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

Dorigo, Wouter A. and Richter, Rudolf and Baret, Frederic and Bamler, Richard and Wagner, Wolfgang (2009) Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach. Remote Sensing, 1, pp. 1139-1170. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs1041139 ISSN 2072-4292

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Official URL: http://www.mdpi.com/journal/remotesensing

Abstract

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 OPEN ACCESS Remote Sens. 2009, 1 1140 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.

Item URL in elib:https://elib.dlr.de/63036/
Document Type:Article
Title:Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Dorigo, Wouter A.wd (at) ipf.tuwien.ac.atUNSPECIFIED
Richter, Rudolfrudolf.richter (at) dlr.deUNSPECIFIED
Baret, FredericInstitut National de la Recherche Agronomique, AvignonUNSPECIFIED
Bamler, RichardRichard.Bamler (at) dlr.deUNSPECIFIED
Wagner, WolfgangTU MünchenUNSPECIFIED
Date:2009
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:1
DOI :10.3390/rs1041139
Page Range:pp. 1139-1170
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:Hyperspectral Data, model automation; grassland; meadow; imaging spectroscopy; precision agriculture; SPECL; vegetation index; semi-empirical approach; crops; agriculture
HGF - Research field:Aeronautics, Space and Transport (old)
HGF - Program:Space (old)
HGF - Program Themes:W - no assignment
DLR - Research area:Space
DLR - Program:W - no assignment
DLR - Research theme (Project):W - no assignment (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute
German Remote Sensing Data Center > Land Surface
Deposited By: Roehl, Cornelia
Deposited On:01 Feb 2010 14:15
Last Modified:08 Mar 2018 18:49

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