Viscarra Rossel / McBratney / Minasny Proximal Soil Sensing

1. Auflage 2010
ISBN: 978-90-481-8859-8
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 448 Seiten

Reihe: Progress in Soil Science

This book reports on developments in Proximal Soil Sensing (PSS) and high resolution digital soil mapping. PSS has become a multidisciplinary area of study that aims to develop field-based techniques for collecting information on the soil from close by, or within, the soil. Amongst others, PSS involves the use of optical, geophysical, electrochemical, mathematical and statistical methods. This volume, suitable for undergraduate course material and postgraduate research, brings together ideas and examples from those developing and using proximal sensors and high resolution digital soil maps for applications such as precision agriculture, soil contamination, archaeology, peri-urban design and high land-value applications, where there is a particular need for high spatial resolution information. The book in particular covers soil sensor sampling, proximal soil sensor development and use, sensor calibrations, prediction methods for large data sets, applications of proximal soil sensing, and high-resolution digital soil mapping. Key themes: soil sensor sampling - soil sensor calibrations - spatial prediction methods - reflectance spectroscopy - electromagnetic induction and electrical resistivity - radar and gamma radiometrics - multi-sensor platforms - high resolution digital soil mapping - applications Raphael A. Viscarra Rossel is a scientist at the Commonwealth Scientific and Industrial Research Organisation (CSIRO) of Australia. Alex McBratney is Pro-Dean and Professor of Soil Science in the Faculty of Agriculture Food & Natural Resources at the University of Sydney in Australia. Budiman Minasny is a Senior Research Fellow in the Faculty of Agriculture Food & Natural Resources at the University of Sydney in Australia.

Autoren/Hrsg.

Minasny, Budiman

Viscarra Rossel, Raphael A.

McBratney, Alex B.

Inhaltsverzeichnis

1;Foreword;6
1.1; Proximal Soil Sensing: Looking, Touching, Feeling;6
2;Preface;8
3;Acknowledgements;10
4;Contents;11
5;About the Editors;15
6;Contributors;16
7;Part I Overview;24
7.1;1 Sampling for High-Resolution Soil Mapping;25
7.1.1;1.1 Introduction;25
7.1.2;1.2 Materials and Methods;27
7.1.2.1;1.2.1 Sensor Sampling: Some Theory;27
7.1.2.1.1;1.2.1.1 Optimisation for Equipment Type A;27
7.1.2.1.2;1.2.1.2 Optimisation for Equipment Type B;29
7.1.2.2;1.2.2 Sensor Sampling: Some Experiments;30
7.1.2.3;1.2.3 Calibration Sampling;31
7.1.2.3.1;1.2.3.1 Latin Hypercube Sampling;32
7.1.2.3.2;1.2.3.2 Sampling by Response Surface Methodology;33
7.1.2.3.3;1.2.3.3 Model-Based Sampling for Universal Kriging;33
7.1.2.3.4;1.2.3.4 Sampling by Fuzzy Cluster Analysis;33
7.1.3;1.3 Results and Discussion;35
7.1.4;1.4 Conclusions;35
7.1.5;References;36
7.2;2 Development of On-the-Go Proximal Soil Sensor Systems;37
7.2.1;2.1 Introduction;37
