RETRIEVAL OF LEAF AREA INDEX AND LEAF CHLOROPHYLL CONTENT FROM HYPERSPECTRAL DATA USING DEEP LEARNING NETWORKS

Abstract

This study aimed to exploit the use of deep learning networks in the retrieval of the biophysical and biochemical parameters of vegetation canopies. Convolutional Neural Network (CNN), network with only fully connected layers, referred as dense network (DNN), and Autoencoder (AE) were investigated to retrieve leaf area index (LAI) and leaf chlorophyll content. Hyperspectral data simulated by the coupled PROSPECT and SAIL model were used for training and validation. The real CASI hyperspectral data in 50 spectral channels ranging from 522.4 nm to 894.2 nm collected over three agricultural crop fields during the growing season of 2001 were used, together with the in-situ measured LAI and leaf chlorophyll content, as independent test set. Occlusion analysis was also employed to determine the important spectral bands at which reflectance made more contributions to the retrieval with a CNN and interpret the latent variables of the AE. Satisfactory results from these deep learning networks were obtained, compared with ground truth. The DNN with the input of the vegetation indices sensitive to LAI and leaf chlorophyll content (MTVI2 and TCARI/OSAVI) generated the best results with R2 of 0.86 for LAI and 0.55 for leaf chlorophyll content.