Articles | Volume 14, issue 4
https://doi.org/10.5194/essd-14-2065-2022
© Author(s) 2022. 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-14-2065-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
28 Apr 2022
Data description paper |
| 28 Apr 2022
| 28 Apr 2022
High-resolution map of sugarcane cultivation in Brazil using a phenology-based method
Yi Zheng
School of Atmospheric Sciences, Southern Marine Science and
Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai
519082, Guangdong, China
Ana Cláudia dos Santos Luciano
Department of Biosystems Engineering, Luiz de
Queiroz College of Agriculture (ESALQ), University of Sao Paulo, P.O. Box 9 Av. Padua
Dias 11, 13418-900 Piracicaba-SP, Brazil
Jie Dong
College of Geomatics & Municipal Engineering, Zhejiang University
of Water Resources and Electric Power, Hangzhou 310018, Zhejiang, China
Wenping Yuan
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Southern Marine Science and
Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai
519082, Guangdong, China
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Cited
23 citations as recorded by crossref.
- CROPGRIDS: a global geo-referenced dataset of 173 crops F. Tang et al. 10.1038/s41597-024-03247-7
- In-Season Mapping of Sugarcane Planting Based on Sentinel-2 Imagery H. Li et al. 10.1109/JSTARS.2024.3497653
- Winter Wheat Extraction Using Time-Series Sentinel-2 Data Based on Enhanced TWDTW in Henan Province, China X. Wang et al. 10.3390/su15021490
- Assessment of Land Suitability for Sugarcane Cultivation Using TOPSIS and Parametric Methods in Southwestern Iran A. Azadi et al. 10.1134/S1064229322602268
- Exploring the effects of training samples on the accuracy of crop mapping with machine learning algorithm Y. Fu et al. 10.1016/j.srs.2023.100081
- Fungal lignocellulolytic enzymes: an in silico and full factorial design approach E. de Souza Candeo et al. 10.1007/s11274-024-04241-2
- Impacts of ground-level ozone on sugarcane production A. Cheesman et al. 10.1016/j.scitotenv.2023.166817
- Agroclimatic and Mechanization Zoning of Sugarcane Associated with Ethanol Production Potential in Minas Gerais Under Climate Change Scenarios F. de Carvalho Júnior et al. 10.1007/s41651-025-00225-4
- Effects of sugarcane cultivation on the carbon dioxide dynamics of tropical headwater streams R. Taniwaki et al. 10.1080/20442041.2024.2431400
- O Papel da Fertirrigação e dos Agroquímicos Associados ao Cultivo da Cana-de-açúcar na Qualidade das Águas Doces do Brasil: Uma Revisão H. Caldas et al. 10.26848/rbgf.v18.4.p2573-2595
- Advances in safe processing of sugarcane and bagasse for the generation of biofuels and bioactive compounds A. Wani et al. 10.1016/j.jafr.2023.100549
- High-resolution distribution maps of single-season rice in China from 2017 to 2022 R. Shen et al. 10.5194/essd-15-3203-2023
- A Robust Index Based on Phenological Features to Extract Sugarcane from Multisource Remote Sensing Data Y. Liu et al. 10.3390/rs15245783
- Parcel-Based Sugarcane Mapping Using Smoothed Sentinel-1 Time Series Data H. Li et al. 10.3390/rs16152785
- A new method for classifying maize by combining the phenological information of multiple satellite-based spectral bands Q. Peng et al. 10.3389/fenvs.2022.1089007
- Water consumption in the production process of the sugar-energy industry: case study in the northwest of São Paulo (Brazil) W. Barroquela et al. 10.5327/Z2176-94781559
- Mapping Land Use and Land Cover Classes in São Paulo State, Southeast of Brazil, Using Landsat-8 OLI Multispectral Data and the Derived Spectral Indices and Fraction Images Y. Shimabukuro et al. 10.3390/f14081669
- A long-term paddy rice distribution dataset in Asia at a 30 m spatial resolution S. Li et al. 10.1038/s41597-025-05374-1
- Crop mapping using supervised machine learning and deep learning: a systematic literature review M. Alami Machichi et al. 10.1080/01431161.2023.2205984
- High-resolution mapping of global winter-triticeae crops using a sample-free identification method Y. Fu et al. 10.5194/essd-17-95-2025
- Mapping sugarcane globally at 10 m resolution using Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 S. Di Tommaso et al. 10.5194/essd-16-4931-2024
- Dissecting forest transition: Contribution of mature forests, second-growth forests and tree plantations to tree cover dynamics in the tropics J. de Jong et al. 10.1016/j.landusepol.2025.107545
- Annual winter wheat mapping dataset in China from 2001 to 2020 J. Dong et al. 10.1038/s41597-024-04065-7
22 citations as recorded by crossref.
