The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data

<p<Large-scale, high-resolution maps of rapeseed (<i<Brassica napus</i< L.), a major oilseed crop, are critical for predicting annual production and ensuring global energy security, but such maps are still not freely available for many areas. In this study, we developed a new pixel...
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Autor*in:	J. Han [verfasserIn] Z. Zhang [verfasserIn] Y. Luo [verfasserIn] J. Cao [verfasserIn] L. Zhang [verfasserIn] J. Zhang [verfasserIn] Z. Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Übergeordnetes Werk:	In: Earth System Science Data - Copernicus Publications, 2009, 13(2021), Seite 2857-2874
Übergeordnetes Werk:	volume:13 ; year:2021 ; pages:2857-2874

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.5194/essd-13-2857-2021

Katalog-ID:	DOAJ018843255

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spelling	10.5194/essd-13-2857-2021 doi (DE-627)DOAJ018843255 (DE-599)DOAJ8720c39c15a548bca42eaeaa6d75b200 DE-627 ger DE-627 rakwb eng GE1-350 QE1-996.5 J. Han verfasserin aut The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Large-scale, high-resolution maps of rapeseed (<i<Brassica napus</i< L.), a major oilseed crop, are critical for predicting annual production and ensuring global energy security, but such maps are still not freely available for many areas. In this study, we developed a new pixel- and phenology-based algorithm and produced a new data product for rapeseed planting areas (2017–2019) in 33 countries at 10 m spatial resolution based on multiple data. Our product is strongly consistent at the national level with official statistics of the Food and Agricultural Organization of the United Nations. Our rapeseed maps achieved F1 spatial consistency scores of at least 0.81 when compared with the Cropland Data Layer in the United States, the Annual Crop Inventory in Canada, the Crop Map of England, and the Land Cover Map of France. Moreover, F1 scores based on independent validation samples ranged from 0.84 to 0.91, implying a good consistency with ground truth. In almost all countries covered in this study, the rapeseed crop rotation interval was at least 2 years. Our derived maps suggest, with reasonable accuracy, the robustness of the algorithm in identifying rapeseed over large regions with various climates and landscapes. Scientists and local growers can use the freely downloadable derived rapeseed planting areas to help predict rapeseed production and optimize planting structures. The product is publicly available at <a href="https://doi.org/10.17632/ydf3m7pd4j.3"<https://doi.org/10.17632/ydf3m7pd4j.3</a< (Han et al., 2021).</p< Environmental sciences Geology Z. Zhang verfasserin aut Y. Luo verfasserin aut J. Cao verfasserin aut L. Zhang verfasserin aut J. Zhang verfasserin aut Z. Li verfasserin aut In Earth System Science Data Copernicus Publications, 2009 13(2021), Seite 2857-2874 (DE-627)590283227 (DE-600)2475469-9 18663516 nnns volume:13 year:2021 pages:2857-2874 https://doi.org/10.5194/essd-13-2857-2021 kostenfrei https://doaj.org/article/8720c39c15a548bca42eaeaa6d75b200 kostenfrei https://essd.copernicus.org/articles/13/2857/2021/essd-13-2857-2021.pdf kostenfrei https://doaj.org/toc/1866-3508 Journal toc kostenfrei https://doaj.org/toc/1866-3516 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 2857-2874
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allfieldsSound	10.5194/essd-13-2857-2021 doi (DE-627)DOAJ018843255 (DE-599)DOAJ8720c39c15a548bca42eaeaa6d75b200 DE-627 ger DE-627 rakwb eng GE1-350 QE1-996.5 J. Han verfasserin aut The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Large-scale, high-resolution maps of rapeseed (<i<Brassica napus</i< L.), a major oilseed crop, are critical for predicting annual production and ensuring global energy security, but such maps are still not freely available for many areas. In this study, we developed a new pixel- and phenology-based algorithm and produced a new data product for rapeseed planting areas (2017–2019) in 33 countries at 10 m spatial resolution based on multiple data. Our product is strongly consistent at the national level with official statistics of the Food and Agricultural Organization of the United Nations. Our rapeseed maps achieved F1 spatial consistency scores of at least 0.81 when compared with the Cropland Data Layer in the United States, the Annual Crop Inventory in Canada, the Crop Map of England, and the Land Cover Map of France. Moreover, F1 scores based on independent validation samples ranged from 0.84 to 0.91, implying a good consistency with ground truth. In almost all countries covered in this study, the rapeseed crop rotation interval was at least 2 years. Our derived maps suggest, with reasonable accuracy, the robustness of the algorithm in identifying rapeseed over large regions with various climates and landscapes. Scientists and local growers can use the freely downloadable derived rapeseed planting areas to help predict rapeseed production and optimize planting structures. The product is publicly available at <a href="https://doi.org/10.17632/ydf3m7pd4j.3"<https://doi.org/10.17632/ydf3m7pd4j.3</a< (Han et al., 2021).</p< Environmental sciences Geology Z. Zhang verfasserin aut Y. Luo verfasserin aut J. Cao verfasserin aut L. Zhang verfasserin aut J. Zhang verfasserin aut Z. Li verfasserin aut In Earth System Science Data Copernicus Publications, 2009 13(2021), Seite 2857-2874 (DE-627)590283227 (DE-600)2475469-9 18663516 nnns volume:13 year:2021 pages:2857-2874 https://doi.org/10.5194/essd-13-2857-2021 kostenfrei https://doaj.org/article/8720c39c15a548bca42eaeaa6d75b200 kostenfrei https://essd.copernicus.org/articles/13/2857/2021/essd-13-2857-2021.pdf kostenfrei https://doaj.org/toc/1866-3508 Journal toc kostenfrei https://doaj.org/toc/1866-3516 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 2857-2874
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source	In Earth System Science Data 13(2021), Seite 2857-2874 volume:13 year:2021 pages:2857-2874
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topic_title	GE1-350 QE1-996.5 The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data
