National-scale mapping of plastic-mulched farmlands in China based on deep semantic segmentation and Sentinel-2 imagery

Plastic mulching films are widely utilized in agriculture around the globe to increase soil temperature, retain soil moisture, and prevent pests. However, its large-scale and long-term application causes several environmental issues, including soil microplastic accumulation and “white pollution”. Therefore, obtaining reliable spatial distribution data on plastic-mulched farmlands (PMFs) is essential, particularly at a large scale. This study aimed to provide the first national-scale PMFs map of China with a high resolution of 10 m. Specifically, this study proposed a hybrid framework for PMFs mapping, which employed the Vision Mamba UNet (VM-UNet) semantic segmentation model for both updating weak labels and national-scale PMFs mapping. First, a web crawler was used to gather news and academic literature related to China's PMFs, extracting temporal and spatial distribution data on plastic mulch films. Afterwards, a national 1° grid system was established for data organization, where initial weak labels were generated via Google Earth Engine and Sentinel-2 imagery. These labels were subsequently refined through an active learning strategy within a transfer learning framework. Finally, VM-UNet was adopted for the national-scale prediction of China's plastic-mulched farmland. Experimental results demonstrate that China-PMF-10 dataset achieved a user's and producer's accuracies of 85.90 ± 1.25% and 78.48 ± 5.37% for PMFs, respectively. China's total PMFs area in 2020 was estimated at approximately 6.06 ± 0.42 Mha (95% CI). Furthermore, statistical significance testing confirmed that the Hu Line emerged as a critical boundary, with more extensive PMFs and higher PMFs-to-cropland ratios to its west. This study provides a scientific basis for sustainable agricultural management and plastic pollution mitigation in China, and offers methodological insights for large-scale PMFs monitoring in other regions.