Articles | Volume 14, issue 4
Earth Syst. Sci. Data, 14, 2065–2080, 2022
https://doi.org/10.5194/essd-14-2065-2022
Earth Syst. Sci. Data, 14, 2065–2080, 2022
https://doi.org/10.5194/essd-14-2065-2022
Data description paper
28 Apr 2022
Data description paper | 28 Apr 2022

High-resolution map of sugarcane cultivation in Brazil using a phenology-based method

Yi Zheng et al.

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Cited articles

Abdel-Rahman, E. M. and Ahmed, F. B.: The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: a review of the literature, Int. J. Remote Sens., 29, 3753–3767, https://doi.org/10.1080/01431160701874603, 2008. 
Adami, M., Theodor Rudorff, B. F., Freitas, R. M., Aguiar, D. A., Sugawara, L. M., and Mello, M. P.: Remote Sensing Time Series to Evaluate Direct Land Use Change of Recent Expanded Sugarcane Crop in Brazil, Sustainability, 4, 574–585, https://doi.org/10.3390/su4040574, 2012. 
Belgiu, M. and Csillik, O.: Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis, Remote Sens. Environ., 204, 509–523, https://doi.org/10.1016/j.rse.2017.10.005, 2018. 
Bendini, H. D. N., Fonseca, L. M. G., Schwieder, M., Korting, T. S., Rufin, P., Sanches, I. D. A., Leitao, P. J., and Hostert, P.: Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series, Int. J. Appl. Earth Obs., 82, 101872, https://doi.org/10.1016/j.jag.2019.05.005, 2019.  
Bordonal, R. D. O., Lal, R., Aguiar, D. A., de Figueiredo, E. B., Perillo, L. I., Adami, M., Theodor Rudorff, B. F., and La Scala, N.: Greenhouse gas balance from cultivation and direct land use change of recently established sugarcane (Saccharum officinarum) plantation in south-central Brazil, Renew. Sust. Energ. Rev., 52, 547–556, https://doi.org/10.1016/j.rser.2015.07.137, 2015. 
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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.