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Contents 2.5.1 Leaf optical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.5.2 Canopy radiative transfer models . . . . . . . . . . . . . . . . . . . . . . . 29 2.5.3 Radiative transfer model inversion . . . . . . . . . . . . . . . . . . . . . . 31 2.5.3.1 Iterative optimisation techniques . . . . . . . . . . . . . . . . . . 31 2.5.3.2 Lookup table approach . . . . . . . . . . . . . . . . . . . . . . . 32 2.5.3.3 Artificial neural networks . . . . . . . . . . . . . . . . . . . . . . 33 2.5.4 Under-determination and ill-posedness in radiative transfer model inversion 33 2.5.5 Improving retrieval performances . . . . . . . . . . . . . . . . . . . . . . . 34 2.5.5.1 Increasing the observation dimensionality . . . . . . . . . . . . . 34 2.5.5.2 Constraints on input variables . . . . . . . . . . . . . . . . . . . 35 2.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3 The automated CRASh approach: theoretical concept and implementation 39 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2 Choosing an appropriate radiative transfer model . . . . . . . . . . . . . . . . . . 40 3.2.1 The leaf optical model PROSPECT . . . . . . . . . . . . . . . . . . . . . 41 3.2.1.1 N compact layers . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.2.1.2 Biochemical components . . . . . . . . . . . . . . . . . . . . . . 41 3.2.1.3 Simplifications and limitations . . . . . . . . . . . . . . . . . . . 45 3.2.2 The canopy reflectance model SAILh . . . . . . . . . . . . . . . . . . . . . 45 3.2.2.1 4-stream approximation . . . . . . . . . . . . . . . . . . . . . . . 45 3.2.2.2 Canopy characterization . . . . . . . . . . . . . . . . . . . . . . . 46 3.2.2.3 The hot spot effect . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.2.2.4 Assumptions and caveats . . . . . . . . . . . . . . . . . . . . . . 49 3.2.3 The soil background model . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.2.4 Interaction of variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3 The inversion approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3.1 Justifying a LUT based inversion scheme . . . . . . . . . . . . . . . . . . 51 3.3.2 Land cover classification for improved retrieval performance . . . . . . . . 53 3.3.3 Considerations underlying lookup table parametrization . . . . . . . . . . 55 3.3.3.1 Variable ranges . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.3.3.2 Variable distribution functions . . . . . . . . . . . . . . . . . . . 57 3.3.3.3 Sampling strategy . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.3.3.4 Sampling distance . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.3.3.5 Incorporating view and sun geometry . . . . . . . . . . . . . . . 58 3.3.3.6 Calculating diffuse radiance . . . . . . . . . . . . . . . . . . . . . 59 3.3.3.7 Background reflectance parametrization . . . . . . . . . . . . . . 59 3.4 The optimization algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.4.1 Exploiting radiometric information . . . . . . . . . . . . . . . . . . . . . . 62 3.4.1.1 Adding local spectral variance to overcome classification anomalies 64 3.4.1.2 Covariance matrix inversion . . . . . . . . . . . . . . . . . . . . 65 3.4.2 Exploiting a priori information on canopy variables . . . . . . . . . . . . . 66 3.4.2.1 Defining prior estimates using vegetation indices . . . . . . . . . 66 3.4.2.2 Assigning uncertainty to the prior estimates . . . . . . . . . . . 70 3.4.2.3 Defining the final solution . . . . . . . . . . . . . . . . . . . . . . 71 3.4.3 Accuracy description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 xiv
Contents 3.4.4 Accounting for angular anisotropy . . . . . . . . . . . . . . . . . . . . . . 72 3.5 Conclusions and preface to Chapter 4 and 5 . . . . . . . . . . . . . . . . . . . . . 74 4 Validating CRASh at ground and airborne level: grassland characterization using field spectrometer and HyMap data 77 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.1.1 Preceding grassland studies using imaging spectroscopy . . . . . . . . . . 78 4.1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.2 Study site and data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.2.1 Study site Waging-Taching . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.2.2 Ground validation measurements . . . . . . . . . . . . . . . . . . . . . . . 81 4.2.2.1 Configuration of ground sampling locations . . . . . . . . . . . . 81 4.2.2.2 Leaf area index . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.2.2.3 Leaf dry matter and water content . . . . . . . . . . . . . . . . . 84 4.2.2.4 Comparing results obtained with direct and indirect LAI sampling 85 4.2.2.5 Field spectrometer measurements . . . . . . . . . . . . . . . . . 86 4.2.3 HyMap imaging spectrometer measurements . . . . . . . . . . . . . . . . 88 4.2.3.1 Sensor characteristics . . . . . . . . . . . . . . . . . . . . . . . . 88 4.2.3.2 Flight configuration . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.2.3.3 Geometric correction . . . . . . . . . . . . . . . . . . . . . . . . 