China-PMF-10: a 10-m national map of plastic-mulched farmlands in China of 2020 using deep semantic segmentation

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 PMF map of China with a high resolution of 10 m. Specifically, this study proposed a hybrid framework for PMF mapping, which employed the VM-UNet semantic segmentation model for both updating weak labels and national-scale PMF 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 86.30 ± 1.51% and 79.45 ± 1.90% for PMF, respectively. China’s total PMF area in 2020 was estimated at approximately 6.02 ± 0.58 Mha (95% CI). Furthermore, this study also found 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 PMF monitoring in other regions.