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Annual dynamic dataset of global cropping intensity from 2001 to 2019

The cropping intensity has received growing concern in the agriculture field in applications such as harvest area research. Notwithstanding the significant amount of existing literature on local cropping intensities, research considering global datasets appears to be limited in spatial resolution an...

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Detalles Bibliográficos
Autores principales: Liu, Xiaoxuan, Zheng, Juepeng, Yu, Le, Hao, Pengyu, Chen, Bin, Xin, Qinchuan, Fu, Haohuan, Gong, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8553865/
https://www.ncbi.nlm.nih.gov/pubmed/34711845
http://dx.doi.org/10.1038/s41597-021-01065-9
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author Liu, Xiaoxuan
Zheng, Juepeng
Yu, Le
Hao, Pengyu
Chen, Bin
Xin, Qinchuan
Fu, Haohuan
Gong, Peng
author_facet Liu, Xiaoxuan
Zheng, Juepeng
Yu, Le
Hao, Pengyu
Chen, Bin
Xin, Qinchuan
Fu, Haohuan
Gong, Peng
author_sort Liu, Xiaoxuan
collection PubMed
description The cropping intensity has received growing concern in the agriculture field in applications such as harvest area research. Notwithstanding the significant amount of existing literature on local cropping intensities, research considering global datasets appears to be limited in spatial resolution and precision. In this paper, we present an annual dynamic global cropping intensity dataset covering the period from 2001 to 2019 at a 250-m resolution with an average overall accuracy of 89%, exceeding the accuracy of the current annual dynamic global cropping intensity data at a 500-m resolution. We used the enhanced vegetation index (EVI) of MOD13Q1 as the database via a sixth-order polynomial function to calculate the cropping intensity. The global cropping intensity dataset was packaged in the GeoTIFF file type, with the quality control band in the same format. The dataset fills the vacancy of medium-resolution, global-scale annual cropping intensity data and provides an improved map for further global yield estimations and food security analyses.
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spelling pubmed-85538652021-10-29 Annual dynamic dataset of global cropping intensity from 2001 to 2019 Liu, Xiaoxuan Zheng, Juepeng Yu, Le Hao, Pengyu Chen, Bin Xin, Qinchuan Fu, Haohuan Gong, Peng Sci Data Data Descriptor The cropping intensity has received growing concern in the agriculture field in applications such as harvest area research. Notwithstanding the significant amount of existing literature on local cropping intensities, research considering global datasets appears to be limited in spatial resolution and precision. In this paper, we present an annual dynamic global cropping intensity dataset covering the period from 2001 to 2019 at a 250-m resolution with an average overall accuracy of 89%, exceeding the accuracy of the current annual dynamic global cropping intensity data at a 500-m resolution. We used the enhanced vegetation index (EVI) of MOD13Q1 as the database via a sixth-order polynomial function to calculate the cropping intensity. The global cropping intensity dataset was packaged in the GeoTIFF file type, with the quality control band in the same format. The dataset fills the vacancy of medium-resolution, global-scale annual cropping intensity data and provides an improved map for further global yield estimations and food security analyses. Nature Publishing Group UK 2021-10-28 /pmc/articles/PMC8553865/ /pubmed/34711845 http://dx.doi.org/10.1038/s41597-021-01065-9 Text en © The Author(s) 2021 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Liu, Xiaoxuan
Zheng, Juepeng
Yu, Le
Hao, Pengyu
Chen, Bin
Xin, Qinchuan
Fu, Haohuan
Gong, Peng
Annual dynamic dataset of global cropping intensity from 2001 to 2019
title Annual dynamic dataset of global cropping intensity from 2001 to 2019
title_full Annual dynamic dataset of global cropping intensity from 2001 to 2019
title_fullStr Annual dynamic dataset of global cropping intensity from 2001 to 2019
title_full_unstemmed Annual dynamic dataset of global cropping intensity from 2001 to 2019
title_short Annual dynamic dataset of global cropping intensity from 2001 to 2019
title_sort annual dynamic dataset of global cropping intensity from 2001 to 2019
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8553865/
https://www.ncbi.nlm.nih.gov/pubmed/34711845
http://dx.doi.org/10.1038/s41597-021-01065-9
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