Retrieval of Leaf Area Index from CHRIS/PROBA data: an analysis of the directional and spectral information content

The use of Earth Observation (EO) data to retrieve biophysical variables of vegetated surfaces has proved to be useful in many operative tools to gather information repetitively, at spatial and temporal resolution, for agricultural and water management applications. The launch of the European Space Agency (ESA) Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA) mission has provided an opportunity to study a multiangular and hyperspectral dataset of images with high spatial resolution. The objective of the study was to use the CHRIS/PROBA data, in both directional and spectral domains, to estimate the Leaf Area Index (LAI). For this purpose, inversion of a canopy reflectance model was performed against CHRIS data. LAI estimates were validated by using ground truth LAI measurements and compared, in terms of accuracy, to a semi-empirical approach. It was shown that, for a given spectral configuration, the directional information always improved the LAI estimation. For the best case (corn), this was achieved with an LAI root mean square error (RMSE) of 0.41 by using five angles and 62 spectral bands compared to a value of 1.42 by using one angle and four bands, as in the Landsat Thematic Mapper (TM) configuration.