## 1 Introduction
The construction of backward scattering coefficient models for long time series under S-1 polarization conditions, the comparative analysis of the growth cycles of different fruit trees, and the search for their growth stage characteristics for identification [26; 27] are important tasks. However, it may be challenging to distinguish different objects when they share similar backscattering characteristics [2]. In this case, a single piece of S-1 data would not be able to distinguish the different features; thus, there is a need to combine this with visible data and use the spectra of the two different data to differentiate between features that are easily intermixed under the same growth stage feature [28; 29]. The mapping of fruit trees is significantly affected by the validity of observations, which must be available to accurately describe the fruit tree phenology. However, many fruit tree morphological characteristics in tropical and subtropical regions are similar, making it easy to confuse them with single-time data [17]. For the identification of different fruit trees, the most important aspect is to construct a long time series to find the key features and determine the optimal identification features according to the sensitivity of the growth stage under different polarizations [2]. C-band SAR data from S-1 are unaffected by cloudy and rainy conditions due to its high penetration and its ability to acquire data at nighttime, as well as its sensitivity to changes in vegetation structure, compared with optical remote sensing data [30; 31]. Many studies have demonstrated the efficacy of using S-1 data to identify annual crops' (e.g., soybean, corn, and rice) phenology [32; 33; 34; 35]. Studies identifying the characteristics of perennial fruit trees' phenology are rarely reported. Some researchers have demonstrated variations in fruit tree species with varying growth stage features. SAR is highly sensitive to structural changes in the canopies of fruit trees [36], providing a suitable basis for this study. Maoming City is located in the southern part of China, a region dominated by agriculture and planted with large areas of fruit trees (e.g., litchi, longan, and citrus orange, etc.). These fruit trees belong to subtropical evergreen broadleaf forests that share similar spatial and spectral features but vary significantly in their fruiting seasons, providing a suitable area for this study [37]. This study's goals were two-fold: (1) to extract the key growth stage characteristics of typical fruit trees from microwave data; (2) to construct a fruit tree identification model appropriate for evergreen broadleaf trees in subtropical and tropical regions by utilizing these key growth stage features. In this study, we used Sentinel-1 long-time-series SAR data to construct the growth stage features of fruit trees. We analyzed and identified the optimal recognition features of litchi, longan, and citrus orchards based on the growth stage features. Then, we developed an improved method for the extraction of spatial distribution information of decision tree orchards by combining single classification recognition algorithms. The methodology can provide a point of reference for the retrieval of perennial fruit trees in tropical and subtropical areas, whilst offering essential technical assistance for the surveillance and meticulous administration of litchi plantations.