Abstract
In this research, we compared two different sets of land surface phenological metrics (phenometrics) derived from dense satellite image time series to classify agricultural land in the Cerrado biome. We derived phenometrics from a dense Enhanced Vegetation Index (EVI) data cube with an 8-day temporal resolution and subjected them to classification using the Random Forest (RF) algorithm. We used a hierarchical classification with four levels, from land cover to crop rotation classes. We then evaluated the classification results comparing the use of phenometrics extracted using TIMESAT software [1], those obtained by polar representation, proposed by Korting et al. (2013) and the combination of both. We concluded that the accuracies of semi-perennial and winter crop classes increase substantially when using TIMESAT metrics combined with Polar features, and the misclassifications between single crops with non commercial crops are reduced.
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