## 5 Conclusions  
The spatial mapping of orchards is essential in monitoring tree growth, estimating yields, and managing planting. This study used S-1 dual-polarization multi-temporal data to design a new decision tree method for the extraction of the spatial distribution information of various fruit trees by combining growth stage features and backscattering coefficients. There was a significant difference in the characteristics of mature fruit trees when compared to other forest and vegetation types in the research area. During this period, there were changes in the size and color of the fruit. The spectral data of the vegetation on fruit trees has undergone significant alterations, enabling the recognition of the spatial distribution of various fruit tree categories. Characteristics for the identification of fruit trees exhibit different separability qualities in six polarization combinations: VV, VH, VV + VH, VV - VH, VH/VV, and (VH - VV)/(VH + VV). The top five identifying features, \(C\_D\), \(Tb\), \(Hv\), \(Lo\), and \(Cr\), have been found to be the most significant markers for differentiation between fruit trees, cropland, other vegetation, and each other using VH polarization. Consequently, VH polarization is deemed the most critical polarization in the classification model as it demonstrates greater sensitivity to the identification features.  
Litchi, Logan, and citrus plantations were widely distributed throughout the region. However, due to the monsoon's impact on Maoming during the fruit tree growth cycle, there was insufficient visible image data and a lack of spectral information in the growth stage period. To address this issue, this study employed S-1 microwave data to create a time-series model that identified the essential growth stage characteristics of fruit trees, and it evaluated the model's accuracy in distinguishing between different fruit trees. The proposed recognition model achieved overall accuracy of 90.34% in recognizing fruit trees, with a kappa coefficient of 0.84. The method offers valuable theoretical support for the remote sensing identification of fruit trees in cloudy and rainy conditions, as well as serving as a reference and foundation for perennial evergreen crops in the tropics and subtropics.  
Conceptualization, L.H. and D.L.; Data curation, X.W., S.W. and Z.Z.; Formal analysis, X.W.; Funding acquisition, L.H.; Investigation, D.L. and W.Y.; Project administration, L.H.; Resources, D.L. and W.Y.; Software, L.H., X.L., X.G. and J.F.; Supervision, L.H. and Z.F.; Validation, D.L.; Visualization, S.W. and Z.Z.; Writing--original draft, X.W.; Writing--review and editing, L.H., D.L. and Z.F. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Natural Science Foundation of Shandong Province, grant numbers ZR2020 MD018 and ZR2020 MD015, and by the National Natural Science Foundation of China, grant number 42171413; Guangdong Province Agricultural Science and Technology Innovation and Promotion Project (2023 KJ102).  
The data presented in this study are available on request from the author.  
The authors would like to thank the anonymous reviewers for their helpful comments that improved the manuscript.  
Author [PERSON] was employed by the Maoming Meteorological Observatory of Guangdong Province. Author [PERSON] was employed by company Satellite (SHANDONG) Technology Group Co., Ltd. Author [PERSON] was employed by company GEOVIS Wisdom Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Figure 11: Differentiability of spectral features of lithi and other forest and vegetation under different polarizations. (**a**) VH polarization difference, (**b**) VV polarization difference, (**c**) VH + VV polarization difference, (**d**) VV \(-\) VH polarization difference, (**e**) VH/VV polarization difference, (**f**) (VH \(-\) VV)/(VH + VV) polarization difference.