Benchmarking of algorithms for crop type land-cover maps using Sentinel-2 image time series

Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems such as Sentinel-2 constitute a major asset for this kind of application. The goal of this paper is to assess to which extent state of the art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type maps at the global scale. Five concurrent strategies for automatic crop type map production have been selected and benchmarked using SPOT4 (Take5) and LANDSAT8 data over 12 test sites spread all over the globe. The results show that a Random Forest classifier operating on linearly temporally gap-filled images can achieve overall accuracies above 80% for most sites. The approach is fully automatic.