Impact of multiangular information on empirical models to estimate canopy nitrogen concentration in mixed forest

Directional effects in remotely sensed reflectance data can influence the retrieval of plant biophysical and biochemical estimates. Previous studies have demonstrated that directional measurements contain added information that may increase the accuracy of estimated plant structural parameters. Because accurate biochemistry mapping is linked to vegetation structure, also models to estimate canopy nitrogen concentration (CN) may be improved indirectly from using multiangular data. Hyperspectral imagery with five different viewing zenith angles was acquired by the spaceborne CHRIS sensor over a forest study site in Switzerland. Fifteen canopy reflectance spectra corresponding to subplots of field-sampled trees were extracted from the preprocessed CHRIS images and subsequently two-term models were developed by regressing CN on four datasets comprising either original or continuum-removed reflectances. Consideration is given to the directional sensitivity of the CN estimation by generating regression models based on various combinations (n=15) of observation angles. The results of this study show that estimating canopy CN with only nadir data is not optimal irrespective of spectral data processing. Moreover adding multiangular information improves significantly the regression model fits and thus the retrieval of forest canopy biochemistry. These findings support the potential of multiangular Earth observations also for application-oriented ecological monitoring

Coupling imaging spectroscopy and ecosystem process modelling: the importance of spatially distributed foliar biochemical concentration estimates for modelling NPP of grassland habitats

Information on canopy chemical concentrations is of great importance for the study of nutrient cycling, productivity and for input to ecosystem process models. In particular, foliar Carbon to Nitrogen ratio (C:N) drives terrestrial biogeochemical processes such as decomposition and mineralization, and thus strongly influences soil organic matter concentrations and turnover rates. This study evaluated the effects of using spatial estimates of foliar C:N derived from hyperspectral remote sensing for simulating NPP by means of the ecosystem process model Biome-BGC. The main objectives of this study were to calibrate spatial statistical models for the prediction of foliar C:N for grassland habitats at the regional scale, using airborne HyMap hyperspectral data, to use the foliar C:N predictions as input to the ecosystem process model Biome-BGC and derive NPP estimates and finally to compare these results to NPP estimates derived using C:N value reported in literature and derived from field measurements. Results from this research indicate that NPP estimates using the HyMap predicted C:N differed significantly from those when C:N values from “global” or “regional” measurements were used. Extending the current research to broader spatial scales can help to initialise, validate and adjust better ecological process models