## 5 Discussion  
The complex topography and cloudy and rainy weather in Zhaotong City are important reasons for the difficulty in accurately identifying apple orchards. Selecting samples and features are two important factors affecting the classification results during the classification process. However, during sample selection, the influence of human and objective factors prevents effective manual sampling. Therefore, based on visual interpretation, to obtain the sample selection area of the study space, we used the random generation of samples through the Create Random Points Tool in ArcGIS 10.6, which attempts to solve these problems regarding feature selection. This study proposes an improved spectral angle feature using the VV and VH bands of 12 months of SAR images as the input of the Spectral Angle Mapper and obtains a spectral angle feature with time-series characteristics that is unaffected by clouds [36; 37]. Based on the GEE platform, a series of features were constructed, and the Ganhe Reservoir area in Zhaoyang District of Zhaotong City was classified by random forest classification. From the results and accuracy of apple orchard identification, it is poor at distinguishing all land use types in the study area only by using improved spectral angle features or texture features. The feature combination of the RGB band and improved spectral angle feature can effectively improve the separability of the feature space, and compared with the feature combination of RGB and NDVI, the combination of RGB and the improved spectral angle feature has a better recognition effect for apple orchards. The experimental results show that the method of combining texture features, such as [PERSON] et al. [7], has poor accuracy in the study area. The combination of spectral angle features and spectral features has obvious advantages in identifying different ground features, and can identify the types of ground features contained in the thin cloud coverage area to a certain extent. However, there are a few 'broken pixels' in the classification result. The reason for this may be that in October, some of the cultivated land pixels in the study area are highly similar in spectrum and space. In addition, the improved spectral angle features of this paper are calculated from 12 months of monthly average SAR images. This will cause these pixels to have a certain similarity to the apple orchard pixels in the improved spectral angle features, resulting in a \"broken pixels\". Therefore, in the case of similar spectral and spatial information, using the monthly average SAR image with a large pixel value difference between apple orchards and other cultivated land to calculate the improved spectral angle features can alleviate the problem of \"broken pixels\". Most of the classification results for other lands were classified as croplands, which differs significantly from the results obtained from visual interpretation; this is an important factor affecting the OA of the RF classification. In subsequent work on identifying apple orchards in the entire Zhaotong City area, it was not easy to interpret visually because of the large scope of the images. Therefore, we could use other data products with higher accuracy to complete the selection of sampleswithout manual intervention, which could effectively avoid the misclassification of land classes due to visual interpretation. Meanwhile, there are numerous mixed pixels present within the study area. Therefore, it is possible to use the hybrid pixel decomposition technique [38] for the decomposition of pixels within the study area, which allows for an increase in the accuracy of the classification. In addition, when producing such special spectral angle features, the effects of SAR images on apple orchards and other land classes in particular months can be considered to obtain better quality spectral angle features.