CropLayer: a 2 m resolution cropland map of China for 2020 from Mapbox and Google satellite imagery

作者：Hao Jiang, Mengjun Ku, Xia Zhou, Zheng Qiong, Yangxiaoyue Liu, Jianhui Xu, Dan Li, Chongyang Wang, Jiayi Wei, Jing Zhang, Shuisen Chen, Jianxi Huang · 发表于：Earth system science data · 年份：2025 · DOI：10.5194/essd-17-6703-2025 · 被引用次数：3 · 研究领域：Remote Sensing in Agriculture、Remote-Sensing Image Classification、Land Use and Ecosystem Services

Abstract. Accurate and detailed cropland maps are essential for food security, yet existing products for China exhibit substantial discrepancies. This study presents CropLayer, a 2 m resolution cropland map of China for 2020, developed from Mapbox and Google satellite imagery. The framework comprises three key stages: (1) image quality assessment (IQA) using a ResNet model to compensate for missing acquisition metadata; (2) cropland extraction via an active learning strategy guided by a Mask2Former segmentation model and XGBoost-based semantic correctness evaluation; and (3) integration of Mapbox and Google results through an XGBoost model informed by four feature groups: Geography, IQA, Regional Property, and Consistency. A three-level validation scheme (pixel, block, and region) ensures robust and interpretable accuracy across spatial scales. CropLayer achieves a pixel-level accuracy of 88.73 %, a block-level semantic correctness of 96.5 %, and provincial-level consistency, with 30 out of 32 provinces showing area estimates within ±10 % of official statistics. In comparison, only 1–9 provinces meet this criterion across eight existing datasets. CropLayer provides a reliable, high-resolution baseline for agricultural structure analysis, yield estimation, and land use planning in China. The CropLayer dataset is available at https://doi.org/10.5281/zenodo.14726428 (Jiang et al., 2025).