ArticleScientific data2024
Annual winter wheat mapping dataset in China from 2001 to 2020.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Anthropogenic climate change accelerates the onset of global flood timing.Nature communications · 2026Article
- A hierarchical framework for cropland and crop classification in the Qingtongxia irrigation district using support vector machine and random forest models.Scientific reports · 2026Article
- A 30-m annual distribution dataset of major crops in China from 2001-2024.Scientific data · 2026Article
- A 30 m winter wheat distribution dataset for the North China Plain from 2000 to 2024.Scientific data · 2026Article
- A 30 m Multi-Year Dataset of Major Crop Distributions in Xinjiang, China (2013-2024) Based on Harmonized Landsat-Sentinel-2 Data.Scientific data · 2026Article
- Dynamic gating-enhanced deep learning model with multi-source remote sensing synergy for optimizing wheat yield estimation.Frontiers in plant science · 2025Article
- Annual winter wheat mapping dataset in China from 2001 to 2020.Scientific data · 2024Article
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6 authors.
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Abstract
Winter wheat is a major staple food grown worldwide. China contributes about 19% of the winter wheat production in the world. Therefore, quickly and accurately acquiring winter wheat distribution in China over long time periods is critical to achieve grain safety and understand the spatiotemporal patterns of winter wheat. However, there is a lack of widely available, high spatial resolution, long-term, and large-scale remotely sensed maps on winter wheat cultivation in China. In this study, we utilized a phenology-based algorithm to identify winter wheat location. The algorithm integrated three key growing features of winter wheat into one variable to calculate the planting probability of winter wheat. Here, we produced a long-term winter wheat map dataset at 30-m resolution for China from 2001 to 2020 using the fusion dataset. The resultant maps achieved high accuracy based on field survey samples, with user's, producer's, and overall accuracies of 91.17%, 90.92%, and 91.6%, respectively. Furthermore, the mapped winter wheat area aggregated to municipal- and county-levels exhibited good correlations with agricultural statistical data.
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