Applying Sentinel-1 Time Series Analysis to Sugarcane Harvest Detection

Sugarcane is the world's largest crop by production quantity, as reported by the Food and Agriculture Organization of the United Nations. Its growth cycle has a duration of about 12-14 months, and the same plantation can be generally harvested up to 7 times before replanting is needed. In order to both predict the yield and optimize the production processes, sugarcane industries need to be regularly updated on the harvest progress; however, they mainly rely on direct communications from farmers, and this has evident limitations.In this paper we present a method that exploits stacks of Sentinel-1 images for the automatic detection of sugarcane harvest dates. The method has been used to monitor, over a period of 21 months, a large cultivated area in Northern Senegal that comprises 719 sugarcane parcels.The results have shown that the method performs well in terms of both detection and estimation accuracy, and has the potential to be operationally used in the sugarcane production processes.