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- Published: 05 January 2026
Scientific Data , Article number: (2026) Cite this article
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
Abstract
Northeast China, a crucial agricultural region contributing one-third of China’s commodity grain production, lacks detailed, high-resolution crop maps pre-2013 due to limited satellite observations. To bridge this gap, this study developed annua…
- Data Descriptor
- Open access
- Published: 05 January 2026
Scientific Data , Article number: (2026) Cite this article
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
Abstract
Northeast China, a crucial agricultural region contributing one-third of China’s commodity grain production, lacks detailed, high-resolution crop maps pre-2013 due to limited satellite observations. To bridge this gap, this study developed annual 30 m crop type maps for Northeast China (2001–2022) using all available Landsat and MODIS imagery and the Highly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm. The accuracy of crop-type maps was assessed using three complementary approaches. First, validation against ground-truth data (2017–2022) yielded overall accuracies of 80.7%–91%. Second, validation using ‘trusted pixels’ from existing crop-mapping products (2001–2020) produced overall accuracies of 85%–95%. Third, comparison with government statistics (2001–2022) showed average R2 of 0.98 (paddy rice), 0.83 (maize), and 0.90 (soybean). The generated maps were found to be the most consistent with government statistics compared to pre-existing crop maps, while providing comprehensive spatial and temporal details. This dataset is an important contribution to long-term fine-resolution crop mapping at the regional scale in China, which provide valuable guidance for sustainable agriculture practices in China’s primary grain-producing region.
Code availability
The JavaScript code used to generate the long-term crop type maps is available for download on GitHub (https://github.com/terra1997/30-m-resolution-annual-crop-type-maps-in-Northeast-China-from-2001-to-2022).
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Acknowledgements
This study is supported by the National Key Research and Development Program of China (Grant No.2023YFD1500200), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA28060100), the National Key Research and Development Program of China (Grant No. 2023YFD1500200), the National Natural Science Foundation of China (Grant No. 42271276), and the Ramón y Cajal Research Fellowship (Grant No. RYC2023-044930-I), funded by the Spanish Ministry of Science, Innovation, and Universities.
Author information
Authors and Affiliations
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Yuanyuan Di, Jinwei Dong, Nanshan You & Zhichao Li 1.
Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, 315100, China
Yuanyuan Di & Ping Fu 1.
Image Processing Laboratory, Universitat de València, 46980, València, Spain
Álvaro Moreno-Martínez 1.
Institute of Geomatics, BOKU University, Vienna, Austria
Emma Izquierdo-Verdiguier 1.
State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
Jing Sun
Authors
- Yuanyuan Di
- Jinwei Dong
- Nanshan You
- Zhichao Li
- Álvaro Moreno-Martínez
- Emma Izquierdo-Verdiguier
- Jing Sun
- Ping Fu
Contributions
Yuanyuan Di: Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Visualization. Jinwei Dong: Conceptualization, Investigation, Resources, Writing – review & editing, Funding acquisition. Nanshan You: Methodology, Investigation, Writing - review & editing. Zhichao Li: Writing - review & editing. Álvaro Moreno-Martínez: Data Curation, Writing - review & editing. Emma Izquierdo-Verdiguier: Data Curation, Writing - review & editing. Jing Sun: Validation, Writing - review & editing. Ping Fu: Writing - review & editing.
Corresponding author
Correspondence to Jinwei Dong.
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Di, Y., Dong, J., You, N. et al. 30 m-resolution annual crop type maps in Northeast China from 2001 to 2022. Sci Data (2026). https://doi.org/10.1038/s41597-025-06516-1
Received: 23 May 2025
Accepted: 19 December 2025
Published: 05 January 2026
DOI: https://doi.org/10.1038/s41597-025-06516-1