Estimating Snow Water Equivalent Using Sentinel-1 Repeat-Pass Interferometry

Shadi Oveisgharan, Robert Zinke, Zachary Keskinen, Hans Peter Marshall

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

Abstract

The Snow Water Equivalent (SWE) is identified as the key element of a snowpack that impacts rivers' streamflow and water cycle. Active and passive microwave remote sensing methods have been used to retrieve SWE. Interferometric Synthetic Aperture Radar (InSAR) has been shown to have the potential to estimate SWE change. In this study, we apply this technique to a large time series of Sentinel-1 data from winter 2021. The retrieved SWE change observations align really well with in situ stations with 0.82 correlation and 0.76cm RSME. The total retrieved SWE also align really well with 16 in situ values in the scene with less than 20cm SWE error. On the other hand, the retrieved SWE using Sentinel-1 data is highly correlated with LIDAR snow depth data with correlation of more than 0.5.

Original languageEnglish
Title of host publicationIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages856-859
Number of pages4
ISBN (Electronic)9798350320107
DOIs
StatePublished - 2023
Event2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023 - Pasadena, United States
Duration: 16 Jul 202321 Jul 2023

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2023-July

Conference

Conference2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Country/TerritoryUnited States
CityPasadena
Period16/07/2321/07/23

Keywords

  • InSAR
  • Sentinel-1
  • SWE

Fingerprint

Dive into the research topics of 'Estimating Snow Water Equivalent Using Sentinel-1 Repeat-Pass Interferometry'. Together they form a unique fingerprint.

Cite this