Crop classification from Sentinel-2 time series with temporal convolutional neural networks

Abstract

Automated crop identification tools are of interest to a wide range of applications related to the environment and agriculture including the monitoring of related policies such as the European Common Agriculture Policy. In this context, this work presents a parcel-based crop classification system which leverages on 1D convolutional neural network supervised learning capacity. For the training and evaluation of the model, we employ open and free data: (i) time series of Sentinel-2 optical data selected to cover the crop season of one year, and (ii) a cadastre-derived database providing detailed delineation of parcels. By considering the most dominant crop types and the temporal features of the optical data, the proposed lightweight approach discriminates a considerable number of crops with high accuracy.