TY - GEN
T1 - A Review of Remote Sensing in Sugarcane Mapping
AU - Li, Hui
AU - Di, Liping
AU - Zhang, Chen
AU - Lin, Li
AU - Guo, Liying
AU - Zhao, Haoteng
AU - Guo, Claire
AU - Hong, Ryan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Sugarcane, a significant essential economic crop for sugar products, bioethanol, and fiber material, is cultivated around the world near tropical regions, such as Brazil, India, China, and Thailand. The sugarcane spatial distribution data efficiently supports various applications of sugarcane management. A greater number of academic articles are heading to address sugarcane mapping. Furthermore, various machine learning algorithms have been used in sugarcane mapping based on diverse Earth Observation (EO) data that achieve considerable classification performance. This paper provides a brief review of sugarcane mapping in recent years. Specifically, this paper aims to: (1) summarizing and comparing remote sensing flatform depending on the various sensors; (2) reviewing different sugarcane mapping techniques with different machine learning methods; (3) describing the essential challenges in sugarcane classification under current remote sensing techniques and trying to discover a patient method for efficient sugarcane mapping.
AB - Sugarcane, a significant essential economic crop for sugar products, bioethanol, and fiber material, is cultivated around the world near tropical regions, such as Brazil, India, China, and Thailand. The sugarcane spatial distribution data efficiently supports various applications of sugarcane management. A greater number of academic articles are heading to address sugarcane mapping. Furthermore, various machine learning algorithms have been used in sugarcane mapping based on diverse Earth Observation (EO) data that achieve considerable classification performance. This paper provides a brief review of sugarcane mapping in recent years. Specifically, this paper aims to: (1) summarizing and comparing remote sensing flatform depending on the various sensors; (2) reviewing different sugarcane mapping techniques with different machine learning methods; (3) describing the essential challenges in sugarcane classification under current remote sensing techniques and trying to discover a patient method for efficient sugarcane mapping.
KW - earth observation
KW - machine learning
KW - remote sensing
KW - sugarcane mapping
UR - https://www.scopus.com/pages/publications/85172202147
UR - https://www.scopus.com/pages/publications/85172202147#tab=citedBy
U2 - 10.1109/Agro-Geoinformatics59224.2023.10233506
DO - 10.1109/Agro-Geoinformatics59224.2023.10233506
M3 - Conference contribution
AN - SCOPUS:85172202147
T3 - 2023 11th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2023
BT - 2023 11th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2023
Y2 - 25 July 2023 through 28 July 2023
ER -