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

Abstract

Plastic-covered greenhouse (PCG) is widely used in agricultural production due to its temperature control, water conservation, and wind protection characteristics, significantly enhancing crop yields and economic benefits. However, its long-term and extensive use can lead to environmental issues, such as the accumulation of local toxic gases and the degradation of soil physicochemical properties. Therefore, obtaining a comprehensive distribution of PCGs is essential. To monitor PCGs on a large scale, this study developed a novel approach for producing the first global 10 m PCG dataset (Global-PCG-10) with high-quality. Firstly, the globe was divided into multiple 5° grids, and grids for classification were organized based on global cropland layer. Then, multi-temporal Sentinel-2 data and initial labels of PCGs were obtained through Google Earth Engine (GEE) to create a training set for deep learning. Next, initial labels were optimized with the active learning strategy combined with the deep learning model, APC-Net. Finally, the PCG classification results were predicted, spatially analyzed, and compared with publicly released land use and land cover (LULC) datasets. Experimental results indicate that the proposed Global-PCG-10 dataset (Niu et al., 2024) has a high overall accuracy of 98.04 % ± 0.12 %. The global area of PCGs is 14 259.85 km2, and 69.24 % of PCGs are located in Asia, covering around 9874.51 km2. China has the largest PCG area of 8224.90 km2, accounting for 57.67 % of the globe and 83.29 % of Asia. Comparisons with other LULC datasets revealed that PCGs, which should be classified as cropland, are often misclassified as bareland, impervious surfaces, ice/snow, etc.