7.2.2;2.2 Sensor Development Review;38
7.2.2.1;2.2.1 Electrical and Electromagnetic Sensors;39
7.2.2.2;2.2.2 Optical and Radiometric Sensors;40
7.2.2.3;2.2.3 Mechanical Sensors;42
7.2.2.4;2.2.4 Acoustic and Pneumatic Sensors;44
7.2.2.5;2.2.5 Electrochemical Sensors;44
7.2.3;2.3 Sensor Applications;46
7.2.3.1;2.3.1 Multisensor Data Fusion;46
7.2.3.2;2.3.2 Sensor Deployment;48
7.2.4;2.4 Conclusions;49
7.2.5;References;49
7.3;3 Diffuse Reflectance Spectroscopy for High-Resolution Soil Sensing;51
7.3.1;3.1 Introduction;51
7.3.2;3.2 Fundamentals of Diffuse Reflectance Spectroscopy;52
7.3.3;3.3 Soil Diffuse Reflectance Spectra;53
7.3.3.1;3.3.1 Vis--NIR;53
7.3.3.2;3.3.2 Mid-IR;54
7.3.4;3.4 Mathematical Preprocessing of Spectra;55
7.3.5;3.5 Spectroscopic Multivariate Calibrations;57
7.3.6;3.6 Spectroscopic Calibrations for Predictions of Soil Properties;58
7.3.6.1;3.6.1 Visible--Near-Infrared (Vis--NIR) Calibrations;58
7.3.6.2;3.6.2 Mid-Infrared (Mid-IR) Calibrations;61
7.3.6.3;3.6.3 Generalisation and Limitations of Spectroscopic Calibrations;61
7.3.7;3.7 Proximal Soil Sensing Using Portable Spectrometers;64
7.3.8;3.8 Conclusions;65
7.3.9;References;66
7.4;4 High-Resolution Digital Soil Mapping: Kriging for Very Large Datasets;70
7.4.1;4.1 Introduction;70
7.4.2;4.2 Spatial Covariance Function;73
7.4.3;4.3 Kriging: Optimal Linear Spatial Prediction;73
7.4.4;4.4 Soil Properties on a Portion of Nowley Farm, New South Wales, Australia;76
7.4.5;4.5 Conclusions;82
7.4.6;References;83
8;Part II Soil Sensing and Sampling;85
8.1;5 The Sun Has Shone Here Antecedently;86
8.1.1;5.1 Introduction;87
8.1.2;5.2 High-Resolution Digital Soil Sensing and Mapping;87
8.1.3;5.3 The Precocious and Prescient Contribution of Haines and Keen;88
8.1.3.1;5.3.1 The Rationale: Cultivation and Soil Strength;88
8.1.3.2;5.3.2 The Sensor: A (Pre-electronic) Design for a Soil Draught Force Sensor;88
8.1.3.3;5.3.3 Data Logging: Analogue Data Recording;89
8.1.3.4;5.3.4 Data Analysis: Spatial Variation and Data Filtering;89
8.1.3.5;5.3.5 The Product: The First High-Resolution Digital Soil Map;90
8.1.4;5.4 Degrees of Separation;91
8.1.5;5.5 Conclusions;93
8.1.6;References;93
8.2;6 Proximal Soil Nutrient Sensing Using Electrochemical Sensors;95
8.2.1;6.1 Introduction;95
8.2.2;6.2 Proximal Soil Sensing Using Electrochemical Sensors;96
8.2.2.1;6.2.1 Electrochemical Sensors;96
8.2.2.2;6.2.2 Soil Nutrient Analysis Using Electrochemical Sensors;98
8.2.2.3;6.2.3 PSS: Stationary In Situ Analysis;99
8.2.2.4;6.2.4 PSS: On-the-Go;100
8.2.2.4.1;6.2.4.1 Direct Soil Measurement;100
8.2.2.4.2;6.2.4.2 Agitated Soil Measurement (ASM);101
8.2.2.4.3;6.2.4.3 Batch/Chamber-Based Methods;101
8.2.2.4.4;6.2.4.4 Flow Injection Analysis (FIA);102
8.2.2.5;6.2.5 Addressing Limitations of Electrochemical Sensors for Proximal Soil Sensing;103
8.2.3;6.3 Conclusions;104
8.2.4;References;105
8.3;7 DIGISOIL: An Integrated System of Data Collection Technologies for Mapping Soil Properties;107
8.3.1;7.1 Introduction;107
