    [J]. REMOTE SENSING, 2020, 12 (21) : 1 - 20
  • [9] An Open Benchmark Dataset for Forest Characterization from Sentinel-1 and-2 Time Series
    Hauser, Sarah
    Ruhhammer, Michael
    Schmitt, Andreas
    Krzystek, Peter
    [J]. REMOTE SENSING, 2024, 16 (03)
  • [10] CROP TYPE MAPPING BASED ON SENTINEL-1 BACKSCATTER TIME SERIES
    Arias, M.
    Campo-Bescos, M. A.
    Alvarez-Mozos, J.
    [J]. IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 6623 - 6626