Articles | Volume 13, issue 6
https://doi.org/10.5194/essd-13-2857-2021
© Author(s) 2021. 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-13-2857-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
16 Jun 2021
Data description paper |
| 16 Jun 2021
| 16 Jun 2021
The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data
Jichong Han
State Key Laboratory of Earth Surface Processes and Resource Ecology/MoE Key Laboratory of Environmental Change and Natural Hazards, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
Zhao Zhang
CORRESPONDING AUTHOR
State Key Laboratory of Earth Surface Processes and Resource Ecology/MoE Key Laboratory of Environmental Change and Natural Hazards, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
Yuchuan Luo
State Key Laboratory of Earth Surface Processes and Resource Ecology/MoE Key Laboratory of Environmental Change and Natural Hazards, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
Juan Cao
State Key Laboratory of Earth Surface Processes and Resource Ecology/MoE Key Laboratory of Environmental Change and Natural Hazards, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
Liangliang Zhang
State Key Laboratory of Earth Surface Processes and Resource Ecology/MoE Key Laboratory of Environmental Change and Natural Hazards, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
Jing Zhang
State Key Laboratory of Earth Surface Processes and Resource Ecology/MoE Key Laboratory of Environmental Change and Natural Hazards, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
Ziyue Li
State Key Laboratory of Earth Surface Processes and Resource Ecology/MoE Key Laboratory of Environmental Change and Natural Hazards, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
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Cited
32 citations as recorded by crossref.
- Mapping annual 10-m soybean cropland with spatiotemporal sample migration H. Zhang et al. 10.1038/s41597-024-03273-5
- Seamless and automated rapeseed mapping for large cloudy regions using time-series optical satellite imagery H. Zhang et al. 10.1016/j.isprsjprs.2021.12.001
- The Role of Sequential Cropping and Biogasdoneright™ in Enhancing the Sustainability of Agricultural Systems in Europe F. Magnolo et al. 10.3390/agronomy11112102
- Machine Learning-Based Classification of Asbestos-Containing Roofs Using Airborne RGB and Thermal Imagery G. Kaplan et al. 10.3390/su15076067
- A Statistical Analysis Model of Big Data for Precise Poverty Alleviation Based on Multisource Data Fusion T. Liang et al. 10.1155/2022/5298988
- Mapping annual 10 m rapeseed extent using multisource data in the Yangtze River Economic Belt of China (2017–2021) on Google Earth Engine W. Liu & H. Zhang 10.1016/j.jag.2023.103198
- Automated in-season mapping of winter wheat in China with training data generation and model transfer G. Yang et al. 10.1016/j.isprsjprs.2023.07.004
- Mapping Crop Rotation by Using Deeply Synergistic Optical and SAR Time Series Y. Liu et al. 10.3390/rs13204160
- ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021 Q. Mei et al. 10.5194/essd-16-3213-2024
- Artificial Intelligence Algorithms for Rapeseed Fields Mapping Using Sentinel-1 Time Series: Temporal Transfer Scenario and Ground Sampling Constraints S. Maleki et al. 10.1109/JSTARS.2023.3316304
- CARM30: China annual rapeseed maps at 30 m spatial resolution from 2000 to 2022 using multi-source data W. Liu et al. 10.1038/s41597-024-03188-1
- Feature-based algorithm for large-scale rice phenology detection based on satellite images X. Zhao et al. 10.1016/j.agrformet.2022.109283
- Mapping winter rapeseed in South China using Sentinel-2 data based on a novel separability index J. TAO et al. 10.1016/j.jia.2022.10.008
- Accurate mapping of rapeseed fields in the initial flowering stage using Sentinel-2 satellite images and convolutional neural networks Y. Sun et al. 10.1016/j.ecolind.2024.112027
- Improving rapeseed carbon footprint evaluation via the integration of remote sensing technology into an LCA approach X. Yang et al. 10.1016/j.scitotenv.2024.174262
- Detection and Mapping of Cover Crops Using Sentinel-1 SAR Remote Sensing Data S. Najem et al. 10.1109/JSTARS.2023.3337989
- Mapping global water-surface photovoltaics with satellite images Z. Xia et al. 10.1016/j.rser.2023.113760
- WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping K. Van Tricht et al. 10.5194/essd-15-5491-2023
- Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation H. Li et al. 10.1016/j.rse.2023.113623
- A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery H. Tian et al. 10.3390/rs14051113
- Mapping rapeseed in China during 2017-2021 using Sentinel data: an automated approach integrating rule-based sample generation and a one-class classifier (RSG-OC) Y. Zang et al. 10.1080/15481603.2022.2163576
- CROPGRIDS: a global geo-referenced dataset of 173 crops F. Tang et al. 10.1038/s41597-024-03247-7
- Mapping rapeseed planting areas using an automatic phenology- and pixel-based algorithm (APPA) in Google Earth Engine J. Han et al. 10.1016/j.cj.2022.04.013
- NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019 J. Han et al. 10.5194/essd-13-5969-2021
- Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data Y. Luo et al. 10.3390/rs14081809
- STSNet: A cross-spatial resolution multi-modal remote sensing deep fusion network for high resolution land-cover segmentation B. Yu et al. 10.1016/j.inffus.2024.102689
- Phenology-Based Unsupervised Rapeseed Mapping Using Multitemporal Data S. Zhang et al. 10.1109/JSTARS.2022.3217665
- Annual paddy rice planting area and cropping intensity datasets and their dynamics in the Asian monsoon region from 2000 to 2020 J. Han et al. 10.1016/j.agsy.2022.103437
- Within-Season Crop Identification by the Fusion of Spectral Time-Series Data and Historical Crop Planting Data Q. Wang et al. 10.3390/rs15205043
- Planting area and production decreased for winter-triticeae crops but increased for rapeseed in Ukraine with climatic impacts dominating J. Han et al. 10.1016/j.geosus.2024.08.006
- Advancements in high-resolution land surface satellite products: A comprehensive review of inversion algorithms, products and challenges S. Liang et al. 10.1016/j.srs.2024.100152
- The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data J. Han et al. 10.5194/essd-13-2857-2021
31 citations as recorded by crossref.
