Acknowledgments |
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xiii | |
About the Editors |
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xv | |
Contributors |
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xvii | |
Introduction |
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xxi | |
Part I: Basic principles of imaging spectrometry |
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1 | (62) |
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Basic physics of spectrometry |
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3 | (14) |
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3 | (1) |
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4 | (1) |
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Surface scattering properties |
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4 | (1) |
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5 | (1) |
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Reflectance properties of materials |
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6 | (9) |
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7 | (3) |
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10 | (1) |
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11 | (2) |
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13 | (1) |
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Man-made and other materials |
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14 | (1) |
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The effect of the atmosphere |
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14 | (1) |
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15 | (2) |
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Imaging spectrometry: Basic analytical techniques |
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17 | (46) |
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17 | (1) |
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Imaging spectrometry: airborne systems |
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18 | (3) |
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20 | (1) |
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Imaging spectrometry: spaceborne instruments |
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21 | (3) |
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Spaceborne versus airborne data |
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24 | (7) |
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Costs of a spaceborne system |
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25 | (1) |
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Costs of an airborne system |
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26 | (1) |
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27 | (1) |
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28 | (1) |
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28 | (1) |
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29 | (1) |
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30 | (1) |
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30 | (1) |
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31 | (1) |
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Laboratory set-up of a pre-processing calibration facility |
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31 | (1) |
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The spectral pre-processing chain |
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32 | (3) |
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35 | (1) |
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Quality control: signal to noise characterization |
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35 | (4) |
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The ``homogeneous area method'' |
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36 | (1) |
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The ``local means and local variances method'' |
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36 | (1) |
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The ``geostatistical method'' |
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37 | (1) |
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38 | (1) |
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39 | (2) |
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39 | (1) |
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40 | (1) |
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Re-sampling and image simulation |
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41 | (3) |
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Analytical processing techniques |
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44 | (16) |
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44 | (1) |
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44 | (1) |
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Waveform characterization |
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45 | (2) |
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Spectral Feature Fitting (SFF) |
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47 | (1) |
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Spectral Angle Mapping (SAM) |
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47 | (1) |
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47 | (4) |
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Iterative spectral unmixing |
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51 | (4) |
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Constrained energy minimization (CEM) |
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55 | (1) |
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Foreground-background analysis |
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55 | (1) |
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56 | (1) |
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Cross Correlogram Spectral Matching (CCSM) |
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56 | (1) |
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57 | (3) |
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Endmember selection for spectral unmixing and other feature finding algorithms |
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60 | (3) |
Part II prospective applications of imaging spectrometry |
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63 | (298) |
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Imaging spectrometry for surveying and modelling land degradation |
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65 | (22) |
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65 | (1) |
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Processes of land degradation |
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66 | (4) |
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66 | (2) |
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Vegetation and Degradation processes |
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68 | (2) |
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Spectrometry for land degradation |
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70 | (1) |
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71 | (5) |
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Classical Soil Description Methods |
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71 | (1) |
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72 | (4) |
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Mapping soil degraded state by imaging spectrometry and spectral unmixing |
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76 | (4) |
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Mapping Soil Units using imaging spectrometry and Spectral Matching |
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77 | (3) |
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80 | (3) |
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Spectral Reflectance of Vegetation |
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80 | (1) |
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Conventional Remote Sensing and Vegetation |
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81 | (1) |
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Assessing Vegetation Properties for Erosion Models from spectroscopical images |
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82 | (1) |
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Contextual approaches to land cover mapping |
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83 | (3) |
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86 | (1) |
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Field and imaging spectrometry for identification and mapping of expansive soils |
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87 | (24) |
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87 | (8) |
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Nature of expansive soils |
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87 | (4) |
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Spectroscopic indicators of clay minerals |
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91 | (4) |
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Field and laboratory analyses |
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95 | (5) |
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Expansive soils in the Front Range Urban Corridor (Colorado) |
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95 | (2) |
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Field sampling and laboratory analyses |
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97 | (1) |
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Relationships between reflectance, mineralogy and swelling potential |
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98 | (2) |
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Hyperspectral image analysis |
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100 | (8) |
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Expansive clays in Colorado: remote sensing considerations |
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100 | (2) |
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Images acquisition and analysis |
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102 | (3) |
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105 | (3) |
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108 | (3) |
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Imaging spectrometry and vegetation science |
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111 | (46) |
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111 | (2) |
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Spectroscopy versus spectrometry |
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113 | (2) |
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Fundamental factors affecting vegetation reflectance |
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115 | (6) |
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115 | (1) |
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reflectance (400-700 nm.) |
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115 | (2) |
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The reflectance red-edge (690- 720nm) |
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117 | (1) |
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The near-infrared region (700-1300nm) |
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117 | (2) |
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The mid-infrared region (1300 - 2500nm) |
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119 | (1) |
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119 | (2) |
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Vegetation reflectance curve |
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121 | (6) |
