期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 58, 期 9, 页码 6008-6019出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.2967364
关键词
Temperature measurement; Backscatter; Spatial resolution; Monitoring; Radar measurements; Synthetic aperture radar; Land surface temperature; Advanced Scatterometer (ASCAT); freeze-thaw; permafrost; Sentinel-1; surface state
类别
资金
- Austrian Science Fund [Fonds zur Forderung der Wissenschaftlichen Forschung (FWF)] through the Doctoral College GIScience [DK W1237-N23]
- ESA's DUE GlobPermafrost Project [4000116196/15/I-NB]
- Zentralanstalt fur Meteorologie und Geodynamik (ZAMG) Entwicklungsprojekt Grant Sen4Austria
- ESA CCI+ Permafrost
- ZAMG Entwicklungsprojekt under Grant Sen4Austria
- Academy of Finland [315519]
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.
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