8.3.2;7.2 Objectives;108
8.3.3;7.3 Strategy and Workplan;111
8.3.4;7.4 From Soil Threats to Geophysical Properties;112
8.3.5;7.5 Conclusions;118
8.3.6;References;118
8.4;8 iSOIL: An EU Project to Integrate Geophysics, Digital Soil Mapping, and Soil Science;120
8.4.1;8.1 Introduction;121
8.4.2;8.2 General Objectives;122
8.4.3;8.3 Motivation of the Project;122
8.4.3.1;8.3.1 Development of Geophysical Techniques;123
8.4.3.2;8.3.2 Development of Geophysical Transfer Functions;123
8.4.3.3;8.3.3 Digital Soil Mapping;124
8.4.4;8.4 Structure of the Project;125
8.4.5;8.5 Conclusions;126
8.4.6;References;127
8.5;9 Conditioned Latin Hypercube Sampling for Calibrating Soil Sensor Data to Soil Properties;128
8.5.1;9.1 Introduction;128
8.5.2;9.2 Theory;130
8.5.3;9.3 Applications;132
8.5.4;9.4 Results and Discussion;134
8.5.5;References;136
8.6;10 Response Surface Sampling of Remotely Sensed Imagery for Precision Agriculture;137
8.6.1;10.1 Introduction;138
8.6.2;10.2 Material and Methods;139
8.6.2.1;10.2.1 Remote Sensing and Image Processing;139
8.6.2.2;10.2.2 Directed Sampling;139
8.6.2.3;10.2.3 Ground Sampling;140
8.6.2.4;10.2.4 Statistical Analysis and Mapping;141
8.6.3;10.3 Results and Discussion;141
8.6.4;10.4 Conclusions;144
8.6.5;References;145
9;Part III Soil UV, Visible, and Infrared Spectral Sensing;146
9.1;11 Mid- Versus Near-Infrared Spectroscopy for On-Site Analysis of Soil;147
9.1.1;11.1 Introduction;148
9.1.2;11.2 Materials and Methods;148
9.1.2.1;11.2.1 Soil Samples;148
9.1.2.2;11.2.2 Compositional Determination;149
9.1.2.3;11.2.3 Fourier Transform Spectrometer (FTS);149
9.1.2.4;11.2.4 Non-FTS NIR Spectroscopy;150
9.1.2.5;11.2.5 Chemometrics;150
9.1.3;11.3 Results and Discussion;151
9.1.4;11.4 Conclusions;155
9.1.5;References;156
9.2;12 Determination of Soil Nitrate and Organic Matter Content Using Portable, Filter-Based Mid-Infrared Spectroscopy;157
9.2.1;12.1 Introduction;158
9.2.2;12.2 Materials and Methods;159
9.2.2.1;12.2.1 FTIR/ATR Spectrometer Tests;159
9.2.2.2;12.2.2 Portable Filter-Based Spectrometer Tests;160
9.2.2.2.1;12.2.2.1 Nitrate Experiments;160
9.2.2.2.2;12.2.2.2 Organic Matter Experiments;161
9.2.3;12.3 Results and Discussion;161
9.2.3.1;12.3.1 FTIR/ATR Spectrometer Test Results;161
9.2.3.2;12.3.2 Filter-Based Spectrometer Test Results;162
9.2.3.2.1;12.3.2.1 Nitrate Results;163
9.2.3.2.2;12.3.2.2 Organic Matter Results;165
9.2.4;12.4 Conclusions;165
9.2.5;References;166
9.3;13 VNIR Spectroscopy Estimates of Within-Field Variability in Soil Properties;167
9.3.1;13.1 Introduction;168
9.3.2;13.2 Materials and Methods;169
9.3.2.1;13.2.1 Study Site and Soil Sampling;169
9.3.2.2;13.2.2 Spectral Data Acquisition;169
9.3.2.3;13.2.3 Analysis Procedures;170
9.3.3;13.3 Results and Discussion;171
9.3.3.1;13.3.1 Variability in Soil Properties;171
9.3.3.2;13.3.2 Predictive Capability of Spectral Regions;171
9.3.3.3;13.3.3 Regression Kriging;174
9.3.4;13.4 Conclusions;176
9.3.5;References;177
9.4;14 Infrared Sensors to Map Soil Carbon in Agricultural Ecosystems;178
