## 1 Introduction
demonstrated the potential of combining active and passive remote sensing for garlic research by extracting garlic using both stepwise and simultaneous approaches, yielding favorable results [28]. While prior classification studies have primarily focused on enhancing classification accuracy [29; 30], insufficient attention has been devoted to exploring the factors influencing classification accuracy. However, investigating these factors may offer novel insights for improving classification accuracy. There is no doubt that ground crop cultivationcharacterized by good regularity and uniformity is advantageous for remote sensing image classification. However, due to the unique circumstances in our country, it is often challenging to achieve this ideal situation. There are differences in the cultivation patterns between garlic and winter wheat in the study area, with garlic cultivation being less regular and more dispersed. To accurately describe and understand the impact of garlic dispersion on classification accuracy, further investigation into the degree of garlic dispersion is warranted. In recent years, the application of machine learning methods in remote sensing classification has seen rapid development. Among these methods, random forest (RF), support vector machine, extreme gradient boosting, decision tree, and deep learning are commonly employed. Random forest, in particular, has demonstrated excellent classification performance in remote sensing classification and has garnered widespread recognition from previous researchers [31; 32]. While there exists a growing body of literature and practical experience in land cover classification using time series image sets [33; 34], the focus of interest in this study lies in the identification and extraction of individual crops based on time series data. Unlike most past studies, the study area under consideration includes the presence of a same-season crop (winter wheat) with a phenological period very similar to the crop of interest, with mixing of the two being common. Distinguishing between these two types of crops using only optical time series data, strongly correlated with phenological characteristics, poses a significant challenge. Moreover, the complexity and diversity of vegetation types in the study area make it prone to the occurrence of the same spectral signature in remote sensing images. Consequently, achieving the desired extraction accuracy by using remote sensing images at a specific moment to differentiate crops with similar phenological characteristics is difficult [35; 36]. Additionally, traditional classification methods based on time series data have limitations and are particularly challenging in distinguishing between different crops with similar phenological characteristics. In this paper, we present a study aimed at extracting garlic in the study area through time series data, with a focus on the cultivation area and distribution of garlic, without making detailed distinctions between crops other than garlic. Situated in the eastern part of China, the study area predominantly features garlic and winter wheat as the main winter crops. The aims of this study were to address the following three research questions: (1) whether the superposition or splicing of temporal information can help improve the classification accuracy of garlic; (2) whether the superposition of optical data and Sentinel-1 SAR data can help identify garlic accurately; (3) whether the constructed garlic fragmentation index can effectively express the fragmentation of garlic distribution in different regions and help analyze the reasons for the differences in garlic extraction accuracy in different regions.