## 4 Discussion and Conclusions  
The latest (R16) SMAP FT records are derived using combined NPR and FT-SCV retrieval algorithms, and extend over a larger global domain than prior product releases. The SMAP global FT products include the mitigation of suspected false freeze andthaw retrievals identified in earlier product releases. The FT accuracy was assessed using in situ daily surface air temperature measurements from approximately 5000 weather stations located across the global domain. Our results indicate that the PM overpass FT results from the 9 km and 36 km products exceed the targeted 80% accuracy threshold for the SMAP baseline mission requirement [8]. The AM FT accuracy is significantly lower than the PM FT results for both the 36 km and 9 km products, but generally exceeds the 70% accuracy threshold of the minimum mission requirement. The FT accuracy is generally greater during the northern hemisphere summer, and is lower in winter and during seasonal transitions when landscape heterogeneity in snow cover and microclimate conditions is enhanced relative to the coarse (~40 km) L-band radiometer footprint. Figure 8: Spatial pattern in the number of annual frost days over southern Australia in 2016 and 2017 depicted from the SMAP L3_FT_P_E global record (upper). The seasonal variation in the corresponding frost affected area (% of domain) is also shown for each year (lower). The grey shading denotes the DEM elevations (m), including higher elevation mountains in southeastern Australia. A key assumption of the NPR and FT-SCV algorithms is that major temporal shifts in the SMAP Tb record are caused by the characteristic large landscape dielectric changes that occur during FT transitions, rather than by other potential factors including large precipitation events or changes in surface in mutation, snow cover or canopy biomass. The physical basis for the NPR algorithm requires relatively stable freeze and thaw seasonal reference conditions and enough difference between NPR reference states to establish a suitable signal-to-noise ratio for the FT classification. These criteria limit the area where the NPR algorithm can be applied. Based on the three-year SMAP data analysis, the NPR reference difference needed to be larger than 0.1 in order to enable reliable FT results. On the other hand, the FT-SCV algorithm exploits the large characteristic dielectric and associated Tbv shift between predominantly frozen and non-frozen conditions, influenced by the correlation between the L-band Tbv and physical temperature. Favorable correlation (\(|\)R\(|\)\(>\) 0.5) between Tbv and surface temperature (represented from the GMAO reanalysis) is a requirement for the FT-SCV application, while the associated Tbv and temperature relationship is used to define the Tbv FT threshold for each grid cell in the FT-SCV algorithm. Nevertheless, there are some grid cells where both algorithms can be applied, though these areas represent a relatively small (\(<\)10%) portion of the SMAP FT global domain. In these areas, the NPR is selected over the FT-SCV during operational processing because the NPR is consistent with the primary baseline algorithm established from the initial SMAP FT science algorithm and product release [7]. This approach helps to preserve product self-consistency. The SMAP FT retrieval patterns showed no apparent artifacts between the NPR and FT-SCV domains. The FT metrics were generally consistent between NPR and FT-SCV derived results despite the different areas and conditions represented by the two algorithms. The SMAP global FT accuracy is influenced by landscape heterogeneity within the sensor footprint and was degraded in areas with higher fw cover, terrain variability, and vegetation cover. Together, these landscape factors accounted for approximately 29-53% of the global variability in mean annual FT classification accuracy from respective 36 km and 9 km PM products relative to the global SAT weather station network (Figure 5).