Global-PCG-10: a 10-m global map of plastic-covered greenhouses derived from Sentinel-2 in 2020

This study constructed the first global PCGs dataset with a high spatial resolution of 10 meters based on deep learning and active learning. Specifically, we first divided the globe into 2,592 grids with a size of 5°×5° and retained those containing cropland as classification units. Then, we obtained pre-processed multi-temporal Sentinel-2 data through GEE and used random forest to generate initial labels to build the training dataset. Next, we developed a classification workflow that integrates the active-learning and deep learning to optimize weak labels, enhance model robustness, and reduce false positives. Subsequently, we used the trained deep learning model to predict the global distribution of PCGs, generating the Global-PCG-10 dataset.Experimental results show that the global PCGs area is approximately 14,259.85 km² in 2020. PCGs are mainly distributed between 30°N and 40°N, accounting for about 65.84% of the total area. Asia holds the most extensive area of PCGs, covering approximately 9874.5 km², accounting for 69.24% of the global total. China, not only has the largest area of PCGs in Asia but also ranks first worldwide, with a PCGs area of 8,224.90 km2, making up 57.67% of the global and 83.29% of the Asia. We validated the Global-PCG-10 dataset using 46,000 randomly sampled points, which indicates that the overall accuracy is satisfactory of 98.04% ± 0.12%.