A Review of Remote Sensing in Sugarcane Mapping

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.