Towards a massive sentinel-2 LAI time-series production using 2-D convolutional networks

Biophysical parameters and more specifically the leaf area index provide an absolute quantification of the biomass of vegetation allowing an overview of the development status of a plant. However, the estimation of the leaf area index requires sophisticated and complex algorithms. This paper proposes a new procedure to estimate the leaf area index using Sentinel-2 data. The proposed procedure relies on the 2-D convolutional network known as the UNet algorithm for regression. The architecture of the UNet algorithm is adapted to account for the processing of large chunks of Sentinel-2 data. Moreover, the adopted procedure makes use of the dropout as a Bayesian approximation at the inference step in order to allow estimating the algorithm confidence interval, which is a very important quality indicator for the production of biophysical parameters. The proposed procedure is validated on multiple Sentinel-2 tiles and years and compared to the multilayer perceptron algorithm and the Sentinel Application Platform of the European Space Agency, also known as SNAP. The UNet and multilayer perceptron algorithms provide coherent results when compared to the results obtained using the SNAP software with an average correlation of 0.99 for both algorithms. However, the UNet algorithm provides better results in terms of average Euclidean distance, mean squared error and R2 score. One main advantage of the UNet algorithm is the vast reduction of inference time when compared to the SNAP software and the multilayer perceptron regressor. The estimation of the leaf area index of a Sentinel-2 tile at 20 m requires 18 s, 13.5 min and 15 min using the UNet, multilayer perceptron and SNAP, respectively. This advantage allows a massive production of temporal sequences of leaf area index based on Sentinel-2 images. Furthermore, experiments conducted on multiple crop types prove that the proposed approach can serve as a generic procedure to estimate the leaf area index regardless of the crop type.