## 4 Discussion  
In this study, the time window for the differentiation of lithi, longan, and citrus fruit trees from other forests and vegetation was the respective maturity period, which was May-June for lithi, July for longan, and September-October for citrus [57]. The shading of foliage by expanding fruit during the ripening period can lead to the weakening of vegetation information [58]. The backscattering coefficient of fruit trees exhibits a discernible trough at the maturity stage, followed by a decreasing and then increasing trend. This characteristic differentiates fruit trees from other vegetation in the identified time frame. Among all the polarization combinations, the trend of identification features under VH polarization was the most obvious, and there was no strong separation between the identification features of the three types of fruit trees and other forest and vegetation in other polarization combinations [39; 59]. Therefore, the identification parameters of fruit trees and other forests and vegetation were equally optimal under VH polarization. This study found that \(C\_D\) features based on the crop harvesting and sowing periods are an effective means of distinguishing between cultivated land and vegetation, including lithi. According to Figure 11, the backscattering coefficient of a lithi orchard during its ripening period exhibited a pattern of low and high values within the optimal window period [55]. Furthermore, a distinct valley value was observed, which allowed for the effective differentiation of the lithi orchard from other vegetation by means of the \(Tb\) and \(Hv\) features during the optimal window period. During the ripening period, citrus and longan possess valley value characteristics, which allowed the \(Cr\) and \(Lo\) features to distinguish citrus and longan orchards from other vegetation at the optimal window for these fruits. This study introduced an improved decision tree model that effectively extracts the spatial distribution information of fruit trees. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Litchi** & **Other Forest and Vegetation** & **Cropland** & **Total** & **User’s Accuracy** \\ \hline Litchi & 265 & 11 & 4 & 280 & 94.64\% \\ Other forest and vegetation & 14 & 193 & 19 & 226 & 85.40\% \\ Cropland & 2 & 17 & 169 & 188 & 89.89\% \\ Total & 281 & 221 & 192 & 694 & - \\ Producer’s accuracy & 94.31\% & 87.33\% & 88.02\% & - & - \\  & Overall accuracy = 90.34\% & & & Kappa = 0.84 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of precision evaluation of classification results. The innovative combination of long-time-series SAR and high-spatial-resolution optical imagery was effectively utilized in this study, and the final extraction results were obtained relatively accurately. However, according to the vegetation extraction result map, there were still some problems in the application of the decision tree model used in this study to extract fruit trees, and the classification results depended on the similarity between the precise sample size and the topography of fruit trees. Moreover, the algorithm needs to be tested in a larger area with a complex planting structure [60]. Therefore, the final results may have included some incorrect scores and omissions. The main reason for the error was that there were different varieties of the same fruit trees grown in the study area, and there was a slight difference in the phenological period. Secondly, sparse woodlands and thickets were more similar to the spectrum of fruit trees, which made it easy to confuse them [61]. In the case of litchi fruit trees, there were early- and late-maturing lithci species, and the time window for the identification of the characteristics of litchi fruit trees in this study was in May and early June. However, for late-maturing varieties, this period was in the growing period, and not yet the period to produce recognition features [46].