9.4.1;14.1 Introduction;179
9.4.2;14.2 Materials and Methods;180
9.4.2.1;14.2.1 Soil Samples;180
9.4.2.2;14.2.2 Spectral Measurements;180
9.4.2.2.1;14.2.2.1 Airborne Measurements;180
9.4.2.2.2;14.2.2.2 Proximal Measurements;181
9.4.2.2.3;14.2.2.3 Laboratory Measurements;182
9.4.2.3;14.2.3 Chemometrics;182
9.4.3;14.3 Results and Discussion;183
9.4.3.1;14.3.1 Calibration and Validation;183
9.4.3.2;14.3.2 Potential for Optimising Sampling Design;186
9.4.3.3;14.3.3 Remote Sensing of Soil Carbon;186
9.4.4;14.4 Conclusions;188
9.4.5;References;189
9.5;15 Predicting Soil Carbon and Nitrogen Concentrations and Pasture Root Densities from Proximally Sensed Soil Spectral Reflectance;190
9.5.1;15.1 Introduction;191
9.5.2;15.2 Materials and Methods;191
9.5.2.1;15.2.1 Contact Probe Modification and Measurement Techniques;191
9.5.2.2;15.2.2 Site Locations and Sample Collection;192
9.5.2.3;15.2.3 Measurement of Soil Properties;193
9.5.2.4;15.2.4 Spectral Pre-processing and Data Analysis;193
9.5.2.5;15.2.5 Regression Model Accuracy;194
9.5.3;15.3 Results and Discussion;195
9.5.3.1;15.3.1 C and N Prediction of Taupo--Rotorua Allophanic, Pumice, and Tephric Recent Soil;195
9.5.3.2;15.3.2 Comparison Between H and V Method for Fluvial Recent Soil;197
9.5.3.3;15.3.3 Vertical Method on Fluvial Recent Soil Collected in Autumn (May);199
9.5.3.4;15.3.4 Is the Calibration Model Influenced by Temporal Variations in the Soil?;199
9.5.3.5;15.3.5 Root Density Prediction on Ramiha and Manawatu Soil;201
9.5.4;15.4 Conclusions;202
9.5.5;References;203
9.6;16 Diagnostic Screening of Urban Soil Contaminants Using Diffuse Reflectance Spectroscopy;204
9.6.1;16.1 Introduction;204
9.6.2;16.2 Materials and Methods;205
9.6.2.1;16.2.1 Location;205
9.6.2.2;16.2.2 Diffuse Spectral Reflectance Measurements;206
9.6.2.3;16.2.3 Statistical Analysis;206
9.6.2.4;16.2.4 Diagnostic Screening of Soil Contaminants;206
9.6.3;16.3 Results and Discussion;207
9.6.3.1;16.3.1 Exploratory Data Analysis;207
9.6.3.2;16.3.2 Spectroscopic Analysis;207
9.6.3.3;16.3.3 Diagnostic Screening Using Ordinal Logistic Regression;209
9.6.4;16.4 Conclusions;211
9.6.5;References;211
9.7;17 Using Wavelets to Analyse Proximally Sensed Vis--NIRSoil Spectra;213
9.7.1;17.1 Introduction;213
9.7.2;17.2 Materials and Methods;214
9.7.2.1;17.2.1 The Soil Spectral Library;214
9.7.2.2;17.2.2 Proximal Vis--NIR Sensing of Soil Profiles;215
9.7.2.3;17.2.3 The Wavelet Transform;216
9.7.2.4;17.2.4 Multivariate Calibrations;216
9.7.3;17.3 Results;217
9.7.3.1;17.3.1 The Soil Vis--NIR Spectral Library and Validation Samples;217
9.7.3.2;17.3.2 A Multiresolution Analysis (MRA);218
9.7.3.3;17.3.3 The Wavelet Transform for Data Compression and Multivariate Calibrations;219
9.7.3.4;17.3.4 Denoising by Back-Transforming the Wavelet Coefficients;220
9.7.4;17.4 Discussion;221
9.7.5;17.5 Conclusions;221
9.7.6;References;222
9.8;18 Mapping Soil Surface Mineralogy at Tick Hill, North-Western Queensland, Australia, Using Airborne Hyperspectral Imagery;223
