Inversion of radiative transfer model through one-dimensional convolutional neural network to retrieve foliar traits of temperate forest canopies with imaging spectrocopy

Estimation of foliar traits through imaging spectroscopy is one of the current major challenge in remote sensing to monitor ecosystem functions. New spaceborne hyperspectral sensors are upcoming or already in activity. In this context, future estimation models are required to be robust to temporal variations (e.g. illumination and phenological conditions) and changes in vegetation geometry in order to cope with the wide variety of canopy observations. Our work is threefold. First, we analyze the direct radiative transfer model to highlight potentials of one-dimensional CNN (1D-CNN) to retrieve foliar traits. Secondly, we build a 1D-CNN inverse model and search for an optimal architecture. Finally, we test the retrieval performance of our 1D-CNN on four oak species over four forest sites in California at different phenological stages during two years.