ArticleScientific data2023
Mapping annual 10-m maize cropland changes in China during 2017-2021.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A century-scale dataset of crop yield and agronomic trait evolution under field management in China.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
- 30 m-resolution annual crop type maps in Northeast China from 2001 to 2022.Scientific data · 2026Article
- Mapping 10-m harvested area in the major winter wheat-producing regions of China from 2018 to 2022.Scientific data · 2024Article
- Mapping annual 10-m soybean cropland with spatiotemporal sample migration.Scientific data · 2024Article
- Mapping annual 10-m maize cropland changes in China during 2017-2021.Scientific data · 2023Article
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Authors and funding
7 authors.
Funding
Abstract
China contributed nearly one-fifth of the world maize production over the past few years. Mapping the distributions of maize cropland in China is crucial to ensure global food security. Nonetheless, 10 m maize cropland maps in China are still unavailable, restricting the promotion of sustainable agriculture. In this paper, we collect numerous samples to produce annual 10-m maize cropland maps in China from 2017 to 2021 with a machine learning based classification framework. To overcome the temporal variations of plants, the proposed framework takes Sentinel-2 sequence images as input and utilizes deep neural networks and random forest as classifiers to map maize in a zone-specific way. The generated maps have an overall accuracy (OA) spanning from 0.87 to 0.95 and the maize-cultivated areas estimated by the maps are highly consistent with the records in statistical yearbooks (R
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Registered trials
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