Articles | Volume 12, issue 3
https://doi.org/10.5194/essd-12-1913-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-1913-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
28 Aug 2020
Data description paper |
| 28 Aug 2020
| 28 Aug 2020
A cultivated planet in 2010 – Part 1: The global synergy cropland map
Miao Lu
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Wenbin Wu
CORRESPONDING AUTHOR
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Liangzhi You
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
International Food Policy Research Institute (IFPRI), Washington, DC,
20005-3915, USA
Linda See
International Institute for Applied Systems Analysis, ESM, Laxenburg,
2361, Austria
Steffen Fritz
International Institute for Applied Systems Analysis, ESM, Laxenburg,
2361, Austria
Qiangyi Yu
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Yanbing Wei
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Di Chen
Institute of Environment and Sustainable Development in Agriculture,
Chinese Academy of Agricultural Sciences, Beijing, 100081, China
Peng Yang
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing,
100081, China
Bing Xue
School of Engineering and Computer Science, Victoria University of
Wellington, Wellington, 6140, New Zealand
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Cited
26 citations as recorded by crossref.
- Global Food Security Assessment during 1961–2019 J. Guo et al. 10.3390/su132414005
- Biodiversity responses to agricultural practices in cropland and natural habitats J. Zhao et al. 10.1016/j.scitotenv.2024.171296
- Soil properties resulting in superior maize yields upon climate warming P. Feng et al. 10.1007/s13593-022-00818-z
- FROM-GLC Plus: toward near real-time and multi-resolution land cover mapping L. Yu et al. 10.1080/15481603.2022.2096184
- MAPS: A new model using data fusion to enhance the accuracy of high-resolution mapping for livestock production systems M. Cheng et al. 10.1016/j.oneear.2023.08.012
- China’s Land Cover Fraction Change during 2001–2015 Based on Remote Sensed Data Fusion between MCD12 and CCI-LC H. Wang et al. 10.3390/rs13030341
- A Reconstruction of Irrigated Cropland Extent in China from 2000 to 2019 Using the Synergy of Statistics and Satellite-Based Datasets M. Bai et al. 10.3390/land11101686
- An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multisource product-fusion approach B. Li et al. 10.5194/essd-15-2347-2023
- Assessment of continuity and efficiency of complemented cropland use in China for the past 20 years: A perspective of cropland abandonment H. Chen et al. 10.1016/j.jclepro.2023.135987
- Tracking spatiotemporal dynamics of irrigated croplands in China from 2000 to 2019 through the synergy of remote sensing, statistics, and historical irrigation datasets C. Zhang et al. 10.1016/j.agwat.2022.107458
- A review of regional and Global scale Land Use/Land Cover (LULC) mapping products generated from satellite remote sensing Y. Wang et al. 10.1016/j.isprsjprs.2023.11.014
- Ammonium-derived nitrous oxide is a global source in streams S. Wang et al. 10.1038/s41467-024-48343-9
- Estimating the global number and distribution of maize and wheat farms O. Erenstein et al. 10.1016/j.gfs.2021.100558
- Spatial transformation of changes in global cultivated land H. Li & W. Song 10.1016/j.scitotenv.2022.160194
- Identifying and quantifying local uncertainty and discrepancy in the comparison of global cropland extent through a synergistic approach X. Liu et al. 10.1016/j.apgeog.2023.103164
- Farms worldwide: 2020 and 2030 outlook O. Erenstein et al. 10.1177/00307270211025539
- Quantifying aboveground biomass, soil organic carbon and erosion with a detailed crop map and PESERA model in the Yangtze River Basin J. Zhou et al. 10.1111/ejss.13503
- A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps Q. Yu et al. 10.5194/essd-12-3545-2020
- Soil buffering capacity enhances maize yield resilience amidst climate perturbations F. Chen et al. 10.1016/j.agsy.2024.103870
- Mapping Irrigated Areas in China Using a Synergy Approach M. van Dijk & S. Geurtsen 10.3390/w15091666
- CCropLand30: High-resolution hybrid cropland maps of China created through the synergy of state-of-the-art remote sensing products and the latest national land survey L. Zhang et al. 10.1016/j.compag.2024.108672
- A new cropland area database by country circa 2020 F. Tubiello et al. 10.5194/essd-15-4997-2023
- A 1 km global cropland dataset from 10 000 BCE to 2100 CE B. Cao et al. 10.5194/essd-13-5403-2021
- Constructing a 30m African Cropland Layer for 2016 by Integrating Multiple Remote sensing, crowdsourced, and Auxiliary Datasets M. Nabil et al. 10.1080/20964471.2021.1914400
- Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries H. Su et al. 10.5194/essd-14-4397-2022
- A review of global gridded cropping system data products K. Kim et al. 10.1088/1748-9326/ac20f4
26 citations as recorded by crossref.
