## 3 Results  
Figure 4a,c show the S1 dual-polarization temporal profiles of one representative DB site (D02.SCBI). VV polarization displayed no obvious seasonal variation (annual  
Figure 3: Flowchart for the forest phenology extracting and evaluation. Figure 2: Schematics of curve fitting for (**a**). FT and (**b**). DL. Threshold for the phenological metrics was set as 50% of either the rising or falling period. The time series shown is for the 2018 growing season at site D02.SCBI. fluctuation is less than 1 dB), while the VH polarization showed a decrease in spring (\(-12\) to \(-14\) dB) and an increase in autumn. Moreover, at the 6 EN sites where seasonal variations were observed, the temporal profile of backscatter measurements showed different seasonal trends than that of DB. Figure 4b illustrates an example of the backscatter coefficients of the VV and VH in EN, which are similar in seasonal variations, while the amplitude of VH is slightly higher than that of VV (approximately 3 dB and 1 dB, respectively). The peak value of VV-VH in EN, therefore, appeared in the winter, and the valley value was in the summer, opposite to that in DB, as shown in Figure 4b. Therefore, it would be necessary to analyze the EN phenology with the negative form of band combinations as that for DB. As discussed above, the S1 C-band backscatter data of the two forest types showed different seasonal patterns. In the following study, we explored the characteristics of different polarization combinations for each forest. Overall, for DB, 12 of the 14 study sites had significant seasonal variation in the time series of VH/VV, while 9 of them showed significant seasonal variations in VV-VH. At the D01.BART and D11.CLBj sites, no significant seasonal patterns were detected on the time series of either of the polarization combinations. For evergreen needleleaf forests, only 6 of 12 study sites witnessed seasonal variations in the VV-VH time series, and only 3 of them observed seasonal patterns in the VH/VV values. Among coniferous sites, only the D16.WREF and D19.BONA sites showed significant annual changes in the time series of the two combinations at the same time. S1 data exhibited higher accuracy in deciduous forest phenology, but for coniferous forests, seasonal changes were less significant. Figure 5 displays the VH/VV and VV-VH time series of a typical deciduous forest site smoothed by FT and DL methods, as well as the Gcc time series obtained from the PhenoCam Dataset. The time series of the two tested polarization combinations both showed seasonal variations comparable to Gcc, but VV-VH showed less seasonal variability over the growing season. Both band combinations could extract phenological metrics consistent with the Gcc series. PhenoCam data in coniferous forest sites were partially missing, and at sites with complete observed data, the VH/VV time series could not extract sufficient phenological metrics to support the ability of phenological metric estimation. Figure 6 shows the results of FT applied on VH/VV and VV-VH for one coniferous forest site. It is obvious that the VV-VH graph followed a similar trend as Gcc, although the slope  
Figure 4: Backscatter coefficient of S1 dual polarization in (**a**,**c**). D02.SCBI (a deciduous site); (**b**,**d**). D02.BLAN (a coniferous site). of rising and falling is relatively flat; however, the VH/VV combination had no obvious fluctuation in value except for small increases in spring 2017 and summer 2019. Although no literature was found discussing the sufficiency of the differencing combination, some recent papers have all used this combination for phenological research [21; 46], which is consistent with the conclusion of this study. Figure 5: Seasonal patterns of Sentinel-1 for a typical deciduous forest (D02.SCBI) between 2017 and 2019. (**a**): VH/VV processed by FT and DL; (**b**): VV-VH processed by FT and DL; and (**c**): Gcc. Red lines: FT function; blue lines: DL function; and dashed line: phenological metrics.