Abstract. In China, the demand for a more precise perception of the national land surface has become most urgent given the pace of development and urbanization. Constructing a very-high-resolution (VHR) land-cover dataset for China with national coverage, however, is a non-trivial task and thus, an active area of research impeded by the challenges of image acquisition, manual annotation, and computational complexity. To fill this gap, the first 1-meter resolution national-scale land-cover map of China, SinoLC-1, was established using a deep learning-based framework and open-access data including global land-cover (GLC) products, open street map (OSM), and Google Earth imagery. Reliable training labels were generated by combining three 10-meter GLC products and OSM data. These training labels and 1-meter resolution images derived from Google Earth were used to train the proposed framework. This framework resolved the label noise stemming from a resolution mismatch between images and labels by combining a resolution-preserving backbone, a weakly supervised module, and a self-supervised loss function, to refine the VHR land-cover results automatically without any manual annotation requirement. Based on large storage and computing servers, processing the 73.25 TB dataset to obtain a final SinoLC-1 land-cover product covering the entire land surface of China, ~9,600,000 km2, took about 10 months. The SinoLC-1 product was validated using a visually interpreted validation set including 106,852 random samples and a statistical validation set collected from the official land survey report provided by the Chinese government. The validation results showed SinoLC-1 achieved an overall accuracy of 73.61 % and a kappa coefficient of 0.6595. Validations for every provincial region further indicated the credible accuracy of this dataset across whole China. Furthermore, the statistical validation results indicated SinoLC-1 conformed closely to the official survey reports. In addition, SinoLC-1 was qualitatively compared with five other widely used GLC products. These results indicated SinoLC-1 had the highest spatial resolution, the most accurate land-cover edges, and the finest landscape details. In conclusion, as the first 1-meter resolution national-scale land-cover map of China, SinoLC-1 delivered accuracy and provided primal support for related research and applications throughout China. The SinoLC-1 land-cover product is freely accessible at https://doi.org/10.5281/zenodo.7707461 (Li et al., 2023).