## Data and Methods
### The SMAP FT Classification Algorithms
#### 2.2.2 Single Channel Algorithm  
The main objective of this study is to conduct a global validation assessment of a new SMAP FT operational data record, which encompasses a larger global domain and longer operational record than earlier product releases (e.g., [7]). For this investigation, an alternative FT algorithm was introduced to fill gaps in the global domain where the NPR algorithm performs poorly. The alternative approach uses a single channel Tbv-based modified seasonal threshold FT algorithm (FT-SCV) similar to the core algorithm used in the FT Earth System Data Record (ESDR) [11; 28]. The FT-SCV approach assumes that the large changes in the microwave dielectric constant of the land surface around the 0\({}^{\circ}\)C freezing point of liquid water dominate the corresponding Tb seasonal signature, rather than other potential sources of Tb variability [11; 29; 30]. The Tb retrievals are indirectly sensitive to near-surface temperatures, so ancillary air temperature records from global reanalysis data are used to define the FT threshold in the FT-SCV algorithm [1; 11; 31]. The SMAP Tbv record is used in the FT-SCV algorithm because it has less sensitivity to snow cover than Tbh [32; 33]. The FT-SCV algorithm is defined as follows:  
\[\text{R}>0.5,\text{ FT status}=\left\{\begin{array}{l}\textit{thaw},\textit{ if Tbv}>\textit{threshold}_{scv}\\ \textit{frozen},\textit{ if Tbv}\leq\textit{threshold}_{scv}\end{array}\right. \tag{4}\]  
\[\text{R}<-0.5,\text{ FT status}=\left\{\begin{array}{l}\textit{thaw}, \textit{ if Tbv}<\textit{threshold}_{scv}\\ \textit{frozen},\textit{ if Tbv}\geq\textit{threshold}_{scv}\end{array}\right. \tag{5}\]  
The FT-SCV threshold (_threshold_SCV) and correlation (R) was defined annually for each grid cell using an empirical linear relationship between Tbv and surface temperature (Tsurf) from the GMAO global model reanalysis [34; 35]. The GMAO product was selected over other available global reanalysis data because it uses a similar GEOS-5 land model assimilation system as the SMAP soil moisture and carbon products [36; 37]. The GMAO AM/PM Tsurf data corresponding to descending/ascending Tbv retrievals are used in the linear regression, where the FT _threshold_scv_ value for each grid cell is defined as the estimated Tbv value where Tsurf is \(\sim\)0.0 \({}^{\circ}\)C. The resulting _threshold_scv_ map is the same for AM and PM processing, but the FT-SCV algorithm is applied separately to the descending and ascending orbit Tbv retrievals to produce the AM and PM FT data. The 2-year (2016-2017) mean _threshold_scv_ and a single year (2017) correlation between SMAP Tbv and GMAO Tsurf records were used in the SMAP FT classification.  
A global map of the correlation between SMAP Tbv and GMAO Tsurf (Figure 1) is used to define the domain for the FT-SCV retrievals. Areas with stronger Tbv and Tsurf correlations are assumed to have a more reliable FT threshold and favorable FT-SCV accuracy because the Tbv retrievals are more directly sensitive to land surface FT dielectric shifts than Tsurf during the FT transition period. The FT-SCV algorithm is used for grid cells where the NPR algorithm is invalid and the absolute value of the Tbv and Tsurf correlation is larger than 0.5. Grid cells with lower correlation are assigned a lower data quality flag in the resulting L3_FT products (Section 2.5). Negative Tbv and Tsurf correlation areas (Figure 1) are associated with lake ice and surface water inundation effects located predominantly in areas with barren or low to moderate vegetation cover, or in dry climate areas (e.g., the Tibetan plateau) where the soil dielectric constant is similar between frozen and thawed conditions [26; 38]. Grid cells identified as having large water bodies, permanent ice and snow cover, and mountainous terrain are generally associated with lower FT algorithm performance, and are either masked or assigned low data quality flags using ancillary static land cover information.