Mapping forested floodplain topography using insar and radar altimetry

Ting Yuan, Hyongki Lee, Hanwen Yu, Hahn Chul Jung, Austin Madson, Yongwei Sheng, Edward Beighley

Research output: Contribution to journalArticlepeer-review


An accurate floodplain digital elevation model (DEM) is an essential input for the hydraulic modeling of water flows across the floodplain. A method combining interferometric synthetic aperture radar (InSAR) and radar altimetry is presented to derive the bare-earth topography for a forested floodplain with an improved accuracy. This method is applied to the floodplains associated with the middle reach of the Congo River in Central Africa. The floodplain topographic model was evaluated using Ice, Cloud, and land Elevation Satellite (ICESat) altimetry measurements, and the root mean squared error (RMSE) was estimated to be 2.71 m. The newly-created elevation model has shown improvements in capturing subtle elevation variations within the forested floodplain, compared with two other vegetation bias corrected DEMs-the multierror-removed improved-Terrain DEM (RMSE of 4.95 m) and the Bare-Earth' SRTM (BEST) DEM (RMSE of 3.32 m). We were unable to generate a DEM for the entirety of the floodplain due to missing InSAR and radar altimetry data. This could be a problem for the DEM's integration into hydraulic modeling workflows where the elevation model would need to be merged with ancillary elevation products. This could lead to inconsistencies in the merged DEM and the subsequent modeled outputs. However, it is expected that future L-band SAR and satellite altimetry missions can remedy the data gaps and provide a more robust spatial coverage.

Original languageEnglish
Article number8968418
Pages (from-to)5189-5198
Number of pages10
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number12
Publication statusPublished - 2019 Dec

Bibliographical note

Funding Information:
Manuscript received February 28, 2019; revised July 27, 2019 and November 3, 2019; accepted November 21, 2019. Date of publication January 24, 2020; date of current version February 4, 2020. The work of H. Lee was supported in part by NASA’s SWOT Mission Program under Grant NNX16AQ33G and in part by the Korea Ministry of Environment under Grant 2019002650004. The work of E. Beighley was supported in part by NASA’s SWOT Mission Program under Grant NNX16AQ39G. The work of T. Yuan was supported by the Earth and Space Sciences Fellowship under Grant NNX15AM70H. The work of Y. Sheng was supported by the SWOT Mission Program under Grant NNX16AH85G. (Corresponding author: Ting Yuan.) T. Yuan, A. Madson, and Y. Sheng are with the Department of Geography, University of California, Los Angeles, CA 90095 USA (e-mail:;;

Publisher Copyright:
© 2008-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Computers in Earth Sciences
  • Atmospheric Science


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