Deriving a Frozen Area Fraction From Metop ASCAT Backscatter Based on Sentinel-1

Surface state data derived from spaceborne microwave sensors with suitable temporal sampling are to date only available in low spatial resolution (25-50 km). 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 the gradual freezing and thawing of complex landscapes. Frozen fractions were retrieved from Advanced Scatterometer (ASCAT, C-band) backscatter on a 12.5-km grid for three sites in noncontinuous 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. The validation of the Sentinel-1 derived freeze-thaw classifications showed high accuracy when compared to in situ near-surface soil temperature (84.7%-94%). Results are discussed with regard to landscape type, differences between spring and autumn, and gridding. This article 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.