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Deriving a frozen area fraction from Metop ASCAT backscatter based on Sentinel-1

Research output: Contribution to journalArticle

  • Helena Bergstedt
  • Annett Bartsch
  • Anton Neureiter
  • Angelika Hofler
  • Barbara Widhalm
  • Dr Nick Pepin
  • Jan Hjort
Surface state data derived from space-born microwave sensors with suitable temporal sampling are to date only available in low spatial resolution (25-50km). Current approaches do not adequately resolve spatial heterogeneity in landscape scale freeze/thaw processes. We propose to derive a frozen fraction instead of binary freeze/thaw information. This introduces the possibility to monitor gradual freezing and thawing of complex landscapes. Frozen fractions were retrieved from Advanced Scatterometer (ASCAT; C-band) backscatter on a 12.5km grid for three sites in non-continuous permafrost areas in northern Finland and the Austrian Alps. To calibrate the retrieval approach, frozen fractions based on Sentinel-1 Synthetic Aperture Radar (SAR, C-band) were derived for all sites and compared to ASCAT backscatter. We found strong relationships for ASCAT backscatter with Sentinel-1 derived frozen fractions (Pearson correlations of -0.85 to -0.96) for the sites in northern Finland and less strong relationships for the alpine site (Pearson correlations -0.579 and -0.611, including and excluding forested areas). Applying the derived linear relationships, predicted frozen fractions using ASCAT backscatter values showed root mean square error (RMSE) values between 7.26% and 16.87% when compared with Sentinel-1 frozen fractions. Validation of the Sentinel-1 derived freeze/thaw classifications showed high accuracy when compared to in situ near-surface soil temperature (84.7% to 94%). Results are discussed with regard to landscape type, differences between spring and autumn and gridding. This study serves as a proof of concept, showcasing the possibility to derive frozen fraction from coarse spatial resolution scatterometer time series to improve the representation of spatial heterogeneity in landscape scale surface state.
Original languageEnglish
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Early online date13 Mar 2020
DOIs
Publication statusEarly online - 13 Mar 2020

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