Skip to main content
SpringerLink
Log in

SAR Time Series for the Analysis of Inundation Patterns in the Yellow River Delta, China

  • Chapter
Remote Sensing Time Series

Part of the book series: Remote Sensing and Digital Image Processing ((RDIP,volume 22))

Abstract

Earth Observation using radar remote sensing is a valuable tool for the monitoring large scale inundation over time. This study performs a time series analysis using 18 ENVISAT/ASAR Wide Swath Mode data sets for the year 2008 and 13 TerraSAR-X Stripmap data sets for the year 2013/2014 to characterize inundation patterns in the Yellow River Delta, located in Shandong Province of China. Water surfaces are automatically derived using the software package WaMaPro, developed at the German Remote Sensing Data Center (DFD), of the German Aerospace Center (DLR), which allows an automatic classification using empirical thresholding. The temporal analysis allows the separation of different types of water bodies such as rivers, water storage basins, aquaculture, brine ponds, and agricultural fields based on inundation frequencies. This supports the understanding of the water dynamics in this highly variable study region. As ENVISAT data is not available anymore since April 2012, and as access to TerraSAR-X data is limited, Sentinel-1 data of the European Space Agency, ESA, are eagerly expected for the region. The good spatial resolution between 40 up to 5 m, as well as a dense temporal coverage, which allow to generate “true” SAR time series, and will help to lift annual analyses to the next level.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Amelung F, Galloway DL, Bell JW, Zebker HA, Laczniak RJ (1999) Sensing the ups and downs of Las Vegas—InSAR reveals structural control of land subsidence and aquifer-system deformation. Geology 27:483–486

    Article  Google Scholar 

  • Askne J, Santoro M (2005) Multitemporal repeat pass SAR interferometry of boreal forests. IEEE Trans Geosci Remote Sens 43(6):1219–1228

    Article  Google Scholar 

  • Cartus O, Santoro M, Schmullius C, Li Z (2011) Large area forest stem volume mapping in the boreal zone using synergy of ERS-1/2 tandem coherence and MODIS vegetation continuous fields. Remote Sens Environ 115:931–943

    Article  Google Scholar 

  • Chaouch N, Temimi M, Hagen S, Weishampel J, Medeiros S, Khanbilvardi R (2011) A synergetic use of satellite imagery from SAR and optical sensors to improve coastal flood mapping in the Gulf of Mexico. Hydrol Process 26:1617–1628

    Article  Google Scholar 

  • Dorigo W, deJeu R, Chung D, Parinussa R, Liu Y, Wagner W, Fernández-Prieto D (2012) Evaluating global trends (1988–2010) in harmonized multi-satellite surface soil moisture. Geophys Res Lett 39(L18405):1–7

    Google Scholar 

  • Dostálová A, Doubková M, Sabel D, Bauer-Marschallinger B, Wagner W (2014) Seven years of Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) of surface soil moisture over Africa. Remote Sens 6(8):7683–7707

    Article  Google Scholar 

  • Doubková M, Dostálová A (née Hegyiová), van Dijk AIJM, Blöschl G, Wagner W, Fernández-Prieto D (2014) How do spatial scale, noise, and reference data affect empirical estimates of error in ASAR-derived 1 km resolution soil moisture? IEEE J Sel Top Appl Earth Obs Remote Sens 7(9):3880–3891. doi:10.1109/JSTARS.2014.2324657

  • Fielding EJ, Blom RG, Goldstein RM (1998) Rapid subsidence over oil fields measured by SAR interferometry. Geophys Res Lett 27:3215–3218

    Article  Google Scholar 

  • Greifeneder F, Wagner W, Sabel D, Naeimi V (2014) Suitability of SAR imagery for automatic flood mapping in the Lower Mekong basin. Int J Remote Sens 35(8):2857–2874

    Article  Google Scholar 

  • Gstaiger V, Gebhardt S, Huth J, Wehrmann T, Kuenzer C (2012) Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data. Int J Remote Sens 33(22):7291–7304

    Article  Google Scholar 

  • Henry JB, Chastanet P, Fellah K, Desnos YL (2006) Envisat multi-polarized ASAR data for flood mapping. Int J Remote Sens 27(10):1921–1929

    Article  Google Scholar 

  • Hess LL, Melack JM, Filoso S, Wang Y (1995) Delineationg of inundated area and vegetation along the Amazon floodplain with the SIR-C synthetic aperture radar. IEEE Trans Geosci Remote Sens 33(4):896–904

    Article  Google Scholar 

  • Hess LL, Melack JM, Novo EMLM, Barbosa CCF, Gastil M (2003) Dual-season mapping of wetland inundation and vegetation for the central Amazon basin. Remote Sens Environ 87:404–428

    Article  Google Scholar 

  • Higgins S, Overeem I, Tanaka A, Syvitski J (2013) Land subsidence at aquaculture facilities in the Yellow River Delta, China. Geophys Res Lett 40(15):3898–3902. doi:10.1002/grl.50758

