## 4 Discussion
### Summary and Future Directions  
A comparison of classification results indicates that, except in Pizhou where the accuracy of the commonly used NDVI time series data was nearly equivalent to that of other features, the overall accuracy of NDVI fell short by more than three percentage points compared to the optimal feature combinations in other regions, with the gap exceeding five percentage points in most areas. Compared to previous studies, the method proposed in this paper achieves higher crop classification accuracy, with overall improvements ranging from 1% to 3% [64; 65]. The extraction of garlic crop distribution and area within the study region marks a significant advancement in winter crop classification. This is particularly important because garlic, despite having a growth cycle similar to that of winter wheat, is often overlooked in studies focused on winter crop mapping [66; 67].  
In summary, the integration of time series data contributes to enhanced garlic classification accuracy, albeit with variations in performance across regions. While the spliced time series data improves classification accuracy in certain areas, its efficacy is diminished in a few other regions. Integration of optical data with Sentinel-1 SAR data aids accurate garlic identification [68; 69], although its impact is less pronounced in regions with better classification results. Lastly, the newly devised garlic fragmentation index facilitates analysis of extraction accuracy disparities across regions.  
However, this study has certain limitations and stochastic elements, which could serve as avenues for future research. A primary limitation lies in the reliance on pre-filtered remote sensing imagery for garlic classification. Despite yielding high accuracy results, this approach may still harbor errors that permit the filtration of a small number of garlic plots. Although this study judging anomalies via harmonic regression and patching time series data in the study area using previous data can effectively increase the validity of the data, this algorithm is not directly related to the surface data of the current year, and the integration of multi-sensor remote sensing data can be considered for study in the future, such as the STARFM algorithm. Additionally, green onions in the study area exhibit phenological characteristics similar to those of garlic. Although scallions are not commonly cultivated in the study area, their presence could potentially affect the accuracy of extraction.  
When applying the classification method across different regions, several limitations arise. Firstly, varying agricultural practices and crop structures in different areas can pose significant challenges for accurate classification. The spectral characteristics of crops can differ regionally, necessitating the collection of additional samples to account for these variations. Secondly, when expanding the classification to a larger area, a more extensive and evenly distributed sample set is required, which often involves supplementary visual sampling, increasing the complexity of the process. Additionally, the increased scale leads to larger datasets, and processing these on platforms such as GEE may result in limitations such as memory constraints and extended computation times. To mitigate these issues, optimizing the algorithm is essential to prevent processing delays or timeouts. The garlic classification results from the six counties demonstrate that different regions may require distinct combinations of classification features to achieve optimal results. Therefore, when applying this method to different regions, it is advisable to first test the effectiveness of various classification features before proceeding with the classification. However, there also exists a robust set of features that provide high accuracy and can be utilized when precision requirements are less stringent.