ArticleScientific data2026
High-resolution distribution map of apple orchards in China based on multisource data.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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5 authors.
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Abstract
China is the world's leading apple producer and vital to global fruit supply and food security. Apple production depends on yield and planting area, both strongly influenced by climate. Regional climate variations cause differing cultivation suitability, exacerbating production disparities. Although climate models can project impacts on apples, the lack of high-resolution distribution data limits reliable large-scale assessment. Mapping apple orchards is more challenging than field crops due to complex growing conditions. To tackle this challenge, we developed the first 30-m national-scale apple orchard distribution map of China (AOMC) for 2019-2021, covering 96% of China's total apple orchard area. This dataset is generated based on an apple-phenology-driven algorithm framework, which integrates apple phenology and multi-source geographical information with the time-weighted dynamic time warping algorithm. Validations revealed that the dataset accurately represented orchard locations (Overall accuracy: 87%) and correlated strongly with municipal-level statistics (R²: 0.892-0.925). The AOMC supports the monitoring of spatiotemporal changes in China's apple production patterns, serves as critical input for crop models, and advances the sustainable development and climate resilience of apple industry.
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