Preprints
https://doi.org/10.5194/essd-2026-24
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2026-24
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
27 Jan 2026
| 27 Jan 2026
Status: this preprint is currently under review for the journal ESSD.
GlobalRice20: A 20 m resolution global paddy rice dataset for 2015 and 2024 derived from multi-source remote sensing
Abstract. Accurate, high-resolution spatial data of paddy rice are indispensable for assessing global food security and tracking progress toward Sustainable Development Goal 2 (Zero Hunger). However, a consistent global rice map at medium-to-high resolution has been lacking due to the challenges of cloud contamination and the temporal irregularity of multi-source satellite archives. Here, we present GlobalRice20, the first global 20m resolution paddy rice dataset for the years 2015 and 2024. We developed a "Time-Series-to-Vision" framework (T2VRCM) that transforms heterogeneous optical and SAR time-series into standardized 2D visual representations, specifically designed to handle irregular sampling and missing modalities. The dataset was produced using Sentinel-1/2 and Landsat imagery and rigorously validated against 164,000 reference samples, achieving an overall accuracy of 92.33 %. Cross-comparison with national agricultural statistics reveals a high coefficient of determination (R2 = 0.91 for 2024), confirming the dataset's reliability for national-scale accounting. Spatiotemporal analysis during the first decade of SDGs (2015–2024) indicates a 6.6 % expansion in global rice area, with Africa exhibiting the most significant growth (15.7 %). This dataset fills a critical gap in global agricultural monitoring, providing a baseline for analyzing food production trends and climate impacts. The dataset is available at https://doi.org/10.5281/zenodo.18168302 (Zhang et al., 2026).
How to cite. Zhang, H., Song, M., Ding, Y., Xie, Y., Guo, H., Xu, L., Ge, J., Zhu, Y., Wang, S., Guo, Z., Wang, Z., Duan, H., Zuo, L., and Huang, W.: GlobalRice20: A 20 m resolution global paddy rice dataset for 2015 and 2024 derived from multi-source remote sensing, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2026-24, in review, 2026.
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GlobalRice20: A 20 m resolution global paddy rice dataset for 2015 and 2024 derived from multi-source remote sensing Hong Zhang et al. https://doi.org/10.5281/zenodo.18168302
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Hong Zhang
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Mingyang Song
CORRESPONDING AUTHOR
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Yinhaibin Ding
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Yazhe Xie
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Huadong Guo
CORRESPONDING AUTHOR
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Ji Ge
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Yafei Zhu
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Shenghan Wang
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Zihuan Guo
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Zhe Wang
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Haoxuan Duan
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Lijun Zuo
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Wenjiang Huang
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Short summary
Accurate rice farming data is vital for global food security. We created GlobalRice20, a global map of paddy rice at 20-meter resolution for 2015 and 2024. By utilizing satellite imagery and advanced processing to overcome cloud issues, this dataset fills a critical gap in agricultural monitoring. It provides a reliable baseline for analyzing food production trends and helps policymakers track progress toward the Sustainable Development Goal of Zero Hunger.
Accurate rice farming data is vital for global food security. We created GlobalRice20, a global...
Altmetrics
This study developed the GlobalRice20 dataset with 20m resolution for 2015 and 2024, constructed a Time-Series-to-Vision framework (T2VRCM) to address cloud contamination and irregular time-series issues in satellite data, and produced a high-accuracy global paddy rice distribution product, which is highly important and practically significant for global food security assessment, agricultural monitoring. The detailed comments are as follows.