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2010年至2020年分辨率为10公里的全球网格化作物产量数据集。

Global Gridded Crop Production Dataset at 10 km Resolution from 2010 to 2020.

作者信息

Qin Xingli, Wu Bingfang, Zeng Hongwei, Zhang Miao, Tian Fuyou

机构信息

Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China.

College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China.

出版信息

Sci Data. 2024 Dec 18;11(1):1377. doi: 10.1038/s41597-024-04248-2.

DOI:10.1038/s41597-024-04248-2
PMID:39695306
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11655857/
Abstract

The global gridded crop production dataset at 10 km resolution from 2010 to 2020 (GGCP10) for maize, wheat, rice, and soybean was developed to address limitations of existing datasets characterized by coarse resolution and discontinuous time spans. GGCP10 was generated using a series of adaptively trained data-driven crop production spatial estimation models integrating multiple data sources, including statistical data, gridded production data, agroclimatic indicator data, agronomic indicator data, global land surface satellite products, and ground data. These models were trained based on agroecological zones to accurately estimate crop production in different agricultural regions. The estimates were then calibrated with regional statistics for consistency. Cross-validation results demonstrated the models' performance. GGCP10's accuracy and reliability were evaluated using gridded, survey, and statistical data. This dataset reveals spatiotemporal distribution patterns of global crop production and contributes to understanding mechanisms driving changes in crop production. GGCP10 provides crucial data support for research on global food security and sustainable agricultural development.

摘要

已开发出2010年至2020年分辨率为10公里的全球网格化作物产量数据集（GGCP10），用于玉米、小麦、水稻和大豆，以解决现有数据集分辨率粗糙和时间跨度不连续的局限性。GGCP10是使用一系列经过自适应训练的数据驱动作物产量空间估计模型生成的，这些模型整合了多个数据源，包括统计数据、网格化产量数据、农业气候指标数据、农艺指标数据、全球陆地表面卫星产品和地面数据。这些模型基于农业生态区进行训练，以准确估计不同农业区域的作物产量。然后根据区域统计数据对估计值进行校准，以确保一致性。交叉验证结果证明了这些模型的性能。使用网格化数据以及调查和统计数据对GGCP10的准确性和可靠性进行了评估。该数据集揭示了全球作物产量的时空分布模式，有助于理解驱动作物产量变化的机制。GGCP10为全球粮食安全和可持续农业发展研究提供了关键数据支持。

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