9.8.1;18.1 Introduction;224
9.8.2;18.2 Tick Hill Study Area;226
9.8.3;18.3 Materials and Methods;228
9.8.3.1;18.3.1 Geoscience Mapping Data and Processing;228
9.8.3.2;18.3.2 Airborne HyMap Data Processing for Mineral Mapping;229
9.8.3.3;18.3.3 Field Samples and Related Laboratory Analyses;230
9.8.3.4;18.3.4 Field Validation of Airborne Mineral-Mapping Results;231
9.8.4;18.4 Results and Discussion;231
9.8.4.1;18.4.1 Field Samples;231
9.8.4.2;18.4.2 Airborne Versus Field Spectra;232
9.8.4.3;18.4.3 Mineral Group Abundances;232
9.8.4.4;18.4.4 Clay Mineral Abundances;235
9.8.4.5;18.4.5 Clay Mineral Physicochemistry;236
9.8.4.6;18.4.6 Other Products;237
9.8.4.7;18.4.7 Integrated Mineral Analysis;238
9.8.5;18.5 Conclusions;239
9.8.6;References;239
10;Part IV Soil Sensing by Electromagnetic Induction and Electrical Resistivity;242
10.1;19 Combining Proximal and Penetrating Soil Electrical Conductivity Sensors for High-Resolution Digital Soil Mapping;243
10.1.1;19.1 Introduction;244
10.1.2;19.2 Materials and Methods ;245
10.1.2.1;19.2.1 Soil Landscapes, Measurements, and Observations;245
10.1.2.2;19.2.2 ECa--P Measurement;245
10.1.2.3;19.2.3 ECa--M Measurement;246
10.1.2.4;19.2.4 Proximal ECa Measurement;246
10.1.3;19.3 Results and Discussion;247
10.1.3.1;19.3.1 Soil Profile ECa ;247
10.1.3.2;19.3.2 ECa--P Predicted Depth to Claypan;249
10.1.3.3;19.3.3 Calibrating ECa to ECa--P Features;249
10.1.3.4;19.3.4 Profile Sources of Proximal ECa ;251
10.1.4;19.4 Conclusions;252
10.1.5;References;253
10.2;20 A Neural Network Approach to Topsoil Clay Prediction Using an EMI-Based Soil Sensor;254
10.2.1;20.1 Introduction;255
10.2.2;20.2 Materials and Methods;255
10.2.2.1;20.2.1 Study Site and Data Collection;255
10.2.2.2;20.2.2 Neural Network Analysis;256
10.2.2.3;20.2.3 Multivariate Linear Regression;257
10.2.3;20.3 Results and Discussion;258
10.2.4;20.4 Conclusions;262
10.2.5;References;262
10.3;21 Field Determination of Soil Moisture in the Root Zone of Deep Vertosols Using EM38 Measurements: Calibration and Application Issues;264
10.3.1;21.1 Introduction;265
10.3.2;21.2 Materials and Methods;266
10.3.2.1;21.2.1 Study Area;266
10.3.2.2;21.2.2 EM38 Depth--Response Function;266
10.3.2.3;21.2.3 Field Calibration and Prediction of Average Moisture Content at Depth;267
10.3.3;21.3 Results and Discussion;268
10.3.4;21.4 Conclusions;271
10.3.5;References;272
10.4;22 Can the EM38 Probe Detect Spatial Patterns of Subsoil Compaction?;273
10.4.1;22.1 Introduction;273
10.4.2;22.2 Materials and Methods ;276
10.4.2.1;22.2.1 Measurement of Penetration Resistance (PR);276
10.4.2.2;22.2.2 Measurement of Apparent Electrical Conductivity (ECa);276
10.4.2.3;22.2.3 Study Sites;276
10.4.3;22.3 Results and Discussion;277
10.4.4;22.4 Conclusions;280
10.4.5;References;281
10.5;23 Changes in Field Soil Water Tracked by Electrical Resistivity;282
10.5.1;23.1 Introduction;283
10.5.2;23.2 Materials and Methods;283
10.5.2.1;23.2.1 Characteristics of the Soils Studied;283
