TY  - BOOK
AU  - Kwast, Johannes van der
TI  - Quantification of top soil moisture patterns : evaluation of field methods, process-based modelling, remote sensing and an integrated appoach
SN  - 9068094246
U1  - 631.432 
PY  - 2009///
CY  - Utrecht (Netherlands)
PB  - Netherlands Geographical Studies; 
N1  - Contents Figures  9 Tables  15 Abbrevations and acronyms  17 List of Symbols Part II  21 List of Symbols Part III  24 List of Symbols Part IV  26 1  Introduction  31 1.1  Context  31 1.2  Problem definition  32 1.2.1Soil moisture patterns  33 1.2.2Field methods  35 1.2.3Process-based models  37 1.2.4Remote sensing  40 1.2.5Data assimilation  42 1.2.6Scaling issues  44 1.3  Study areas  45 1.4  Objective, research questions and thesis outline  46 Part I Remote sensing data for soil moisture modelling 2  Review of remote sensing techniques  51 2.1Remote sensing for soil moisture mapping  51 2.2  Optical reflectance of vegetation  52 2.3  Remote sensing image classification  54 2.4  Spectral vegetation indices  55 2.5  Radiation data from satellites  58 2.6  DEMs derived from Remote Sensing  59 3  Case Study: DEM generation from Remote Sensing, a comparison between aerial photographs, ASTER and SRTM63 3.1  Introduction  63 3.2Study area and datasets  64 3.3  Methodology  68 3.3.1DEM extraction  68 3.3.2Aerial Photography  68 3.3.3ASTER imagery  71 3.4  Quality assessment  73 3.4.1 Mass point quality 74 3.4.2 Quality of interpolated DEMs 74 3.4.4  Variatiation at a transect 77 3.5Discussion 78 3.6Conclusions 81 Part II Evapotranspiration modelling using remote sensing 4 Surface energy balance theory 85 4.1 Introduction 85 4.2 Global mean annual radiation and energy budget 85 4.3 The surface energy balance 89 4.4 Modelling the surface energy balance 94 4.5 The Surface Energy Balance System (SEBS) 96 4.5.1 Calculation of meteorological parameters 97 4.5.2 Submodel to derive energy balance terms 98 4.5.3 Submodel to derive stability parameters 99 4.5.4 Submodel to derive roughness length for heat transfer 101 4.5.5 Energy balance at limiting cases 103 5 Acquisition of input data for SEES from different satellite sensors and field measurements107 5.1 Introduction 107 5.2 Study area 107 5.3 Methods 110 5.3.1 Preprocessing LandsatTM5 110 5.3.2 Preprocessing ASTER 112 5.3.3 Preprocessing MODIS 114 5.3.4 Field measurement of emissivity 116 5.4 Results & Discussion 122 5.4.1 SEBS 122 5.4.2 Emissivity 122 5.5 Conclusions 125 6 Evaluation of the Surface Energy Balance System (SEES) using  ASTER imagery at the SPARC 2004 site (Barrax, Spain)127 6.1 Introduction 127 6.2 SEBS 128 6.3 Data description 128 6.3.1 Remote Sensing data 129 6.3.2 Ground data 129 6.4 Results 131 6.4.1 Model output 131 6.4.2 Model evaluation 132 6.4.3 Model sensitivity 134 6.5 Discussion 138 6.6 Conclusions 139 7 Comparison of MODIS and ASTER derived surface temperatures and energy fluxes at different resolutions 141 7.1 Introduction 141 7.2 Approach 142 7.3 Remote Sensing and Reld Data 143 7.4 Methods 144 7.4.1 SEES 144 7.4.2 UniTrad 144 7.4.3 DisTrad 144 7.4.4 Analysis methods 147 7.5 Results 149 7.5.1 Temperatures 149 7.5.2 Energy fluxes 157 7.6 Discussion 162 7.7 Conclusions 163 Part III Soil moisture modelling with a process-based model 8 Process-based soil moisture modelling using the Soil Moisture  System (SOMS) model 167 8.1 Introduction 167 8.2 Site description 168 8.2.1 Climate 168 8.2.2 Geomorphology and soils 169 8.2.3 Land cover 171 8.3 Methods 172 8.3.1 The SOMS model 172 8.3.2 Field data 180 8.3.3 Model calibration 189 8.4 Results, calibration and evaluation 190 8.4.1 Calibration 190 8.4.2 Evaluation 192 8.4.3 Spatial results 194 8.5 Discussion 197 8.6 Conclusions 198 Part IV Integration of remote sensing data in a soil moisture model 9 Error propagation in SEES and SOMS using Monte Carlo simulations 201 9.1 Introduction 201 9.2 Methods 202 9.2.1 SOMS - Data Rich Scenario 203 9.2.2 SOMS - Data Poor Scenario 213 9.2.3 SEES - Large Error scenario 214 9.2.4 SEES - Small Error scenario 217 9.3Results and discussion 217 9.3.1 SOMS - Data Rich Scenario 217 -94.2 SOMS - Data Poor Scenario 222 9.3.3 SEBS error propagation results 224 9.3.4Comparison of SOMS and SEES 229 9.4  Conclusions 231 10 Integrating remote sensing observations in soil moisture modelling by means  of a particle filter  235 10.1  Introduction 235 10.2 Methods 236 10.2.1  Particle Filter 236 10.2.2  Implementation 238 10.3  Results 239 10.3.1  Filter results for actual evapotranspiration 239 10.3.2  Filtering results for Volumetric Moisture Content (VMC) 243 10.4 Discussion 249 10.4.1  Variance versus distribution of AET 249 10.4.2  Correlation between AET and VMC 250 10.4.3  Spatial variability of VMC after filtering 256 10.4.4 Technical restrictions of the Particle Filter 256 10.5  Conclusions 257 11 Conclusions 259 11.1  Remote sensing data as input to process-based models 259 11.2  Evapotranspiration modelling using remote sensing 261 11.3  Soil moisture prediction using process-based models 264 11.4  Uncertainty analysis of SEES and SOMS 264 11.5  Integrating remote sensing derived AET in a soil moisture model by means  of a particle filter  266 11.6 Main research question 267 References 271 Summary 287 Samenvatting 291 Resume 295 Acknowledgements 305 Curriculum Vitae 308 Publications 309
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