topic	misc GE1-350 misc QE1-996.5 misc Environmental sciences misc Geology
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topic_browse	misc GE1-350 misc QE1-996.5 misc Environmental sciences misc Geology
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title	The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data
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title_full	The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data
author_sort	J. Han
journal	Earth System Science Data
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author_browse	J. Han Z. Zhang Y. Luo J. Cao L. Zhang J. Zhang Z. Li
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doi_str_mv	10.5194/essd-13-2857-2021
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title_sort	rapeseedmap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data
callnumber	GE1-350
title_auth	The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data
abstract	<p<Large-scale, high-resolution maps of rapeseed (<i<Brassica napus</i< L.), a major oilseed crop, are critical for predicting annual production and ensuring global energy security, but such maps are still not freely available for many areas. In this study, we developed a new pixel- and phenology-based algorithm and produced a new data product for rapeseed planting areas (2017–2019) in 33 countries at 10 m spatial resolution based on multiple data. Our product is strongly consistent at the national level with official statistics of the Food and Agricultural Organization of the United Nations. Our rapeseed maps achieved F1 spatial consistency scores of at least 0.81 when compared with the Cropland Data Layer in the United States, the Annual Crop Inventory in Canada, the Crop Map of England, and the Land Cover Map of France. Moreover, F1 scores based on independent validation samples ranged from 0.84 to 0.91, implying a good consistency with ground truth. In almost all countries covered in this study, the rapeseed crop rotation interval was at least 2 years. Our derived maps suggest, with reasonable accuracy, the robustness of the algorithm in identifying rapeseed over large regions with various climates and landscapes. Scientists and local growers can use the freely downloadable derived rapeseed planting areas to help predict rapeseed production and optimize planting structures. The product is publicly available at <a href="https://doi.org/10.17632/ydf3m7pd4j.3"<https://doi.org/10.17632/ydf3m7pd4j.3</a< (Han et al., 2021).</p<
abstractGer	<p<Large-scale, high-resolution maps of rapeseed (<i<Brassica napus</i< L.), a major oilseed crop, are critical for predicting annual production and ensuring global energy security, but such maps are still not freely available for many areas. In this study, we developed a new pixel- and phenology-based algorithm and produced a new data product for rapeseed planting areas (2017–2019) in 33 countries at 10 m spatial resolution based on multiple data. Our product is strongly consistent at the national level with official statistics of the Food and Agricultural Organization of the United Nations. Our rapeseed maps achieved F1 spatial consistency scores of at least 0.81 when compared with the Cropland Data Layer in the United States, the Annual Crop Inventory in Canada, the Crop Map of England, and the Land Cover Map of France. Moreover, F1 scores based on independent validation samples ranged from 0.84 to 0.91, implying a good consistency with ground truth. In almost all countries covered in this study, the rapeseed crop rotation interval was at least 2 years. Our derived maps suggest, with reasonable accuracy, the robustness of the algorithm in identifying rapeseed over large regions with various climates and landscapes. Scientists and local growers can use the freely downloadable derived rapeseed planting areas to help predict rapeseed production and optimize planting structures. The product is publicly available at <a href="https://doi.org/10.17632/ydf3m7pd4j.3"<https://doi.org/10.17632/ydf3m7pd4j.3</a< (Han et al., 2021).</p<
abstract_unstemmed	<p<Large-scale, high-resolution maps of rapeseed (<i<Brassica napus</i< L.), a major oilseed crop, are critical for predicting annual production and ensuring global energy security, but such maps are still not freely available for many areas. In this study, we developed a new pixel- and phenology-based algorithm and produced a new data product for rapeseed planting areas (2017–2019) in 33 countries at 10 m spatial resolution based on multiple data. Our product is strongly consistent at the national level with official statistics of the Food and Agricultural Organization of the United Nations. Our rapeseed maps achieved F1 spatial consistency scores of at least 0.81 when compared with the Cropland Data Layer in the United States, the Annual Crop Inventory in Canada, the Crop Map of England, and the Land Cover Map of France. Moreover, F1 scores based on independent validation samples ranged from 0.84 to 0.91, implying a good consistency with ground truth. In almost all countries covered in this study, the rapeseed crop rotation interval was at least 2 years. Our derived maps suggest, with reasonable accuracy, the robustness of the algorithm in identifying rapeseed over large regions with various climates and landscapes. Scientists and local growers can use the freely downloadable derived rapeseed planting areas to help predict rapeseed production and optimize planting structures. The product is publicly available at <a href="https://doi.org/10.17632/ydf3m7pd4j.3"<https://doi.org/10.17632/ydf3m7pd4j.3</a< (Han et al., 2021).</p<
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title_short	The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data
url	https://doi.org/10.5194/essd-13-2857-2021 https://doaj.org/article/8720c39c15a548bca42eaeaa6d75b200 https://essd.copernicus.org/articles/13/2857/2021/essd-13-2857-2021.pdf https://doaj.org/toc/1866-3508 https://doaj.org/toc/1866-3516
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author2	Z. Zhang Y. Luo J. Cao L. Zhang J. Zhang Z. Li
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up_date	2024-07-03T20:19:59.283Z
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