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121 | (3) |
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124 | (3) |
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127 | (3) |
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127 | (1) |
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127 | (1) |
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Models based on the Kubelka-Munk (K-M) theory |
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128 | (1) |
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129 | (1) |
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130 | (1) |
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130 | (7) |
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Chemical Compounds in Plants and methods of estimation |
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130 | (3) |
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133 | (4) |
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Reflectance models for foliar biochemical estimations |
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137 | (3) |
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Radiative transfer modelling |
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137 | (1) |
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137 | (1) |
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Canopy reflectance models |
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138 | (1) |
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Spectral analogies between leaves and canopies |
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139 | (1) |
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Applications: Field spectroscopy for vegetation studies |
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140 | (6) |
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140 | (1) |
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141 | (1) |
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142 | (2) |
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144 | (2) |
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Applications: Airborne imaging spectroscopy for vegetation studies |
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146 | (4) |
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Extracting biophysical variables (e.g. LAI, FAPAR, Cover) |
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146 | (3) |
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149 | (1) |
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Hyperspectral-BRDF inverse modelling |
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150 | (3) |
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150 | (2) |
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152 | (1) |
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153 | (4) |
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Extracting Biochemical variables |
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153 | (1) |
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154 | (1) |
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154 | (3) |
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Imaging spectrometry for agricultural applications |
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157 | (44) |
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157 | (1) |
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Role of imaging spectroscopy in agriculture |
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158 | (5) |
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158 | (1) |
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159 | (1) |
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160 | (1) |
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160 | (1) |
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160 | (1) |
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160 | (1) |
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161 | (1) |
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162 | (1) |
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163 | (12) |
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163 | (1) |
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Definition red-edge index |
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163 | (1) |
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Simulations using radiative transfer models |
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163 | (2) |
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165 | (1) |
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165 | (1) |
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166 | (1) |
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Simulation results at the leaf level |
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167 | (1) |
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Simulation results at the canopy level |
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167 | (6) |
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173 | (1) |
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Discussion and conclusions |
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174 | (1) |
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Case study I - Red-edge index and crop nitrogen status |
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175 | (4) |
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175 | (1) |
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Estimation of nitrogen status |
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175 | (1) |
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Estimation of nitrogen deficiency |
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176 | (1) |
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176 | (1) |
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176 | (1) |
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Set-up of the potato trials |
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176 | (1) |
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177 | (1) |
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177 | (1) |
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178 | (1) |
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Case study II - A framework for crop growth monitoring |
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179 | (11) |
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179 | (1) |
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Framework for yield prediction |
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179 | (1) |
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179 | (2) |
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181 | (1) |
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Estimating leaf angle distribution (LAD) |
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182 | (1) |
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Estimating leaf optical properties in the PAR region |
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182 | (1) |
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Linking optical remote sensing with crop growth models |
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183 | (1) |
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184 | (1) |
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184 | (1) |
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185 | (1) |
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185 | (1) |
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185 | (1) |
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CropScan™ ground-based reflectances |
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185 | (1) |
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185 | (1) |
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186 | (1) |
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Measurements of leaf optical properties |
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186 | (1) |
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186 | (1) |
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187 | (1) |
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Estimating leaf optical properties |
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187 | (1) |
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Results calibration SUCROS |
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188 | (1) |
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189 | (1) |
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Case study III - Using MERIS for deriving the red-edge index |
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190 | (7) |
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190 | (1) |
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191 | (1) |
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192 | (1) |
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192 | (1) |
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Red-edge index simulation with MERIS |
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192 | (3) |
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Upscaling to the MERIS resolution |
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195 | (1) |
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195 | (2) |
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197 | (4) |
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Imaging spectrometry and geological applications |
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201 | (18) |
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201 | (1) |
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Mineral mapping; surface mineralogy |
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201 | (1) |
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Mineral mapping; exploration |
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202 | (1) |
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Mineral mapping; lithology |
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203 | (1) |
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Vegetation stress and geobotany |
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204 | (1) |
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204 | (1) |
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Petroleum related studies |
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205 | (1) |
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Atmospheric effects resulting from geologic processes |
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205 | (1) |
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206 | (1) |
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Case-study I: Petroleum case study: seepage detection at Bluff using mineral alteration |
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206 | (7) |
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206 | (1) |
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The Bluff area and petroleum geology |
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207 | (3) |
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Probe-1 imaging spectrometer data |
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210 | (1) |
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Mineral alteration mapping for microseepage detection at Bluff |
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211 | (1) |
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Quaternary eolian loess deposits (Qe) |
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211 | (1) |
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211 | (1) |
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Brushy Basin Member (Jmb) |
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211 | (1) |
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211 | (2) |
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213 | (1) |
