Prototyping of physically based methods to retrieve leaf area index and canopy water content from satellite data

Regional scale vegetation monitoring and yield forecasting require the estimation of biophysical parameters, such as Leaf Area Index (LAI), biomass and canopy water content (CWC). The CWC is also targeted for fire risk and drought monitoring. This work presents a methodology for jointly retrieval of Canopy Water Content (CWC) and Leaf Area Index (LAI) from coarse resolution satellite data. The method combines the use of databases generated by radiative transfer models (PROSAIL) and powerful nonlinear regression approaches. The advantage of physical models is that they can be coupled each other, thereby providing a physically-based linkage between optical data and biochemical or structural state variables. Suitable parameter combinations of leaf and optical properties were used as inputs into the model to avoid unrealistic simulated spectra.