The spaceborne ESA-mission CHRIS (Compact High Resolution Imaging Spectrometer) on-board PROBA-1 (Project for On-board Autonomy) delivers multi-directional data sets that contain spectral, directional, spatial and multi-temporal information.
CHRIS/PROBA data have been acquired over two well documented test sites in Switzerland (Swiss National Park (SNP) and Vordemwald (VOR) since 2003 and allow for the monitoring of complex and dynamic vegetation canopies of forests and agricultural crops. For vegetated surfaces, the spectral information content of CHRIS/PROBA may yield the biochemical and biophysical properties of vegetation canopies, while the directional component may deliver additional information on canopy structure. The CHRIS instrument offers the possibility to combine independent information sources, such as spectral and directional observations, for a complete and robust characterization of a vegetation canopy. Such an integrated approach bears the potential to improve the estimation of biophysical and biochemical canopy characteristics relevant for applications such as ecological modeling and precision agriculture.
A full pre-processing scheme for CHRIS/PROBA data for geometric and atmospheric processing over mountainous and hilly terrain, which is a pre-requisite for the subsequent spectro-directional data analysis, has been established. The three case studies presented in this paper deal with a) the assessment of canopy structure and heterogeneity from multi-angular data, b) the contribution of directional data for the estimation of canopy biochemistry and c) the estimation of leaf area index (LAI) from multi-temporal
CHRIS/PROBA data. Concerning the first case study, the structure and heterogeneity of a coniferous canopy based on its degree of reflectance anisotropy is addressed using multi-angular CHRIS data and the parametric Rahman-Pinty-Verstraete (RPV) model. The second case study aims at an improved retrieval of foliar nitrogen concentration (CN) and water content (CW) using multi-angular CHRIS data and ground truth in multiple linear regression analyses limited by a statistical variable selection method. In the third case study, a radiative transfer model (RTM) is coupled to a canopy structure dynamics model (CSDM) for provision of a continuous LAI time course of maize over the season. The resulting estimation of the temporal and spatial variation of LAI is improved by integrating multi-temporal CHRIS/PROBA data and ground meteorological observations.
The paper shows the potential and value of spectro- directional Earth observations, as provided by an innovative system like CHRIS/PROBA for Earth system studies.