Wet Snow Depth from Tandem-X Single-Pass Insar Dem Differencing

Silvan Leinss*, Oleg Antropov, Juho Vehvilainen, Juha Lemmetyinen, Irena Hajnsek, Jaan Praks

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)


Single pass radar interferometry (sp-InSAR) is a well established technique for generation of digital elevation models (DEM). Differencing two DEMs acquired at different times can reveal topographic changes. However snow depth estimation by DEM differencing is still an ongoing topic in radar research: in contrast to snow free surfaces, the snow surface elevation is difficult to detect either because of microwave penetration into dry snow or because of the weak backscatter return from wet snow which significantly decorrelates the interferometric signal. In this study we demonstrate first results of wet snow depth estimation by differencing sp-InSAR DEMs acquired by the TanDEM-X satellite mission. The results show, in contrast to dry snow, a clear sensitivity to wet snow. However, additionally to a high vertical sensitivity of a few ten centimeters a very low noise-equivalent-sigma-zero (NESZ) is crucial for successful snow depth estimation.

Original languageEnglish
Title of host publicationIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Number of pages4
ISBN (Electronic)978-1-5386-7150-4
ISBN (Print)978-1-5386-7151-1
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventInternational Geoscience and Remote Sensing Symposium - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018
Conference number: 38

Publication series

NameIEEE International Symposium on Geoscience and Remote Sensing IGARSS
ISSN (Print)2153-6996
ISSN (Electronic)2153-7003


ConferenceInternational Geoscience and Remote Sensing Symposium
Abbreviated titleIGARSS
Internet address


  • snow depth
  • wet snow
  • TanDEM-X
  • radar interferometry
  • DEM generation
  • DEM differencing


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