89 4.2.3.4 Calibration and atmospheric correction . . . . . . . . . . . . . . 91 4.3 Exploring algorithm potential and constraints using field spectrometer data . . . 92 4.3.1 Spectral field characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.3.2 Comparing modeled with measured reflectance . . . . . . . . . . . . . . . 94 4.3.3 Correlation between canopy variables and wavebands . . . . . . . . . . . . 95 4.3.4 Stepwise integration of algorithm components . . . . . . . . . . . . . . . . 97 4.3.4.1 Influence of land cover classification . . . . . . . . . . . . . . . . 99 4.3.4.2 Quantifying spectral covariance and the influence of sensor configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 4.3.4.3 Introducing prior information on variables . . . . . . . . . . . . 104 4.3.4.4 Introducing covariance between the variables . . . . . . . . . . . 105 4.3.4.5 Integrating a priori estimates based on predictive regression functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.3.4.6 Comparing predictions based on regression functions with final RTM estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 4.3.5 Exploring additional regularization . . . . . . . . . . . . . . . . . . . . . . 109 4.3.5.1 Estimating biochemicals at canopy level . . . . . . . . . . . . . . 110 4.3.5.2 Coupling Cw with Cdm . . . . . . . . . . . . . . . . . . . . . . . 111 4.3.6 Model sensitivity to LUT parametrization . . . . . . . . . . . . . . . . . . 112 4.3.6.1 Reproducibility of estimates . . . . . . . . . . . . . . . . . . . . 112 4.3.6.2 Dependence on variable ranges . . . . . . . . . . . . . . . . . . . 114 4.3.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 4.4 RTM inversion applied to HyMap flight lines . . . . . . . . . . . . . . . . . . . . 121 4.4.1 Accounting for spectral anisotropy . . . . . . . . . . . . . . . . . . . . . . 122 4.4.1.1 Quantifying spectral anisotropy . . . . . . . . . . . . . . . . . . 122 4.4.1.2 Incorporating view angle information in model inversion . . . . . 125 xv
Page 1: Retrieving canopy variables by radi
Page 5: German Aerospace Center - German Re
Page 8 and 9: Abstract The performance and stabil
Page 10 and 11: Zusammenfassung zu berechnen. Die a
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Page 22 and 23: List of Figures 3.18 Example of att
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Page 32 and 33: 1.2. User requirements on remote se
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4.5. Conclusions Figure 4.40: Estim
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4.5. Conclusions Generally, radiati
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5.1. Introduction surface propertie
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6.3. Conclusions data. The large di
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6.4. Outlook models, and ground bas
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Class name Decision rules snow b4/b
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B.2. SPECL class 3: Average vegetat
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B.4. SPECL class 5: Yellow vegetati
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B.5. SPECL class 6: Mixed vegetatio
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B.7. SPECL class 13: Sparse vegetat
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B.7. SPECL class 13: Sparse vegetat
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(continued) Vegetation Index Equati
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VIS NIR SWIR1 SWIR2 Band λ FWHM Ba
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Table E.1: Results of canopy measur
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Band nr. λ (nm) FWHM (nm) 1 442.5
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Bibliography Bacour, C. (2001). Con
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Bibliography Borel, C. C., Gerstl,
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Bibliography Combal, B., Baret, F.,
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Bibliography Garrigues, S., Allard,
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Bibliography Hapke, B. (1981). Bidi
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Bibliography Kaufman, Y. and Tanré
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Bibliography LICOR (2000). LAI-2000
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Bibliography Proceedings of the 8th
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Bibliography Rondeaux, G., Steven,
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Bibliography Suits, G. (1983). The
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Bibliography Weiss, M., Baret, F.,
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Bibliography 226
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Rischbeck, P., Schneider, W., Suppa
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Wouter Dorigo Date and place of bir
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