The impact of canopy structure assumption on the retrieval of GAI and Leaf Chlorophyll Content for wheat and maize crops

Green Area Index (GAI) and Leaf Chlorophyll Content (LCC) are key variables that reflect the potential growth of the canopy. In the past decades, the retrieval of these variables from remote sensing data to generate operational products at high spatial resolution (lower than decametric) was mainly based on 1D radiative transfer model inversion. However, due to the recent advances in computational facility, it is now possible to invert 3D radiative transfer models to improve the operational product accuracy. The use of 3D models allows taking into account more realistic canopy architectures than when using the turbid medium assumption from the 1D radiative transfer models. In this study, we demonstrate the gain in accuracy when inverting crop specific using 3D radiative transfer models as compared to a generic algorithm based on 1D model. We investigate two crops characterized by contrasted architectures along the vegetation cycle, e.g. wheat and maize.