Abstract

More than half of the global land area undergoes seasonal frozen and thawed conditions that constrain eco-hydrological processes. The freeze-thaw (FT) retrieval from satellite microwave remote sensing detects landscape changes between frozen and non-frozen conditions due to the strong dependence of surface microwave emissions on liquid water abundance. We conducted an assessment of the latest version (R16) of the NASA Soil Moisture Active Passive (SMAP) Level 3 FT (L3_FT) global product. The L3_FT product provides a global FT classification with 3-day mean temporal fidelity derived using SMAP L-band (1.4 GHz) microwave brightness temperature (Tb) retrievals. The R16 product uses both normalized polarization ratio (NPR) and single channel vertically-polarized Tb (FT-SCV) algorithms to obtain FT retrievals over land areas where frozen temperatures are a significant ecological constraint. The L3_FT product is generated in a standard global grid with similar grid cell resolution (36-km) as the SMAP radiometer footprint. An enhanced 9-km global grid L3_FT product is also produced from optimally interpolated SMAP Tb retrievals. The resulting L3_FT products span a larger domain and longer period (2015–present) than earlier product releases. The L3_FT 36-km results showed a respective global mean annual FT classification accuracy of approximately 78 and 90 percent for descending (AM) and ascending (PM) orbit observations in relation to independent surface air temperature-based FT estimates from ~5000 global weather stations. The FT accuracy was lower in areas with greater terrain complexity, open water and vegetation cover; where the combined land cover factors explained 29–53% of the variability in the SMAP FT global accuracy. The L3_FT 9-km product showed an apparent enhancement of FT spatial patterns, but with ~4% lower accuracy than the 36-km product; the lower 9-km accuracy was attributed to stronger degradation from land cover heterogeneity, particularly in coastal areas, and artifact noise introduced from the spatial interpolation of SMAP Tb retrievals. Selected regional applications indicated product utility in capturing anomalous frost events over Australia and seasonal thaw and spring onset patterns over Alaska. Overall, the L3_FT global accuracy meets or exceeds the FT product science requirements established by the mission, while enabling studies of dynamic FT and water mobility constraints influencing hydrological and ecosystem processes, and global water-carbon-energy cycle linkages.

Highlights

  • Spatial patterns and the timing of landscape freeze/thaw (FT) state transitions have a major influence on eco-hydrological processes where frozen temperatures are a significant part of the annual cycle [1,2,3]

  • The FT state parameter is a key component of the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) and European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) missions [6,7,8]

  • The 36 km grid is closer to the native footprint of the SMAP Tb retrievals, whereas the enhanced resolution (9 km) grid product is derived from optimally interpolated SMAP Tb retrievals [10]

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Summary

Introduction

Spatial patterns and the timing of landscape freeze/thaw (FT) state transitions have a major influence on eco-hydrological processes where frozen temperatures are a significant part of the annual cycle [1,2,3]. The FT state parameter is a key component of the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) and European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) missions [6,7,8]. Both missions provide L-band (~1.4 GHz) microwave radiometer brightness temperature retrievals with potentially enhanced sensitivity to soil FT conditions compared to higher frequency passive microwave observations from other current and historical satellite sensors [7,9], but with different sensor configurations and sampling footprints. The 36 km grid is closer to the native footprint of the SMAP Tb retrievals, whereas the enhanced resolution (9 km) grid product is derived from optimally interpolated SMAP Tb retrievals [10]

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