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.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Status: open (until 28 Apr 2026)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
- CC1: 'Comment on essd-2026-24', Ran Huang, 10 Mar 2026 reply
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CC2: 'Comment on essd-2026-24', Dailiang Peng, 22 Mar 2026
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The authors innovatively propose the T2VRCM model, which effectively resolves critical technical bottlenecks such as asynchronous sensor observations and missing data modalities. Based on this framework, they have successfully constructed the world's first 20-meter resolution global paddy rice dataset (GlobalRice20), demonstrating strong methodological innovation and practical application value. Overall, the experimental design of this study is comprehensive and rigorous, the figures are beautifully and intuitively crafted, and the public release of the related dataset provides invaluable data support for global food security assessment and the monitoring of UN Sustainable Development Goal 2 (SDG 2). To further enhance the overall quality and readability of the manuscript, I offer the following revision suggestions:
- The manuscript currently mentions the use of 164,000 global samples for the experiments, but the specific partitioning method for these samples is not clearly described. It is recommended to further clarify in the text exactly how these 164,000 global samples were divided into training and test sets.
- In the results analysis of Section 4.2, it is recommended to supplement the model's accuracy performance when utilizing only optical data (e.g., Sentinel-2 in 2024 or Landsat-8 in 2015). Furthermore, please quantitatively analyze the specific magnitude of accuracy improvement brought by the addition of Sentinel-1 radar data, thereby better highlighting the necessity of multi-source data fusion.
- To improve the readability of the tables, it is recommended to highlight the best-performing model results in bold in both Table 1 and Table 2.
- In Table 2, which presents the ablation study results, adding an extra column or using a clearer notation (e.g., adding "+X.XX%" in parentheses) to show the specific performance improvement relative to the baseline would help to more intuitively reflect the actual contributions of each module.
- In Figure 1, the font size within the legends appears slightly uncoordinated compared to the typography of the rest of the map. It is recommended to appropriately adjust and standardize the font sizes in these legends to further enhance the overall aesthetic appeal of the figure.
- There is a certain degree of content overlap between Figure 3 and Figure 4. It is recommended to appropriately simplify the model visualization of the T2VRCM component in Figure 3 to avoid excessive redundancy with Figure 4.
- The core concept "Time-Series-to-Vision" appears multiple times in the text. However, the capitalization forms (e.g., "Time-series-to-Vision" vs. "Time-Series-to-Vision") are currently used interchangeably and are not fully consistent. It is recommended to conduct a full-text search and unify them into a standard format.
- In Section 3.4, the metrics are denoted as UArice and PArice, while the subsequent Table 2 and Figure 6 use UA and PA. It is recommended to standardize the names of these relevant indicators throughout the manuscript to avoid any ambiguity.
- The capitalization of "KM" and "km" in the spatial distribution maps is inconsistent. It is recommended to unify their representation (preferably using the standard lowercase "km").
- In the in-text labels of Figure 8, the coefficient of determination is currently written as "R2". It is recommended to format this using the standard mathematical superscript as R2 to comply with rigorous academic publishing standards.
Citation: https://doi.org/10.5194/essd-2026-24-CC2
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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.