10.5.2.2;23.2.2 Soil Water Content Monitoring at the Field Scale;283
10.5.2.3;23.2.3 Electrical Monitoring Over Time;284
10.5.2.4;23.2.4 Spatial and Temporal Variability Analysis;285
10.5.3;23.3 Results and Discussion;286
10.5.3.1;23.3.1 Statistical Relationship Between Electrical Resistivity and Soil Water Content;286
10.5.3.2;23.3.2 Spatial Analysis of the Experimental Data;286
10.5.3.3;23.3.3 Temporal Analysis of the Experimental Data;288
10.5.4;23.4 Conclusion;289
10.5.5;References;289
10.6;24 Is a Systematic Two-Dimensional EMI Soil Survey Always Relevant for Vineyard Production Management? A Test on Two Pedologically Contrasting Mediterranean Vineyards;290
10.6.1;24.1 Introduction;291
10.6.2;24.2 Materials and Methods;291
10.6.2.1;24.2.1 Location, Geology, and Pedology;291
10.6.2.2;24.2.2 Geophysical Surveys;292
10.6.2.3;24.2.3 Soil Survey;293
10.6.2.4;24.2.4 NDVI Maps of Vine Vigour and Map Comparison;294
10.6.3;24.3 Results;295
10.6.3.1;24.3.1 Relations Between NDVI, Soil, and ECa ;295
10.6.3.2;24.3.2 Differences Between Different ECa Measurements;296
10.6.3.2.1;24.3.2.1 Erratic Shifting with Mobile EMI;296
10.6.3.2.2;24.3.2.2 Comparison Between R--ECa and I--ECa ;297
10.6.3.3;24.3.3 Electrical Conductivity of Different Soils and Materials in the Two Blocks;298
10.6.3.4;24.3.4 Soil Type Detection with R--ECa Data;299
10.6.4;24.4 Discussion and Conclusions;301
10.6.5;References;302
11;Part V Radar and Gamma Radiometric Sensors;303
11.1;25 Full-Waveform Modelling and Inversion of Ground-Penetrating Radar Data for Non-invasive Characterisation of Soil Hydrogeophysical Properties;304
11.1.1;25.1 Introduction;305
11.1.2;25.2 Ground-Penetrating Radar;306
11.1.3;25.3 Full-Waveform Analysis of Proximal GPR Data;308
11.1.3.1;25.3.1 GPR Forward Modelling;308
11.1.3.1.1;25.3.1.1 Antenna Equation in the Frequency Domain;308
11.1.3.1.2;25.3.1.2 Zero-Offset Green's Function for Multilayered Media;309
11.1.3.2;25.3.2 Model Inversion;311
11.1.3.3;25.3.3 Model Validation and Applications;311
11.1.4;25.4 Conclusions;314
11.1.5;References;314
11.2;26 Using Proximal Sensors to Continuously Monitor Agricultural Soil Physical Conditions for Tillage Management;317
11.2.1;26.1 Introduction;318
11.2.2;26.2 Materials and Methods;318
11.2.2.1;26.2.1 Description of the Sensors;318
11.2.2.2;26.2.2 Field Experiment;320
11.2.2.3;26.2.3 Data Acquisition;320
11.2.3;26.3 Results and Discussion;321
11.2.3.1;26.3.1 Soil Physical Conditions;321
11.2.3.2;26.3.2 Radar Data;321
11.2.3.3;26.3.3 Capacitance Probe;322
11.2.3.4;26.3.4 Mechanical Resistance Probe;323
11.2.3.5;26.3.5 Tillage Effects on Seedling Emergence;323
11.2.4;26.4 Conclusions;325
11.2.5;References;325
11.3;27 Gamma Ray Sensor for Topsoil Mapping: The Mole;326
11.3.1;27.1 Introduction;326
11.3.2;27.2 Equipment and Data Analysis Methods;328
11.3.2.1;27.2.1 Hardware;328
11.3.2.2;27.2.2 Spectral Data Analysis;328
11.3.2.3;27.2.3 Fingerprinting and Soil Sampling;330
11.3.3;27.3 Applications;333
