ArticleScientific data2024
National-scale 10-m maps of cropland use intensity in China during 2018-2023.
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 5 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
5 citing papers in PubMed.
- Optimized Land-Water Management Expands Afforestation Potential Under Land and Human Water-Use Constraints.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
- Dynamic maps of plastic-mulched farmlands in Northeast China from 1985 to 2025.Scientific data · 2026Article
- A 10-meter resolution dataset of abandoned and reclaimed cropland from 2016 to 2023 in Inner Mongolia, China.Scientific data · 2025Article
- National-scale 10-m maps of cropland use intensity in China during 2018-2023.Scientific data · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
The amount of actively cultivated land in China is increasingly threatened by rapid urbanization and rural population aging. Quantifying the extent and changes of active cropland and cropping intensity is crucial to global food security. However, national-scale datasets for smallholder agriculture are limited in spatiotemporal continuity, resolution, and precision. In this paper, we present updated annual Cropland Use Intensity maps in China (China-CUI10m) with descriptions of the extent of fallow/abandoned, actively cropped fields and cropping intensity at a 10-m resolution in recent six years (2018-2023). The dataset is produced by robust algorithms with no requirements for regional adjustments or intensive training samples, which take full advantage of the Sentinel-1 (S1) SAR and Sentinel-2 (S2) MSI time series. The China-CUI10m maps have achieved high accuracy when compared to ground truth data (Overall accuracy = 90.88%) and statistical data (R
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Registered trials
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