- CROPGRIDS: a global geo-referenced dataset of 173 crops F. Tang et al. 10.1038/s41597-024-03247-7
- In-Season Mapping of Sugarcane Planting Based on Sentinel-2 Imagery H. Li et al. 10.1109/JSTARS.2024.3497653
- Winter Wheat Extraction Using Time-Series Sentinel-2 Data Based on Enhanced TWDTW in Henan Province, China X. Wang et al. 10.3390/su15021490
- Assessment of Land Suitability for Sugarcane Cultivation Using TOPSIS and Parametric Methods in Southwestern Iran A. Azadi et al. 10.1134/S1064229322602268
- Exploring the effects of training samples on the accuracy of crop mapping with machine learning algorithm Y. Fu et al. 10.1016/j.srs.2023.100081
- Fungal lignocellulolytic enzymes: an in silico and full factorial design approach E. de Souza Candeo et al. 10.1007/s11274-024-04241-2
- Impacts of ground-level ozone on sugarcane production A. Cheesman et al. 10.1016/j.scitotenv.2023.166817
- Agroclimatic and Mechanization Zoning of Sugarcane Associated with Ethanol Production Potential in Minas Gerais Under Climate Change Scenarios F. de Carvalho Júnior et al. 10.1007/s41651-025-00225-4
- Effects of sugarcane cultivation on the carbon dioxide dynamics of tropical headwater streams R. Taniwaki et al. 10.1080/20442041.2024.2431400
- O Papel da Fertirrigação e dos Agroquímicos Associados ao Cultivo da Cana-de-açúcar na Qualidade das Águas Doces do Brasil: Uma Revisão H. Caldas et al. 10.26848/rbgf.v18.4.p2573-2595
- Advances in safe processing of sugarcane and bagasse for the generation of biofuels and bioactive compounds A. Wani et al. 10.1016/j.jafr.2023.100549
- High-resolution distribution maps of single-season rice in China from 2017 to 2022 R. Shen et al. 10.5194/essd-15-3203-2023
- A Robust Index Based on Phenological Features to Extract Sugarcane from Multisource Remote Sensing Data Y. Liu et al. 10.3390/rs15245783
- Parcel-Based Sugarcane Mapping Using Smoothed Sentinel-1 Time Series Data H. Li et al. 10.3390/rs16152785
- A new method for classifying maize by combining the phenological information of multiple satellite-based spectral bands Q. Peng et al. 10.3389/fenvs.2022.1089007
- Water consumption in the production process of the sugar-energy industry: case study in the northwest of São Paulo (Brazil) W. Barroquela et al. 10.5327/Z2176-94781559
- Mapping Land Use and Land Cover Classes in São Paulo State, Southeast of Brazil, Using Landsat-8 OLI Multispectral Data and the Derived Spectral Indices and Fraction Images Y. Shimabukuro et al. 10.3390/f14081669
- A long-term paddy rice distribution dataset in Asia at a 30 m spatial resolution S. Li et al. 10.1038/s41597-025-05374-1
- Crop mapping using supervised machine learning and deep learning: a systematic literature review M. Alami Machichi et al. 10.1080/01431161.2023.2205984
- High-resolution mapping of global winter-triticeae crops using a sample-free identification method Y. Fu et al. 10.5194/essd-17-95-2025
- Mapping sugarcane globally at 10 m resolution using Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 S. Di Tommaso et al. 10.5194/essd-16-4931-2024
- Dissecting forest transition: Contribution of mature forests, second-growth forests and tree plantations to tree cover dynamics in the tropics J. de Jong et al. 10.1016/j.landusepol.2025.107545
1 citations as recorded by crossref.
Latest update: 10 Jul 2025
Short summary
Brazil is the largest sugarcane producer. Sugarcane in Brazil can be harvested all year round. The flexible phenology makes it difficult to identify sugarcane in Brazil at a country scale. We developed a phenology-based method which can identify sugarcane with limited training data. The sugarcane maps for Brazil obtain high accuracy through comparison against field samples and statistical data. The maps can be used to monitor growing conditions and evaluate the feedback to climate of sugarcane.
Brazil is the largest sugarcane producer. Sugarcane in Brazil can be harvested all year round....
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