    Article  Google Scholar 

  • Hoque R, Nakayama D, Matsuyama H, Matsumoto J (2010) Flood monitoring, mapping and assessing capabilities using RADARSAT remote sensing, GIS and ground data for Bangladesh. Nat Hazards 58:525–548

    Google Scholar 

  • Huth J, Gebhardt S, Ahrens M, Hoffmann J, Kuenzer C (2015) Water surface derivation with WaMaPro: an easy to use radar data processing tool. Int J Remote Sens

    Google Scholar 

  • Kasischke ES, Smith KB, Bourgeau-Chavez LL, Romanowicz EA, Brunzell S, Richardson CJ (2003) Effects of seasonal hydrologic patterns in south Florida wetlands on radar backscatter measured from ERS-2 SAR imagery. Remote Sens Environ 88(4):423–441

    Article  Google Scholar 

  • Kiage LM, Walker ND, Balasubramanian S, Babin A, Barras J (2005) Applications of Radarsat 1 synthetic aperture radar imagery to assess hurricane related flooding of coastal Louisiana. Int J Remote Sens 26(24):5359–5380

    Article  Google Scholar 

  • Kuenzer C, Knauer K (2013) Remote sensing of rice crop areas – a review. Int J Remote Sens 34(6):2101–2139

    Article  Google Scholar 

  • Kuenzer C, Zhao D, Scipal K, Sabel D, Naeimi V, Bartalis Z, Hasenauer S, Mehl H, Dech S, Wagner W (2009) El Niño influences represented in ERS scatterometer derived soil moisture data. Appl Geogr 29(4):463–477

    Article  Google Scholar 

  • Kuenzer C, Bluemel A, Gebhardt S, Vo Quoc T, Dech S (2011) Remote sensing of mangrove ecosystems: a review. Remote Sens 3:878–928. doi:10.3390/rs3050878

    Article  Google Scholar 

  • Kuenzer C, Guo H, Leinenkugel L, Huth J, Li X, Dech S (2013a) Flood mapping and flood dynamics of the Mekong Delta: an ENVISAT-ASAR-WSM based Time Series Analyses. Remote Sens 5:687–715. doi:10.3390/rs5020687

    Article  Google Scholar 

  • Kuenzer C, Guo H, Schlegel I, Vo QT, Li X, Dech S (2013b) Scale and the capability of Envisat ASAR-WSM, TerraSAR-X Scansar, and TerraSAR-X stripmap data to assess urban flood situations: a case study in Can Tho Province of the Mekong Delta. Remote Sens 5:5122–5142. doi:10.3390/rs5105122

    Article  Google Scholar 

  • Kuenzer C, Ottinger M, Liu G, Sun B, Dech S (2014) Earth observation-based coastal zone monitoring of the Yellow River Delta: dynamics in China’s second largest oil producing region over four decades. Appl Geogr 55:72–107

    Article  Google Scholar 

  • Lang MW, Kasischke ES, Prince SD, Pittman KW (2008) Assessment of C-band synthetic aperture radar data for mapping and monitoring Coastal Plain forested wetlands in the Mid-Atlantic Region, U.S.A. Remote Sens Environ 112(11):4120–4130

    Article  Google Scholar 

  • Martinez JM, Le Toan T (2007) Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data. Remote Sens Environ 108(3):209–223

    Article  Google Scholar 

  • Martinis S (2010) Automatic near real-time flood detection in high resolution X-band synthetic aperture radar satellite data using context-based classification on irregular graphs. Dissertation, Ludwig-Maximilians-University Munich, Munich, Germany

    Google Scholar 

  • Martinis S, Twele A (2010) A hierarchical spatio-temporal Markov model for improved flood mapping using multi-temporal X-band SAR data. Remote Sens 2(9):2240–2258

    Article  Google Scholar 

  • Martinis S, Twele A, Voigt S (2011) Unsupervised extraction of flood-induced backscatter changes in SAR data using Markov image modeling on irregular graphs. IEEE Trans Geosci Remote Sens 49(1):251–263

    Article  Google Scholar 

  • Martinis S, Twele A, Kersten J (2014) A fully automated TerraSAR-X based flood service. ISPRS J Photogramm Remote Sens. doi:10.1016/j.isprsjprs.2014.07.014 (in press)

  • Martinis S, Kuenzer C, Twele A (2015) Flood studies using synthetic aperture radar data. Remote Sens Water Resour Disasters and Urban Stud Taylor & Francis (submitted). http://www.crcpress.com/product/isbn/9781482217919

  • Mason DC, Speck R, Devereux B (2010) Flood detection in urban areas using TerraSAR-X. IEEE Trans Geosci Remote Sens 48(2):882–893

    Article  Google Scholar 

  • Naemi V, Leinenkugel P, Sabel D, Wagner W, Apel H, Kuenzer C (2013) Evaluation of soil moisture retrieval from the ERS and Metop scatterometers in the lower Mekong Basin. Remote Sens 5(4):1603–1623

