## 2 Related Works  
Recent advancements in Earth observation technologies have paved the way for remote sensing to offer innovative approaches and scientific methods for the extraction of agricultural plastic covers. Remote sensing technology, with its advantages of extensive coverage, all-weather capability, and real-time monitoring [19], provides an efficient, non-invasive, and cost-effective solution for large-scale, multi-temporal APC extraction, significantly enhancing research and monitoring efficiency and accuracy. The resolution of remote sensing imagery is critical for APC extraction. Low-resolution imagery often lacks sufficient spatial detail, hindering the accurate differentiation of land cover types and resulting in low classification accuracy [20]. In contrast, high-resolution imagery offers finer details and clearer structures, making it more suitable for precision extraction tasks [11]. As technology advances, researchers are increasingly relying on high-resolution imagery to improve the accuracy and precision of APC extraction. In remote sensing-based APC extraction, shade houses and PCGs share similar structural and material characteristics, but research on shade house extraction remains scarce. Therefore, this paper will leverage the latest advances in PCG extraction research to analyze and address the key technical challenges in shade house extraction. Remote sensing techniques for extracting PCGs fall into two main categories: object-oriented and pixel-based approaches. The object-oriented approach involves segmenting an image into superpixel blocks, which are then classified using rule-based features to identify PCGs. For instance, [PERSON] et al. developed a threshold model using Landsat 8 OLI imagery, combining object-oriented segmentation with spectral analysis. By selecting 7 features, they successfully extracted PCGs in Xuzhou, Jiangsu Province, for the years 2014 and 2018 [21]. However, the accuracy of object-oriented methods is often limited by the size of the superpixel blocks. Large superpixel blocks may cause confusion between different land cover types, while very small superpixel blocks can result in excessive fragmentation of the classification [22]. Consequently, recent studies on remote sensing-based PCG extraction have increasingly favored pixel-based approaches, especially spectral index and deep learning methods. Spectral index methods typically construct feature indices using different spectral bands of remote sensing imagery to differentiate PCGs, and utilize techniques such as threshold comparison to distinguish PCGs, offering the advantages of fast computation and easy application. For instance, during winter, the temperature differences inside PCGs often lead to condensation, causing the PCGs to exhibit water-related characteristics in remote sensing imagery. Based on this, [PERSON] et al. proposed an enhanced water index (EWI) using multiple spectral bands of Landsat imagery to extract PCGs in Jiangmen, Guangdong, accurately distinguishing them from other land cover types [23]. Similarly, [PERSON] et al. introduced a new spectral index designed to suppress spectral signals from vegetation and enhance the spectral differences between PCGs and other land cover types [24]. The proposed method yielded excellent extraction results on both Landsat and QuickBird-2 images. [PERSON] et al. developed the Advanced Plastic Greenhouse Index (APGI), which is effective for the large-scale identification of PCGs, providing good mapping results in multiple study areas globally [25]. The index shows a significant difference between PCGs and the background, and uses a single-threshold segmentation approach, making it efficient for mapping. However, the optimal threshold varies across different study areas. In large-scale extraction tasks, manually adjusting the index threshold is time-consuming and inefficient, leading to lower overall accuracy.