Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy |
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| Institution: | 1. University of Natural Resources and Life Sciences (BOKU), Institute of Surveying, Remote Sensing and Land Information, Peter Jordan-Strasse 82,1190 Vienna, Austria;2. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, P.O. Box 6, 7500 AA Enschede, The Netherlands;3. Centre de Recherche Public – Gabriel Lippmann, Département Environnement et Agrobiotechnologies, 41, rue du Brill, L-4422 Belvaux, Luxembourg;4. CIRAD, UMR Eco&Sols, 2 Place Viala, 34060 Montpellier, France |
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| Abstract: | Fine scale maps of vegetation biophysical variables are useful status indicators for monitoring and managing national parks and endangered habitats. Here, we assess in a comparative way four different retrieval methods for estimating leaf area index (LAI) in grassland: two radiative transfer model (RTM) inversion methods (one based on look-up-tables (LUT) and one based on predictive equations) and two statistical modelling methods (one partly, the other entirely based on in situ data). For prediction, spectral data were used that had been acquired over Majella National Park in Italy by the airborne hyperspectral HyMap instrument. To assess the performance of the four investigated models, the normalized root mean squared error (nRMSE) and coefficient of determination (R2) between estimates and in situ LAI measurements are reported (n = 41). Using a jackknife approach, we also quantified the accuracy and robustness of empirical models as a function of the size of the available calibration data set. The results of the study demonstrate that the LUT-based RTM inversion yields higher accuracies for LAI estimation (R2 = 0.91, nRMSE = 0.18) as compared to RTM inversions based on predictive equations (R2 = 0.79, nRMSE = 0.38). The two statistical methods yield accuracies similar to the LUT method. However, as expected, the accuracy and robustness of the statistical models decrease when the size of the calibration database is reduced to fewer samples. The results of this study are of interest for the remote sensing community developing improved inversion schemes for spaceborne hyperspectral sensors applicable to different vegetation types. The examples provided in this paper may also serve as illustrations for the drawbacks and advantages of physical and empirical models. |
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| Keywords: | Leaf area index Radiative transfer model Look-up table Narrow band vegetation index Predictive equation Sample size |
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