Mapping Sugarcane Using Vegetation Indices and Time Series of Sentinel-2 Images.

Sugarcane is one of the most important crops in Mexico; but it is facing increasing issues in its productivity, making necessary implementing monitoring techniques to improve crop management. To contribute to solve the problem, a method to identify anomalies in sugarcane cultivations was proposed through image data obtained from MultiSpectral Instrument sensor (MSI) onboard Sentinel-2 satellites. Three main steps were defined in the methodology: (1) image preprocessing, (2) Index calculation, and (3) anomaly detection. Two study areas were defined. On one hand, an area with a thorough control of the crop was selected, of which a health profile was defined by using PVI and LAI indices. On the other hand, a test area was selected to detect possible anomalous zones in the crop. Results showed that test area of sugarcane crop showed anomalies from day 200. To conclude, determining areas presenting deficiencies in sugarcane crops was possible through the proposed method.