## 1 Introduction  
Remote sensing technology can provide important data for crop monitoring. This can help guide agricultural production and management [1]. Zhaotong City, located in southwestern China, boasts the largest apple production area. By 2022, its apple cultivation area had exceeded 800,000 acres, establishing it as a vital component of the local apple industry [2]. However, because of the complex topography of the Zhaotong City area, apple orchards are scattered, and field surveys are difficult to conduct. Simultaneously, with the implementation of the western rural revitalization strategy, sustainable development of the apple industry in the Zhaotong City area needs to be implemented. Therefore, it is very necessary to study the remote sensing identification method of apple orchards in the Zhaotong area. This can provide a theoretical basis for the sustainable development of the Zhaotong apple industry, which is of great significance to local and national economic development. Scholars worldwide have mostly researched crops, such as wheat, corn, and rice, all of which have achieved good results [3; 4; 5; 6]. However, little research and corresponding progress have been made in apple orchard extraction [7; 8; 9]. While monitoring crops via remote sensing in southwestern China, it was observed that the critical growth period is frequently obstructed by clouds and rain, significantly impacting the accuracy and timeliness of monitoring and hindering the acquisition of optimal temporal optical crop images [10]. Furthermore, the complex and fragmented terrain in the southwest of China is often affected by information, such as brightness and shadows, which exacerbates the \"pepper and salt\" phenomenon and leads to misclassification. Therefore, it is necessary to build a remote sensing method to identify apple orchards to solve these problems. During the classification of apple orchards, the precision of the classification is greatly influenced by the samples selected and the input features. The spatial homogeneity of the sample selection allows it to be representative. The selection of input features that enable the classification algorithm to learn characteristics specific to apple orchards is the key to improving the accuracy of apple orchard recognition. Numerous studies have shown that time-series data, as an input feature, can effectively deal with the \"same spectrum and different objects\" problems and positively impact the final classification results. For example, based on the GaoFen-1 image data of time series, [PERSON] et al. adopted the time-series remote sensing image classification method of DTW-K Means to classify tree species in the Harbin experimental forest farm, and achieved good results [11]. [PERSON] et al. evaluates how a time-weighted dynamic time warping (TWDTW) method that uses Sentinel-2 time series performs when applied to pixel-based and object-based classifications of various crop types in three different study areas [12]. With the continuous development of radar technology, Synthetic Aperture Radar (SAR) technology has been developed and has wide applications in crop monitoring due to its all-weather, all-day observation and independence from clouds and rain. [PERSON] et al. demonstrate the synergy of optical and radar satellite data for land cover mapping in tropical regions [13]. [PERSON] et al. analyzed the usefulness and potential of SAR data for vegetation characterization and plant physiological parameter estimation [14]. Meanwhile, the spectral angle mapper classifies features based on their spectral properties [15], focusing on the differences among bands in the image, and does not rely on prior knowledge. To a certain extent, this method reduces the influence of factors, such as the \"same spectrum and different objects.\" [PERSON] et al. used MODIS-EVI data to identify soybean and corn using the Spectral Angle Mapper [16]. [PERSON] et al.