Sun and sensor geometry cause directional effects in remotely sensed reflectance data which can influence the estimation of biophysical and biochemical variables. Previous studies have indicated that bidirectional measurements contain added information with which the accuracy of derived plant structural parameters can be increased. Because accurate biochemistry mapping is linked to vegetation structure, estimates of nitrogen concentration (CN) and water content (CW) might be indirectly improved with multiangular information. We analyzed data of the spaceborne ESA-mission CHRIS on-board PROBA-1, which provides hyperspectral and directional data. The images were acquired in July 2006 over a forest study site in Switzerland. From each of the five CHRIS images (five different viewing zenith angles) we extracted 60 crown spectra, which correspond to field-sampled trees. Then we developed four-term models by regressing lab-measured CN and CW on two datasets either consisting of original reflectance values (SPEC) or continuum-removed data (BNC). The wavebands used in the regression models were determined with a subset selection algorithm. For the data of all view angle combinations particular models were generated, in total 31 equations were evaluated per spectral dataset by comparing the coefficients of determination (R2) and cross-validated root mean square errors. The results showed that directional information contained in multiangular data improved regression models for CN and CW estimation and lowered RMS errors. Considerable contribution can be achieved with data of a second and third viewing zenith angle. Monodirectional models developed on data of backward scattering angles performed in general better than models based on forward scattering data. Multidirectional models based on combinations of off-nadir data performed best for CN but not for CW. These findings support the potential of multiangular Earth observations for ecological monitoring and modeling studies.