11.3.4;27.4 Future Developments;333
11.3.5;27.5 Conclusions;335
11.3.6;References;335
11.4;28 Gamma Ray Sensing for Cadmium Risk Assessment in Agricultural Soil and Grain: A Case Study in Southern Sweden;336
11.4.1;28.1 Introduction;336
11.4.2;28.2 Materials and Methods;337
11.4.3;28.3 Results and Discussion;339
11.4.4;28.4 Conclusions;344
11.4.5;References;345
11.5;29 Use of EM38 and Gamma Ray Spectrometry as Complementary Sensors for High-Resolution Soil Property Mapping;346
11.5.1;29.1 Introduction;347
11.5.2;29.2 Materials and Methods ;348
11.5.2.1;29.2.1 Location and Soil;348
11.5.2.2;29.2.2 EM38 and .-Radiometric Survey;348
11.5.2.3;29.2.3 Sensor Response and Interpretation;349
11.5.3;29.3 Results and Discussion;349
11.5.4;29.4 Conclusions;351
11.5.5;References;352
12;Part VI Multisensor Systems and Other Sensors;353
12.1;30 Field-Scale Draught Resistance and Soil Moisture Measurement in Australia Using a Tine-Based ForceCapacitance Sensing System;354
12.1.1;30.1 Introduction;354
12.1.2;30.2 Materials and Methods ;356
12.1.3;30.3 Results and Discussion;358
12.1.3.1;30.3.1 Transect;358
12.1.3.2;30.3.2 Whole Paddock;361
12.1.4;30.4 Conclusions;362
12.1.5;References;363
12.2;31 Sensor-Based Mapping of Soil Quality on Degraded Claypan Landscapes of the Central United States;364
12.2.1;31.1 Introduction;364
12.2.2;31.2 Materials and Methods ;365
12.2.2.1;31.2.1 Soil ECa ;366
12.2.2.2;31.2.2 Yield Mapping;366
12.2.2.3;31.2.3 Claypan Hydraulic Properties;367
12.2.2.4;31.2.4 Soil Compaction;367
12.2.3;31.3 Results and Discussion;367
12.2.3.1;31.3.1 Claypan Topsoil Depth;367
12.2.3.2;31.3.2 Claypan Hydraulic Properties;369
12.2.3.3;31.3.3 Soil Organic Carbon;370
12.2.3.4;31.3.4 Nutrients;371
12.2.3.5;31.3.5 Soil Compaction;372
12.2.4;31.4 Conclusions;374
12.2.5;References;374
12.3;32 Proximal Sensing Methods for Mapping Soil Water Status in an Irrigated Maize Field;375
12.3.1;32.1 Introduction;376
12.3.2;32.2 Materials and Methods ;377
12.3.2.1;32.2.1 Study Site;377
12.3.2.2;32.2.2 Electromagnetic Induction Mapping and Soil AWC;378
12.3.2.3;32.2.3 Soil Moisture Measurement;379
12.3.2.3.1;32.2.3.1 Time Domain Reflectometry (TDR);379
12.3.2.3.2;32.2.3.2 Collection of Vis--NIR Soil Reflectance Spectra;379
12.3.2.3.3;32.2.3.3 Spectral Data Pre-processing;380
12.3.3;32.3 Results and Discussion;380
12.3.3.1;32.3.1 Electromagnetic Induction Mapping and Soil AWC;380
12.3.3.2;32.3.2 Soil Moisture Measurements;382
12.3.3.3;32.3.3 Vis--NIR Soil Reflectance Spectra;382
12.3.4;32.4 Conclusions;384
12.3.5;References;384
12.4;33 Comparing the Ability of Multiple Soil Sensors to Predict Soil Properties in a Scottish Potato Production System;386
12.4.1;33.1 Introduction;386
12.4.2;33.2 Materials and Methods;388
12.4.2.1;33.2.1 On-the-Go Soil Survey;388
12.4.2.2;33.2.2 Manual Soil Sampling;388
12.4.2.3;33.2.3 Multivariate Data Analysis;389
12.4.3;33.3 Results and Discussion;389
12.4.3.1;33.3.1 Comparison of the Usefulness of Individual Sensors;389
