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This paper presents a feasibility study using multi‐temporal Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) data to classify sugarcane crop. This study was carried out in São Paulo State which is the major sugarcane producer in Brazil, occupying more than 3.1 million hectares. Cloud‐free MODIS images (16 days mosaics) were acquired over a period of almost 15 months. Samples of sugarcane and non‐sugarcane were randomly selected and cluster analysis was performed to establish similar EVI temporal behaviour clusters. It was observed that EVI was sensitive to variations in land‐use cover mainly due to phenology and land management practices. Therefore, selection of sugarcane samples with similar EVI temporal behaviour for supervised classification was difficult due to both large planting and large harvesting periods. Consequently, cluster analysis was chosen to carry out an unsupervised classification. The best results were obtained in regions occupied by: natural and planted forest, soybean, peanuts, water bodies and urban areas which contrasted with the temporal‐spectral behaviour of sugarcane. The lowest performance was observed mainly in regions dominated by pasture, which has similar temporal‐spectral behaviour to sugarcane. This study provided useful information to define a MODIS image classification procedure for sugarcane crop for the whole State area based on the large amount of cloud‐free MODIS images when compared with other currently available optical sensors.
Xavier et al. (Tue,) studied this question.