## 1 Introduction  
With the extensive adjustment and optimization of the agricultural industrial structure, fruit trees have become one of the most important ways to increase incomes for farmers. Currently, the worldwide fruit tree planting area exceeds 64,859.3 thousand hectares and the production surpasses 887.027 million tons [1]. The fruit tree planting industry has become an essential component of agricultural production on a global scale [2]. Accurate and timely access to information on fruit tree planting areas is crucial for agricultural production [3]. Remote sensing is a valuable resource in terms of identifying and mapping fruit tree species at a large scale, especially in providing the growth stage information of fruit tree types [4; 5]. Over the last two decades, the use of various optical remote sensing data for the identification and mapping of fruit tree species at different spatial resolutions has been reported [6; 7]. MODIS and MERRA-2 data, with their high temporal and spectral resolutions, are commonly used in crop and forest mapping on a large scale [8; 9]. However, the spatial resolution of MODIS and MERRA2 data is too coarse for fruit tree species identification in regions with highly fragmented fields [10; 11]. Satellites such as Landsat 5/7/8, SPOT 5/6/7, and Sentinel-2 (S-2) can provide high-spatial-resolution data that are suitable for the accurate identification and mapping of fruit tree species in large areas [12; 13]. The current remote sensing technology allows for the acquisition of high-resolution and multispectral imagery data through drones and satellites [14; 15]. However, most fruit tree mapping methods used for the feature selection of classification algorithms are affected by the methods of observation (e.g., timing and frequency of cloud-free images acquired during fruit tree growing season). These methods were typically developed assuming that accurate and high-precision remote sensing data are available to describe the fruit tree phenology. However, optical remote sensing data, particularly high-spatial-resolution satellite data [9] (e.g., Landsat 5/7/8, SPOT 5/6/7, and S-2), are limited by the weather conditions (cloudy and rainy days) when characterizing the fruit tree phenology (i.e., specific phenology or time-series data of entire growing season) [10; 16; 17]. Even though UAV data are affected less by the weather conditions compared to satellites, their use in large areas is not feasible [18]. In addition, current methods of mapping fruit trees primarily rely on the spectral features and spatial features of fruit trees, which may not be the most optimal features for the identification of specific fruit tree species because of the comparable spectral and spatial features. To establish optimized features that preserve the classification accuracy and reduce the computational cost, various studies have reported optimal feature selection by different methods [19] (e.g., random forest, convolutional neural networks (CNNs), artificial neural networks (ANNs), extreme gradient boosting, recursive feature elimination [20; 21]), which may result in varying optimal feature subsets for the same identification task. Numerous studies have shown that the random forest, CNN, and ANN methods have higher accuracy and better performance in classification [21]. However, as a nonparametric supervised learning approach for classification and regression problems, the decision tree has been proven more effective in distinguishing various types of vegetation [22]. The spatial structure of the orchards of evergreen fruit trees in tropical and subtropical regions is similar, and the variety of fruit tree species leads to similar visible spectral information, which can easily cause confusion and brings difficulties to the identification of fruit trees [23; 24; 25]. Therefore, the identification of perennial fruit trees requires the use of Sentinel-1 (S-1) data, and synthetic aperture radar (SAR) is highly sensitive to structural changes in the canopies of fruit trees [22].