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Mapping gray-green colored rocks that may associate with hydrocarbon microseepage |
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213 | (1) |
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213 | (5) |
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Background on the mining problem |
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213 | (2) |
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215 | (2) |
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Validation and interpretation |
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217 | (1) |
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218 | (1) |
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Imaging spectrometry and petroleum geology |
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219 | (24) |
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219 | (1) |
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219 | (3) |
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222 | (3) |
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222 | (2) |
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Microbial effects and Hydrocarbon-induced surface manifestations |
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224 | (1) |
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Detecting hydrocarbon-induced surface manifestations by remote sensing |
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225 | (6) |
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226 | (1) |
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226 | (1) |
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227 | (1) |
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228 | (3) |
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231 | (1) |
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232 | (1) |
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232 | (1) |
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Trends in monitoring emissions |
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233 | (1) |
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A case study from Santa Barbara, southern California |
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233 | (10) |
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Petroleum geology of the Southern Californian Basins |
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233 | (4) |
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Hyperspectral data analysis |
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237 | (6) |
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Imaging spectrometry for urban applications |
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243 | (40) |
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243 | (1) |
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Remote Sensing for Urban Applications |
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244 | (1) |
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Aspects of Remote Sensing of the Urban Environment |
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245 | (1) |
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HSR and Urban Applications |
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246 | (2) |
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Spectral Properties of Urban Material |
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248 | (14) |
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Building a Spectral Library of Urban Objects from the existing database |
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251 | (1) |
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251 | (1) |
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252 | (7) |
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Summary for Urban Library from Existing Database |
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259 | (1) |
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Building a Spectral Library of Urban Objects from in Situ Measurements |
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260 | (2) |
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Summary for Urban Library from in-Situ Measurements |
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262 | (1) |
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Spectral Pattern Recognition |
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262 | (10) |
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Examining the Spectral-Based Information for Urban Mapping in the VIS-NIR Region |
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262 | (1) |
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Examining the Spectral-Based Information for Urban Mapping in the VIS-NIR-SWIR Region |
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263 | (1) |
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Examining the Spectral-Based Information for Urban Mapping, Using the TIR Region |
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264 | (6) |
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Special Benefit of the HSR over Urban Areas: Asphalt and Shade |
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270 | (1) |
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Summary and Conclusion for the Spectral Analysis |
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271 | (1) |
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Remote Sensing of the Urban Atmosphere using HSR |
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272 | (1) |
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Recent HSR Urban Applications: A Discussion |
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273 | (3) |
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Recommendation for HSR Utilization over Urban Areas |
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276 | (4) |
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Data Evaluation and Processing |
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277 | (1) |
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Spectral Preprocessing steps: |
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277 | (2) |
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279 | (1) |
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279 | (1) |
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280 | (1) |
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280 | (3) |
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Imaging spectrometry in the Thermal Infrared |
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283 | (24) |
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283 | (1) |
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284 | (3) |
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284 | (1) |
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284 | (1) |
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Emissivity of rocks and minerals |
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284 | (3) |
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Current TIR airborne systems |
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287 | (14) |
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287 | (1) |
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287 | (7) |
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294 | (2) |
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296 | (2) |
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298 | (3) |
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301 | (2) |
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301 | (1) |
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302 | (1) |
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Future of Hyperspectral TIR Imaging |
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303 | (4) |
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Major Trends in Hyperspectral Remote Sensing |
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303 | (2) |
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The Future - 1-5 Year Forecast: |
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305 | (1) |
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The Future: 5-15 Year Forecast |
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306 | (1) |
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Imaging spectrometry of water |
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307 | (54) |
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307 | (1) |
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308 | (19) |
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Introduction to the theory |
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308 | (2) |
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310 | (1) |
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Optical properties of the water column for optically deep waters |
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311 | (1) |
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The inherent optical properties |
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311 | (2) |
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Radiometric variables and apparent optical properties |
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313 | (3) |
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The diffuse apparent optical properties |
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316 | (1) |
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The two-flow model for irradiance |
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316 | (5) |
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An analytical model for the irradiance reflectance |
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321 | (2) |
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323 | (2) |
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325 | (1) |
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325 | (1) |
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Atmospheric effects and atmospheric correction |
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326 | (1) |
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Optically deep and shallow waters: applications and case studies |
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327 | (31) |
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327 | (1) |
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Optically deep inland and estuarine waters |
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327 | (1) |
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Imaging spectrometry of optically deep inland waters |
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327 | (7) |
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Imaging spectrometry of optically deep estuaries |
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334 | (6) |
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Conclusions for imaging spectrometry of optically deep inland and estuarine waters |
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340 | (1) |
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341 | (1) |
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Bathymetry and bright substrate mapping |
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341 | (4) |
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Macrophyte/seagrass and macro-algae mapping |
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345 | (12) |
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Conclusions imaging spectrometry of optically shallow waters |
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357 | (1) |
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358 | (3) |
Acronyms |
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361 | (4) |
Index |
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365 | (6) |
References |
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371 | |