Accurate quantitative estimation of vegetation biochemical characteristics is necessary for a large variety of agricultural and ecological applications. The advent of hyperspectral remote sensing has offered possibilities for measuring specific vegetation variables that were difficult to measure using conventional multi-spectral sensors. In this study, the potential of biophysical modelling to predict leaf and canopy chlorophyll contents in a heterogeneous grassland is investigated. The well-known PROSAIL model was inverted with HyMap measurements by means of a look-up table (LUT). HyMap images along with simultaneous in situ measurements of chlorophyll content were acquired over a National Park. We tested the impact of using multiple solutions and spectral sub-setting on parameter retrieval. To assess the performance of the model inversion, the RMSE and R2 between independent in situ measurements and estimated parameters were used. The results of the study demonstrated that inversion of the PROSAIL model yield higher accuracies for Canopy chlorophyll content, in comparison to Leaf chlorophyll content (R2=0.84, RMSE=0.24). Further a careful selection of spectral subset, which comprised the development of a new method to subset the spectral data, proved to contain sufficient information for a successful model inversion. Consequently, it increased the estimation accuracy of investigated parameters (R2=0.87, RMSE=0.22). Our results confirm the potential of model inversion for estimating vegetation biochemical parameters using hyperspectral measurements.
|Number of pages||5|
|Journal||The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences|
|Publication status||Published - 2010|
|Event||ISPRS Technical Commission VII Symposium: 100 Years ISPRS: Advancing Remote Sensing Science - Vienna, Austria|
Duration: 5 Jul 2010 → 7 Jul 2010
- hyper spectral