## 1 Introduction  
As the global population expands and dietary patterns evolve, the cultivation area and production of garlic have steadily increased [1]. China consistently dominates in garlic production, exportation, and consumption, positioning garlic as a critical crop within the nation's economy [2]. In recent years, garlic production has exhibited a steady increase, underscoring the significance of promptly accessing information regarding its planted area and distribution to inform assessments of garlic prices and food security. Conventional garlic planting area surveys rely on field sampling techniques, which are characterized by high costs, prolonged sampling periods, and challenges in providing timely information on garlic cultivation [3]. The maturation of the remote sensing industry offers a solution, enabling rapid, large-scale, and cost-effective monitoring of garlic cultivation through satellite remote sensing data [4; 5; 6]. In China's primary garlic-producing regions, mixed cropping of garlic with other crops is prevalent, resulting in numerous mixed pixels in low spatial resolution remote sensing imagery. This phenomenon poses a challenge to accurately identifying garlic cultivation areas. Therefore, the utilization of high spatial resolution imagery emerges as the preferred approach for precisely delineating the spatial distribution of garlic within these key production zones [7; 8; 9]. Furthermore, the predominant mixed cropping of garlic with winter wheat and rape in the study area yields similar spectral signatures during specific stages of vegetation growth, leading to the manifestation of \"foreign body in the same spectrum\" in multispectral images [10]. Consequently, discriminating between garlic, rape, and winter wheat solely based on multispectral images at a particular stage of the vegetation growth cycle becomes inherently challenging, significantly impeding garlic identification accuracy. The duration and diversity of time series data for agricultural crops are directly correlated with the complexity of ground vegetation types [11; 12; 13; 14]. Leveraging remote sensing imagery provided by satellites to form time series data enables the rapid and precise extraction of crop types. Time series imagery has found widespread application in classifying both single and multiple crop types [15; 16; 17; 18], predominantly by constructing the NDVI (normalized difference vegetation index) time series from remotely sensed optical imagery to serve as an indicator of crop phenology [19; 20; 21; 22; 23]. However, time series data typically encompass a significant period during which environmental factors are complex and variable, and the crop state undergoes changes. Therefore, relying solely on NDVI as an indicator may not be universally applicable across all environments. To establish a more robust basis for classification, scholars have explored alternative indices to mitigate environmental influences and thereby enhance classification accuracy. For instance, the Enhanced Vegetation Index (EVI) [24] and Modified SAVI (Modified Soil Adjusted Vegetation Index, MSAVI) [25] have been investigated. Through the integration of various indices to construct new time series data, it becomes feasible to more accurately reflect crop growth dynamics. Although significant progress has been made in vegetation classification using time series data from either active or passive remote sensing images, both modalities possess distinct advantages and limitations. Obtaining high spatial and temporal resolution time series data exclusively from one type of remote sensing image remains challenging. Optical imagery, often utilized for constructing time series curves over the crop growth period, is susceptible to cloud cover and shadowing, diminishing its usability [26; 27]. On the other hand, SAR is sensitive to environmental factors such as rain, snow, and atmospheric humidity, potentially compromising image quality under adverse weather conditions. [PERSON] et al.