Fine-Scale Inversion of Leaf Area Index and Chlorophyll Content Using Coupled 3D Radiative Transfer Model and Deep Learning

Vegetation Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) are crucial indicators for monitoring vegetation growth. Remote sensing combined with radiative transfer models (RTMs) is the primary method for estimating vegetation parameters. However, traditional 1D RTMs make idealized assumptions that are not suitable for fine-scale inversion. Although existing 3D RTMs can intricately consider the radiative propagation mechanisms of complex vegetation structures, they require high computational costs, making it challenging to conduct fine-scale vegetation parameter inversion at a regional scale. In this study, a mixed forest scene is utilized as the foundational application scenario. A semi-empirical 3D acceleration model, named Semi-LESS, is used for fast simulation to generate a large dataset of 3m spatial resolution multispectral imagery. A residual network is used to encode vegetation structure and establish relationships between multi-band reflectance and vegetation parameters for vegetation parameter inversion. Our results indicate favorable outcomes for LAI and LCC inversion in the mixed forest, with RMSE values of 0.24 and 3.68ug/cm
²
, respectively. The conclusion highlights the capability of quickly achieving fine-scale inversion of vegetation parameters using the coupled Semi-LESS and deep learning. Our approach also holds potential for achieving rapid fine-scale vegetation parameter mapping in orchards and agricultural scenes.