Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

Remote Sensing

Wouter Dorigo³  Rudolf Richter²  Frຝéric Baret⁴  Richard Bamler¹ 

[1]German Aerospace Center, Remote Sensing Technology Institute (IMF), 82234 Wessling, Germany

[2] E-Mail:

[3]German Aerospace Center, German Remote Sensing Data Center (DFD), 82234 Wessling, Germany

[4] E-Mail:

[5]Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Gusshausstrasse 27-29, 1040 Vienna, Austria

[6] E-Mails:

[7]Institut National de la Recherche Agronomique—EMMAH UMR1114, Agroparc, 84914 Avignon, France

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关键词: model automation;    grassland;    meadow;    imaging spectroscopy;    precision agriculture;    SPECL;    vegetation index;    semi-empirical approach;    crops;    agriculture;   

DOI  :  10.3390/rs1041139

来源: mdpi

【 摘 要 】

Automated, image based methods for the retrieval of vegetation biophysical and biochemical variables are often hampered by the lack of a priori knowledge about land cover and phenology, which makes the retrieval a highly underdetermined problem. This study addresses this problem by presenting a novel approach, called CRASh, for the concurrent retrieval of leaf area index, leaf chlorophyll content, leaf water content and leaf dry matter content from high resolution solar reflective earth observation data. CRASh, which is based on the inversion of the combined PROSPECT+SAILh radiative transfer model (RTM), explores the benefits of combining semi-empirical and physically based approaches. The approach exploits novel ways to address the underdetermined problem in the context of an automated retrieval from mono-temporal high resolution data. To regularize the inverse problem in the variable domain, RTM inversion is coupled with an automated land cover classification. Model inversion is based on a two step lookup table (LUT) approach: First, a range of possible solutions is selected from a previously calculated LUT based on the analogy between measured and simulated reflectance. The final solution is determined from this subset by minimizing the difference between the variables used to simulate the spectra contained in the reduced LUT and a first guess of the solution. This first guess of the variables is derived from predictive semi-empirical relationships between classical vegetation indices and the single variables. Additional spectral regularization is obtained by the use of hyperspectral data. Results show that estimates obtained with CRASh are significantly more accurate than those obtained with a tested conventional RTM inversion and semi-empirical approach. Accuracies obtained in this study are comparable to the results obtained by various authors for better constrained inversions that assume more a priori information. The completely automated and image-based nature of the approach facilitates its use in operational chains for upcoming high resolution airborne and spaceborne imaging spectrometers.

【 授权许可】

CC BY   
© 2009 by the authors; licensee MDPI, Basel, Switzerland.