    Article  Google Scholar 

  • Ottinger M, Kuenzer C, Liu G, Wang S, Dech S (2013) Monitoring land cover dynamics in the Yellow River Delta from 1995 to 2010 based on Landsat 5 TM. Appl Geogr 44:53–68

    Article  Google Scholar 

  • Pierdicca N, Pulvirenty L, Chini M, Guerriero L, Candela L (2013) Observing floods from space: Experience gained from COSMO-SkyMed observations. Acta Astronaut 3(80):122–133

    Article  Google Scholar 

  • Richards JA, Woodgate PW, Skidmore AK (1987) An explanation of enhanced radar backscattering from flooded forests. Int J Remote Sens 8(7):1093–1100

    Article  Google Scholar 

  • Santoro M, Askne J, Smith G, Fransson JES (2002) Stem volume retrieval in boreal forests from ERS-1/2 interferometry. Remote Sens Environ 81:19–35

    Article  Google Scholar 

  • Schumann G, Matgen P, Hoffman L, Hostache R, Pappenberger F, Pfister L (2007) Deriving distributed roughness values from satellite radar data for flood inundation modelling. J Hydrol 344:96–111

    Article  Google Scholar 

  • Townsend PA (2001) Mapping seasonal flooding in forested wetlands using multi-temporal radarsat SAR. Photogramm Eng Remote Sens 67(7):857–864

    Google Scholar 

  • Wagner W, Lemoine G, Rott H (1999) A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sens Environ 70:191–207

    Article  Google Scholar 

  • Wagner W, Luckman A, Vietmeier J, Tansey K, Balzter H, Schmullius C, Davidson M, Gaveau D, Gluck M, Le Toan T, Quegan S, Shvidenko A, Wiesmann A, Jiong Yu JJ (2003) Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data. Remote Sens Environ 85(2):125–144

    Article  Google Scholar 

  • Wagner W, Hahn S, Kidd R, Melzer T, Bartalis Z, Hasenauer S, Figa-Saldanấ J, de Rosnay P, Jann A, Schneider S, Komma J, Kubu G, Brugger K, Aubrecht C, Züger C, Gangkofer U, Kienberger S, Brocca L, Wang Y, Blöschl G, Eitzinger J, Steinnocher K, Zeil P, Rubel F (2013) The ASCAT soil moisture product: a review of its specifications, validation results, and emerging applications. Meteorol Z 22(1):5–33

    Article  Google Scholar 

  • Wang Y (2010) Remote sensing of coastal environments. CRC Press/Taylor & Francis Group, Boca Raton, 457 pp

    Book  Google Scholar 

  • Werle D, Martin TC, Hasan K (2001) Flood and coastal zone monitoring in bangladesh with radarsat ScanSAR: technical experience and institutional challenges. John Hopkins APL Tech Dig 21(1):148–154

    Google Scholar 

  • Zhao D, Kuenzer C, Fu C, Wagner W (2008) Evaluation of the ERS scatterometer derived soil water index to monitor water availability and precipitation distribution at three different scales in China. J Hydrometeorol 9:549–562

    Article  Google Scholar 

  • Zhou C, Luo J, Yang C, Li B, Wang S (2000) Flood monitoring using multi-temporal AVHRR and RADARSAT imagery. Photogramm Eng Remote Sens 66(5):633–638

    Google Scholar 

Download references

Acknowledgements

This study was undertaken in the context of the DELIGHT project (www.delight.eoc.dlr.de) funded by the German Federal Ministry of Education and Research, BMBF. We are furthermore grateful for the constructive comments of two anonymous reviewers.

Author information

Authors and Affiliations

  1. German Remote Sensing Data Center, DFD, Earth Observation Center, EOC, German Aerospace Center, DLR, Oberpfaffenhofen, Germany

    Claudia Kuenzer, Juliane Huth & Sandro Martinis

  2. Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, RADI, Chinese Academy of Sciences, CAS, Beijing, China

    Linlin Lu

  3. German Remote Sensing Data Center, DFD, German Aerospace Center, DLR, Wessling, Germany

    Stefan Dech

  4. Institute for Geography and Geology, University of Wuerzburg, Wuerzburg, Germany

    Stefan Dech

Authors
  1. Claudia Kuenzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juliane Huth
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sandro Martinis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Linlin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stefan Dech
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Claudia Kuenzer .

Editor information

Editors and Affiliations

  1. German Remote Sensing Data Center, DFD, German Aerospace Center, DLR, Wessling, Germany

    Claudia Kuenzer

  2. German Remote Sensing Data Center, DFD, German Aerospace Center, DLR, Wessling, Germany

    Stefan Dech

  3. Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria

    Wolfgang Wagner

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Kuenzer, C., Huth, J., Martinis, S., Lu, L., Dech, S. (2015). SAR Time Series for the Analysis of Inundation Patterns in the Yellow River Delta, China. In: Kuenzer, C., Dech, S., Wagner, W. (eds) Remote Sensing Time Series. Remote Sensing and Digital Image Processing, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-15967-6_20

Download citation

Publish with us

Policies and ethics

Search

Navigation