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A twenty-year dataset of high-resolution maize distribution in China
China is the world’s second-largest maize producer, contributing 23% to global production and playing a crucial role in stabilizing the global maize supply. Therefore, accurately mapping the maize distribution in China is of great significance for regional and global food security and international...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10522722/ https://www.ncbi.nlm.nih.gov/pubmed/37752131 http://dx.doi.org/10.1038/s41597-023-02573-6 |
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author | Peng, Qiongyan Shen, Ruoque Li, Xiangqian Ye, Tao Dong, Jie Fu, Yangyang Yuan, Wenping |
author_facet | Peng, Qiongyan Shen, Ruoque Li, Xiangqian Ye, Tao Dong, Jie Fu, Yangyang Yuan, Wenping |
author_sort | Peng, Qiongyan |
collection | PubMed |
description | China is the world’s second-largest maize producer, contributing 23% to global production and playing a crucial role in stabilizing the global maize supply. Therefore, accurately mapping the maize distribution in China is of great significance for regional and global food security and international cereals trade. However, it still lacks a long-term maize distribution dataset with fine spatial resolution, because the existing high spatial resolution satellite datasets suffer from data gaps caused by cloud cover, especially in humid and cloudy regions. This study aimed to produce a long-term, high-resolution maize distribution map for China (China Crop Dataset–Maize, CCD-Maize) identifying maize in 22 provinces and municipalities from 2001 to 2020. The map was produced using a high spatiotemporal resolution fused dataset and a phenology-based method called Time-Weighted Dynamic Time Warping. A validation based on 54,281 field survey samples with a 30-m resolution showed that the average user’s accuracy and producer’s accuracy of CCD-Maize were 77.32% and 80.98%, respectively, and the overall accuracy was 80.06% over all 22 provinces. |
format | Online Article Text |
id | pubmed-10522722 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105227222023-09-28 A twenty-year dataset of high-resolution maize distribution in China Peng, Qiongyan Shen, Ruoque Li, Xiangqian Ye, Tao Dong, Jie Fu, Yangyang Yuan, Wenping Sci Data Data Descriptor China is the world’s second-largest maize producer, contributing 23% to global production and playing a crucial role in stabilizing the global maize supply. Therefore, accurately mapping the maize distribution in China is of great significance for regional and global food security and international cereals trade. However, it still lacks a long-term maize distribution dataset with fine spatial resolution, because the existing high spatial resolution satellite datasets suffer from data gaps caused by cloud cover, especially in humid and cloudy regions. This study aimed to produce a long-term, high-resolution maize distribution map for China (China Crop Dataset–Maize, CCD-Maize) identifying maize in 22 provinces and municipalities from 2001 to 2020. The map was produced using a high spatiotemporal resolution fused dataset and a phenology-based method called Time-Weighted Dynamic Time Warping. A validation based on 54,281 field survey samples with a 30-m resolution showed that the average user’s accuracy and producer’s accuracy of CCD-Maize were 77.32% and 80.98%, respectively, and the overall accuracy was 80.06% over all 22 provinces. Nature Publishing Group UK 2023-09-26 /pmc/articles/PMC10522722/ /pubmed/37752131 http://dx.doi.org/10.1038/s41597-023-02573-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Peng, Qiongyan Shen, Ruoque Li, Xiangqian Ye, Tao Dong, Jie Fu, Yangyang Yuan, Wenping A twenty-year dataset of high-resolution maize distribution in China |
title | A twenty-year dataset of high-resolution maize distribution in China |
title_full | A twenty-year dataset of high-resolution maize distribution in China |
title_fullStr | A twenty-year dataset of high-resolution maize distribution in China |
title_full_unstemmed | A twenty-year dataset of high-resolution maize distribution in China |
title_short | A twenty-year dataset of high-resolution maize distribution in China |
title_sort | twenty-year dataset of high-resolution maize distribution in china |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10522722/ https://www.ncbi.nlm.nih.gov/pubmed/37752131 http://dx.doi.org/10.1038/s41597-023-02573-6 |
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