Benchmarking passive-microwave-satellite-derived freeze–thaw datasets

Abstract

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 terms of spatial and temporal characteristics. All of them offer only comparably coarse spatial resolutions on the order of several kilometers to tens of kilometers, 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 m×20 m in range and azimuth, provide dense time series in some regions and are therefore a suitable basis for benchmarking. We developed a robust freeze–thaw (FT) detection approach that is suitable for tundra regions, applying a constant threshold to the combined C-band VV (vertically sent and received) and VH (vertically sent and horizontally received) polarization ratios. The achieved performance (91.8 %) is similar to previous methods which apply an empirical local threshold to 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 the season, particularly during the 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.