- Mapping annual 10-m soybean cropland with spatiotemporal sample migration H. Zhang et al. 10.1038/s41597-024-03273-5
- Seamless and automated rapeseed mapping for large cloudy regions using time-series optical satellite imagery H. Zhang et al. 10.1016/j.isprsjprs.2021.12.001
- The Role of Sequential Cropping and Biogasdoneright™ in Enhancing the Sustainability of Agricultural Systems in Europe F. Magnolo et al. 10.3390/agronomy11112102
- Machine Learning-Based Classification of Asbestos-Containing Roofs Using Airborne RGB and Thermal Imagery G. Kaplan et al. 10.3390/su15076067
- A Statistical Analysis Model of Big Data for Precise Poverty Alleviation Based on Multisource Data Fusion T. Liang et al. 10.1155/2022/5298988
- Mapping annual 10 m rapeseed extent using multisource data in the Yangtze River Economic Belt of China (2017–2021) on Google Earth Engine W. Liu & H. Zhang 10.1016/j.jag.2023.103198
- Automated in-season mapping of winter wheat in China with training data generation and model transfer G. Yang et al. 10.1016/j.isprsjprs.2023.07.004
- Mapping Crop Rotation by Using Deeply Synergistic Optical and SAR Time Series Y. Liu et al. 10.3390/rs13204160
- ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021 Q. Mei et al. 10.5194/essd-16-3213-2024
- Artificial Intelligence Algorithms for Rapeseed Fields Mapping Using Sentinel-1 Time Series: Temporal Transfer Scenario and Ground Sampling Constraints S. Maleki et al. 10.1109/JSTARS.2023.3316304
- CARM30: China annual rapeseed maps at 30 m spatial resolution from 2000 to 2022 using multi-source data W. Liu et al. 10.1038/s41597-024-03188-1
- Feature-based algorithm for large-scale rice phenology detection based on satellite images X. Zhao et al. 10.1016/j.agrformet.2022.109283
- Mapping winter rapeseed in South China using Sentinel-2 data based on a novel separability index J. TAO et al. 10.1016/j.jia.2022.10.008
- Accurate mapping of rapeseed fields in the initial flowering stage using Sentinel-2 satellite images and convolutional neural networks Y. Sun et al. 10.1016/j.ecolind.2024.112027
- Improving rapeseed carbon footprint evaluation via the integration of remote sensing technology into an LCA approach X. Yang et al. 10.1016/j.scitotenv.2024.174262
- Detection and Mapping of Cover Crops Using Sentinel-1 SAR Remote Sensing Data S. Najem et al. 10.1109/JSTARS.2023.3337989
- Mapping global water-surface photovoltaics with satellite images Z. Xia et al. 10.1016/j.rser.2023.113760
- WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping K. Van Tricht et al. 10.5194/essd-15-5491-2023
- Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation H. Li et al. 10.1016/j.rse.2023.113623
- A Novel Spectral Index for Automatic Canola Mapping by Using Sentinel-2 Imagery H. Tian et al. 10.3390/rs14051113
- Mapping rapeseed in China during 2017-2021 using Sentinel data: an automated approach integrating rule-based sample generation and a one-class classifier (RSG-OC) Y. Zang et al. 10.1080/15481603.2022.2163576
- CROPGRIDS: a global geo-referenced dataset of 173 crops F. Tang et al. 10.1038/s41597-024-03247-7
- Mapping rapeseed planting areas using an automatic phenology- and pixel-based algorithm (APPA) in Google Earth Engine J. Han et al. 10.1016/j.cj.2022.04.013
- NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019 J. Han et al. 10.5194/essd-13-5969-2021
- Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data Y. Luo et al. 10.3390/rs14081809
- STSNet: A cross-spatial resolution multi-modal remote sensing deep fusion network for high resolution land-cover segmentation B. Yu et al. 10.1016/j.inffus.2024.102689
- Phenology-Based Unsupervised Rapeseed Mapping Using Multitemporal Data S. Zhang et al. 10.1109/JSTARS.2022.3217665
- Annual paddy rice planting area and cropping intensity datasets and their dynamics in the Asian monsoon region from 2000 to 2020 J. Han et al. 10.1016/j.agsy.2022.103437
- Within-Season Crop Identification by the Fusion of Spectral Time-Series Data and Historical Crop Planting Data Q. Wang et al. 10.3390/rs15205043
- Planting area and production decreased for winter-triticeae crops but increased for rapeseed in Ukraine with climatic impacts dominating J. Han et al. 10.1016/j.geosus.2024.08.006
- Advancements in high-resolution land surface satellite products: A comprehensive review of inversion algorithms, products and challenges S. Liang et al. 10.1016/j.srs.2024.100152
Latest update: 23 Nov 2024
Short summary
Large-scale and high-resolution maps of rapeseed are important for ensuring global energy security. We generated a new database for the rapeseed planting area (2017–2019) at 10 m spatial resolution based on multiple data. Also, we analyzed the rapeseed rotation patterns in 25 representative areas from different countries. The derived rapeseed maps are useful for many purposes including crop growth monitoring and production and optimizing planting structure.
Large-scale and high-resolution maps of rapeseed are important for ensuring global energy...
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