## 4 Discussion and Conclusions
Regions influenced by the landscape factors largely represent heterogeneous grid cells encompassing mountainous regions, coastal areas, dense forests and large open water bodies. The negative impacts from large open water bodies (20-50% fw), active precipitation, and complex terrain are partially represented by the product data quality (QA) flags. Excluding these low QA flagged grid cells resulted in FT accuracy improvements of up to 2.73% for the 9 km FT product, but less than 1% improvement in the 36 km product. Other potential negative impacts from known RFI, excessive open water (fw \(>\) 50%) and permanent ice/snow cover are identified from ancillary information and are masked prior to the FT retrievals during SMAP operational processing. However, our results also indicate the important role of vegetation cover affecting FT accuracy, which is neglected in the current QA flagging process. The current screening process also misses a significant number (\(\sim\)181 \(\pm\) 32) of excessive water contaminated grid cells having relatively low FT accuracy (Figure 5); these areas are represented in the ESA water body map used in this study, but are missing from the static water map (MOD44W) used for SMAP operational processing. Other potential factors influencing FT accuracy include the correlation (R) between SMAP Tb retrievals and GMAO surface temperatures used for the FT-SCV calibration (Figure 1), and the seasonal difference between NPR frozen and non-frozen reference conditions. These additional constraints represent potential candidates for future QA enhancements. Potential noise introduced from the BG Tb interpolation used for the L3_FT_P_E retrievals, and an effective spatial resolution closer to 33 km, may also contribute to a lower-than-expected performance in this product relative to the L3_FT_P standard 36 km product. However, the validation results showed relatively small (3-4%) FT accuracy differences between both products; these accuracy differences were further reduced (\(\sim\)1.5%) by excluding low quality flagged grid cells. Although the overall FT accuracy of the 36 km product was slightly better than that of the 9 km product for both the PM and AM results, the finer grid product showed generally enhanced delineation of FT spatial patterns (e.g., Figure 6). The science utility of the SMAP FT products was highlighted for two regional case studies involving spring onset variability in Alaska and frost occurrence over Australia. Our results confirm the ecological significance of the FT retrieval, including the seasonal thaw and spring onset, and anomalous frost detection, in assessing FT impacts on vegetation productivity. However, our results also reveal critical differences in the SMAP L-band FT sensitivity, including an early spring thaw pattern that occurs approximately \(6.6\pm 3.4\) weeks prior to growing season onset, and frost events that more closely resemble soil rather than surface air temperature conditions. These results contrast with similar studies using higher frequency satellite Tb retrievals [4; 59] and may reflect a greater SMAP L-band FT sensitivity to snow and soil properties. The relatively high mean annual FT classification accuracy reported in warmer climate areas may mask FT uncertainty because the intermittent frost events in these areas represent only a small part of the annual cycle. Although the SMAP L-band Tb retrievals are expected to have enhanced sensitivity to surface soil FT conditions under low to moderate vegetation cover, the SMAP FT algorithms and retrievals characterize the predominant frozen or non-frozen condition within a grid cell and do not distinguish different landscape elements within the sensor footprint. The finer 9 km grid product provides a level of FT spatial enhancement contributed from the BG optimal Tb interpolation, which offers potential benefits for delineating regional climate variability.