ArticleScientific data2025
Mapping the harvest area of a comprehensive set of crop types in China from 1990 to 2020 at a 1-km resolution.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- A high-resolution gridded dataset of biomass resource potentials in China with policy-aware land-use constraints for renewable energy planning.Scientific data · 2026Article
- A 1-km Dataset of Water Consumption and Irrigation for Major Grain Crops in the Yellow River Basin Based on the Crop Water Production Function.Scientific data · 2026Article
- Mapping the harvest area of a comprehensive set of crop types in China from 1990 to 2020 at a 1-km resolution.Scientific data · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Changing crop patterns are primary driver of land use change and can impact global atmospheric cycles. While existing studies have mapped the distribution of several crops in China, harvest area maps for a complete set of crops over the past decades are lacking. This study pioneered the development of a spatiotemporal dataset of harvest area maps for 16 crop types in China at a 1-km resolution from 1990 to 2020 with 5-year intervals. Prefecture-level crop statistics were allocated to grids based on synthetical crop suitability score, which is evaluated by natural and socioeconomic factors. County-level validations demonstrated the built dataset is highly consistent with statistics, especially for primary grains and oilseed. Moreover, crop harvest area at sub-pixel level can better represent gradient changes within urban-rural zones. The built crop maps revealed the harvest zones for maize, rice and soybeans in Northern China have steadily expanded since 1990. This dataset fully supports identification of spatiotemporal changes in China's crop patterns and can serve as critical input for biogeochemical and agricultural models.
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