Dai Kaixuan, Cheng Changxiu, Li Bin, Xie Yun, Gomez Jose Alfonso, Wang Zheng, Wu Xudong
State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, P. R. China.
National Tibetan Plateau Data Center, Beijing, 100101, P. R. China.
Sci Data. 2025 Aug 6;12(1):1371. doi: 10.1038/s41597-025-05723-0.
Changing crop patterns are primary driver of land use change and can impact global atmospheric cycles. While existing studies have mapped the distribution of several crops in China, harvest area maps for a complete set of crops over the past decades are lacking. This study pioneered the development of a spatiotemporal dataset of harvest area maps for 16 crop types in China at a 1-km resolution from 1990 to 2020 with 5-year intervals. Prefecture-level crop statistics were allocated to grids based on synthetical crop suitability score, which is evaluated by natural and socioeconomic factors. County-level validations demonstrated the built dataset is highly consistent with statistics, especially for primary grains and oilseed. Moreover, crop harvest area at sub-pixel level can better represent gradient changes within urban-rural zones. The built crop maps revealed the harvest zones for maize, rice and soybeans in Northern China have steadily expanded since 1990. This dataset fully supports identification of spatiotemporal changes in China's crop patterns and can serve as critical input for biogeochemical and agricultural models.
作物种植模式的变化是土地利用变化的主要驱动因素，并且会影响全球大气循环。虽然现有研究已绘制了中国几种作物的分布情况，但过去几十年间一套完整作物的收获面积图尚付阙如。本研究率先开发了一个时空数据集，该数据集包含了1990年至2020年期间中国16种作物类型、分辨率为1公里、时间间隔为5年的收获面积图。地级作物统计数据根据综合作物适宜性得分分配到各个网格，该得分由自然和社会经济因素评估得出。县级验证表明，所构建的数据集与统计数据高度一致，尤其是对于主要谷物和油籽而言。此外，亚像素级别的作物收获面积能够更好地反映城乡区域内的梯度变化。所构建的作物图显示，自1990年以来，中国北方玉米、水稻和大豆的收获区一直在稳步扩大。该数据集充分支持对中国作物模式时空变化的识别，并可作为生物地球化学和农业模型的关键输入数据。
