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

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.

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Publication Details

Journal
Scientific Data
Published
2024-12-18
DOI
https://doi.org/10.1038/s41597-024-04248-2
Citations
21
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
9.64

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

Bingfang Wu, Xingli Qin, Fuyou Tian, Hongwei Zeng et al.
21 citations
Scientific Data
Remote Sensing in Agriculture
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article

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

Bingfang Wu, Xingli Qin, Fuyou Tian, Hongwei Zeng, Miao Zhang
article en
21 citations

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.

Scientific DataVol. 11(1)
Chinese Academy of Sciences (CN), Institute of Remote Sensing and Digital Earth (CN), Aerospace Information Research Institute (CN), University of Chinese Academy of Sciences (CN)
U.S. Department of Agriculture, National Bureau of Statistics of China, European Commission, National Natural Science Foundation of China, Chinese Academy of Sciences, Youth Innovation Promotion Association of the Chinese Academy of Sciences, Alliance of International Science Organizations, U.S. Geological Survey, Youth Innovation Promotion Association
Zero hunger
Openalex Percentile: Top 2%
Remote Sensing in Agriculture
9.64
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