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High-resolution crop yield and water productivity dataset generated using random forest and remote sensing

Accurate and high-resolution crop yield and crop water productivity (CWP) datasets are required to understand and predict spatiotemporal variation in agricultural production capacity; however, datasets for maize and wheat, two key staple dryland crops in China, are currently lacking. In this study,...

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Autores principales: Cheng, Minghan, Jiao, Xiyun, Shi, Lei, Penuelas, Josep, Kumar, Lalit, Nie, Chenwei, Wu, Tianao, Liu, Kaihua, Wu, Wenbin, Jin, Xiuliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586934/
https://www.ncbi.nlm.nih.gov/pubmed/36271097
http://dx.doi.org/10.1038/s41597-022-01761-0
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author Cheng, Minghan
Jiao, Xiyun
Shi, Lei
Penuelas, Josep
Kumar, Lalit
Nie, Chenwei
Wu, Tianao
Liu, Kaihua
Wu, Wenbin
Jin, Xiuliang
author_facet Cheng, Minghan
Jiao, Xiyun
Shi, Lei
Penuelas, Josep
Kumar, Lalit
Nie, Chenwei
Wu, Tianao
Liu, Kaihua
Wu, Wenbin
Jin, Xiuliang
author_sort Cheng, Minghan
collection PubMed
description Accurate and high-resolution crop yield and crop water productivity (CWP) datasets are required to understand and predict spatiotemporal variation in agricultural production capacity; however, datasets for maize and wheat, two key staple dryland crops in China, are currently lacking. In this study, we generated and evaluated a long-term data series, at 1-km resolution of crop yield and CWP for maize and wheat across China, based on the multiple remotely sensed indicators and random forest algorithm. Results showed that MOD16 products are an accurate alternative to eddy covariance flux tower data to describe crop evapotranspiration (maize and wheat RMSE: 4.42 and 3.81 mm/8d, respectively) and the proposed yield estimation model showed accuracy at local (maize and wheat rRMSE: 26.81 and 21.80%, respectively) and regional (maize and wheat rRMSE: 15.36 and 17.17%, respectively) scales. Our analyses, which showed spatiotemporal patterns of maize and wheat yields and CWP across China, can be used to optimize agricultural production strategies in the context of maintaining food security.
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spelling pubmed-95869342022-10-23 High-resolution crop yield and water productivity dataset generated using random forest and remote sensing Cheng, Minghan Jiao, Xiyun Shi, Lei Penuelas, Josep Kumar, Lalit Nie, Chenwei Wu, Tianao Liu, Kaihua Wu, Wenbin Jin, Xiuliang Sci Data Data Descriptor Accurate and high-resolution crop yield and crop water productivity (CWP) datasets are required to understand and predict spatiotemporal variation in agricultural production capacity; however, datasets for maize and wheat, two key staple dryland crops in China, are currently lacking. In this study, we generated and evaluated a long-term data series, at 1-km resolution of crop yield and CWP for maize and wheat across China, based on the multiple remotely sensed indicators and random forest algorithm. Results showed that MOD16 products are an accurate alternative to eddy covariance flux tower data to describe crop evapotranspiration (maize and wheat RMSE: 4.42 and 3.81 mm/8d, respectively) and the proposed yield estimation model showed accuracy at local (maize and wheat rRMSE: 26.81 and 21.80%, respectively) and regional (maize and wheat rRMSE: 15.36 and 17.17%, respectively) scales. Our analyses, which showed spatiotemporal patterns of maize and wheat yields and CWP across China, can be used to optimize agricultural production strategies in the context of maintaining food security. Nature Publishing Group UK 2022-10-21 /pmc/articles/PMC9586934/ /pubmed/36271097 http://dx.doi.org/10.1038/s41597-022-01761-0 Text en © The Author(s) 2022 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/) .
spellingShingle Data Descriptor
Cheng, Minghan
Jiao, Xiyun
Shi, Lei
Penuelas, Josep
Kumar, Lalit
Nie, Chenwei
Wu, Tianao
Liu, Kaihua
Wu, Wenbin
Jin, Xiuliang
High-resolution crop yield and water productivity dataset generated using random forest and remote sensing
title High-resolution crop yield and water productivity dataset generated using random forest and remote sensing
title_full High-resolution crop yield and water productivity dataset generated using random forest and remote sensing
title_fullStr High-resolution crop yield and water productivity dataset generated using random forest and remote sensing
title_full_unstemmed High-resolution crop yield and water productivity dataset generated using random forest and remote sensing
title_short High-resolution crop yield and water productivity dataset generated using random forest and remote sensing
title_sort high-resolution crop yield and water productivity dataset generated using random forest and remote sensing
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586934/
https://www.ncbi.nlm.nih.gov/pubmed/36271097
http://dx.doi.org/10.1038/s41597-022-01761-0
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