Benchmarking passive microwave satellite derived freeze/thaw datasets
Satellite derived soil surface state has been identified to be of added value for a wide range of applications. Frozen versus unfrozen conditions are operationally mostly derived using passive microwave (PMW) measurements from various sensors and different frequencies. Products differ thematically a...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Text |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://doi.org/10.5194/egusphere-2024-2518 https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2518/ |
| Summary: | Satellite derived soil surface state has been identified to be of added value for a wide range of applications. Frozen versus unfrozen conditions are operationally mostly derived using passive microwave (PMW) measurements from various sensors and different frequencies. Products differ thematically as well as in spatial and temporal characteristics. All of them offer only comparably coarse spatial resolution in the order of several km to tens of km which limits their applicability. Quality assessment is usually limited to comparisons with in situ point records, but a regional benchmarking dataset is thus far missing. Synthetic Aperture Radar (SAR) offers high spatial detail and thus is potentially suitable for assessment of the operational products. Specifically, dual polarized C-band data acquired by Sentinel-1, operating in Interferometric Wide (IW) swath mode with a ground resolution of 5 x 20 m in range and azimuth, provide dense time series in some regions and are therefore suitable as basis for benchmarking. We developed a robust freeze/thaw (FT) detection approach, applying a constant threshold on the combined C-band VV and VH polarization ratios, that is suitable for tundra regions. The achieved performance (91.8 %) is similar to previous methods which apply an empirical local threshold on single polarized VV backscatter data. All global products, tested with the resulting benchmarking dataset, are of value for freeze/thaw retrieval, although differences were found depending on seasons, in particular during spring and autumn transition. Fusion can improve the representation of thaw and freeze-up, but a multi-purpose applicability cannot be obtained since the transition periods are not fully captured by any of the operational coarse resolution products. |
|---|