12.4.3.2;33.3.2 Multi-sensors vs. Single Sensor;393
12.4.3.3;33.3.3 Discussion;393
12.4.3.4;33.3.4 Other Considerations;394
12.4.4;33.4 Conclusions;394
12.4.5;References;395
12.5;34 Spatial Variability and Pattern of Selected Properties of Agricultural Soils in the Czech Republic Measured by Indirect Proximal and Remote Sensing;396
12.5.1;34.1 Introduction;396
12.5.2;34.2 Materials and Methods ;397
12.5.2.1;34.2.1 Experimental Field Description;397
12.5.2.2;34.2.2 Soil Sampling and Soil Property Determination;397
12.5.2.3;34.2.3 Proximal and Remote Measurement Methods;397
12.5.2.4;34.2.4 Data Evaluation, Statistical, and Geostatistical Analyses;398
12.5.3;34.3 Results and Discussion;398
12.5.3.1;34.3.1 Geostatistical Analysis;399
12.5.3.2;34.3.2 Discussion;404
12.5.4;34.4 Conclusions;407
12.5.5;References;408
13;Part VII Applications;409
13.1;35 Inverse Meta-modelling of Yield-Monitor Data for Estimating Soil-Available Water-Holding Capacities at a Farm Resolution of 10 m;410
13.1.1;35.1 Introduction;411
13.1.2;35.2 Materials and Methods ;412
13.1.2.1;35.2.1 Key Assumptions;412
13.1.2.2;35.2.2 Study Site and Available Data;413
13.1.2.3;35.2.3 Creating a Meta-model;413
13.1.2.4;35.2.4 Estimating 'Effective' Hydraulic Properties;414
13.1.2.5;35.2.5 Validating 'Effective' AWCs;414
13.1.3;35.3 Results and Discussion;414
13.1.4;35.4 Conclusions;417
13.1.5;References;418
13.2;36 Reconstructing Palaeotopography at the Beginning of the Weichselian Glacial Stage Using an Electromagnetic Induction Sensor;419
13.2.1;36.1 Introduction;420
13.2.2;36.2 Materials and Methods;420
13.2.2.1;36.2.1 Study Site;420
13.2.2.2;36.2.2 Electromagnetic Induction Sensing;420
13.2.2.3;36.2.3 Mobile ECa Measurement Equipment and ECa Mapping;421
13.2.2.4;36.2.4 Depth to Tertiary Clay Observations;422
13.2.2.5;36.2.5 Relationship Between ECa-V and Depth to Tertiary Clay;422
13.2.2.6;36.2.6 Relationship Between the Combined ECa-V and ECa-H and Depth to Tertiary Clay;425
13.2.3;36.3 Results and Discussion;426
13.2.3.1;36.3.1 Relationship Between ECa-V and Depth to Tertiary Clay;426
13.2.3.2;36.3.2 Relationship Between the Combined ECa-V and ECa-H and Depth to Tertiary Clay;427
13.2.3.3;36.3.3 Validation of Predicted Depth of Tertiary Clay;427
13.2.3.4;36.3.4 Palaeotopography Beneath the Loess Cover;428
13.2.4;36.4 Conclusions;429
13.2.5;References;430
13.3;Postscript: Where to from Here?;431
13.3.1; Soil Sensing and Sampling;431
13.3.1.1; Future Work;431
13.3.2; Soil UV, Visible, and Infrared Spectral Sensing;431
13.3.2.1; Future Work;432
13.3.3; Soil Sensing by Electromagnetic Induction and Electrical Resistivity;432
13.3.3.1; Future Work;432
13.3.4; Radar and Gamma Radiometric Sensors;433
13.3.4.1; Future Work;433
13.3.5; Multi-sensor Systems and Other Sensors;433
13.3.5.1; Future Work;433
13.3.6; Applications;433
13.3.6.1; Future Work;434
13.3.7; Initiatives;434
13.3.7.1; FP7 Projects iSoil and Digisoil;434
13.3.7.2; IUSS WG-PSS;434
13.3.7.3; Global Soil Spectral Library;434
14;Index;436