- Global Food Security Assessment during 1961–2019 J. Guo et al. 10.3390/su132414005
- Biodiversity responses to agricultural practices in cropland and natural habitats J. Zhao et al. 10.1016/j.scitotenv.2024.171296
- Soil properties resulting in superior maize yields upon climate warming P. Feng et al. 10.1007/s13593-022-00818-z
- FROM-GLC Plus: toward near real-time and multi-resolution land cover mapping L. Yu et al. 10.1080/15481603.2022.2096184
- MAPS: A new model using data fusion to enhance the accuracy of high-resolution mapping for livestock production systems M. Cheng et al. 10.1016/j.oneear.2023.08.012
- China’s Land Cover Fraction Change during 2001–2015 Based on Remote Sensed Data Fusion between MCD12 and CCI-LC H. Wang et al. 10.3390/rs13030341
- A Reconstruction of Irrigated Cropland Extent in China from 2000 to 2019 Using the Synergy of Statistics and Satellite-Based Datasets M. Bai et al. 10.3390/land11101686
- An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multisource product-fusion approach B. Li et al. 10.5194/essd-15-2347-2023
- Assessment of continuity and efficiency of complemented cropland use in China for the past 20 years: A perspective of cropland abandonment H. Chen et al. 10.1016/j.jclepro.2023.135987
- Tracking spatiotemporal dynamics of irrigated croplands in China from 2000 to 2019 through the synergy of remote sensing, statistics, and historical irrigation datasets C. Zhang et al. 10.1016/j.agwat.2022.107458
- A review of regional and Global scale Land Use/Land Cover (LULC) mapping products generated from satellite remote sensing Y. Wang et al. 10.1016/j.isprsjprs.2023.11.014
- Ammonium-derived nitrous oxide is a global source in streams S. Wang et al. 10.1038/s41467-024-48343-9
- Estimating the global number and distribution of maize and wheat farms O. Erenstein et al. 10.1016/j.gfs.2021.100558
- Spatial transformation of changes in global cultivated land H. Li & W. Song 10.1016/j.scitotenv.2022.160194
- Identifying and quantifying local uncertainty and discrepancy in the comparison of global cropland extent through a synergistic approach X. Liu et al. 10.1016/j.apgeog.2023.103164
- Farms worldwide: 2020 and 2030 outlook O. Erenstein et al. 10.1177/00307270211025539
- Quantifying aboveground biomass, soil organic carbon and erosion with a detailed crop map and PESERA model in the Yangtze River Basin J. Zhou et al. 10.1111/ejss.13503
- A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps Q. Yu et al. 10.5194/essd-12-3545-2020
- Soil buffering capacity enhances maize yield resilience amidst climate perturbations F. Chen et al. 10.1016/j.agsy.2024.103870
- Mapping Irrigated Areas in China Using a Synergy Approach M. van Dijk & S. Geurtsen 10.3390/w15091666
- CCropLand30: High-resolution hybrid cropland maps of China created through the synergy of state-of-the-art remote sensing products and the latest national land survey L. Zhang et al. 10.1016/j.compag.2024.108672
- A new cropland area database by country circa 2020 F. Tubiello et al. 10.5194/essd-15-4997-2023
- A 1 km global cropland dataset from 10 000 BCE to 2100 CE B. Cao et al. 10.5194/essd-13-5403-2021
- Constructing a 30m African Cropland Layer for 2016 by Integrating Multiple Remote sensing, crowdsourced, and Auxiliary Datasets M. Nabil et al. 10.1080/20964471.2021.1914400
- Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries H. Su et al. 10.5194/essd-14-4397-2022
- A review of global gridded cropping system data products K. Kim et al. 10.1088/1748-9326/ac20f4
Latest update: 29 Jun 2024
Short summary
Global cropland distribution is critical for agricultural monitoring and food security. We propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of cropland area, which is independent of training samples. The synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics.
Global cropland distribution is critical for agricultural monitoring and food security. We...
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