# Applying the diagnostic approach for the definition of soil functions – a pilot example on carbon sequestration and storage

Erika Micheli, Rachel E. Creamer, Adam Csorba, Sébastien Drufin, Peter Vadnai, Endre Dobos

► To cite this version:

Erika Micheli, Rachel E. Creamer, Adam Csorba, Sébastien Drufin, Peter Vadnai, et al. Applying the diagnostic approach for the definition of soil functions – a pilot example on carbon sequestration and storage. Pedometrics 2017, Jun 2017, Wageningen, Netherlands. 298 p. hal-01608034

HAL Id: hal-01608034

https://hal.science/hal-01608034

Submitted on 2 Jun 2020

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ΠΕΔΟ
METRICS

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WAGENINGEN 2017
# Contents

<table><tr><td>Evaluating Use of Ground Penetrating Radar and Geostatistic Methods for Mapping Soil Cemented Horizon</td><td>13</td></tr><tr><td>Digital soil mapping in areas of mussunungas: algoritmos comparission</td><td>14</td></tr><tr><td>Sensing of farm and district-scale soil moisture content using a mobile cosmic ray probe (COSMOS Rover)</td><td>15</td></tr><tr><td>Proximal sensing of soil crack networks using three-dimensional electrical resistivity tomography</td><td>16</td></tr><tr><td>Using digital microscopy for rapid determination of soil texture and prediction of soil organic matter</td><td>17</td></tr><tr><td>Analysis of complementarities of different spectral analytics to sense soil properties</td><td>18</td></tr><tr><td>Long-term diachronic series for soil carbon saturation evidence. A case study on volcanic soils of reunion island under sugarcane crops.</td><td>19</td></tr><tr><td>Concept of entropy in spatial distribution of vegetation in satellite images</td><td>20</td></tr><tr><td>Digital Soil Mapping Method Based on the Similarity of Environmental Covariates in the Spatial Neighborhood</td><td>21</td></tr><tr><td>Multivariate and multi-layer soil mapping using structural equation modelling</td><td>22</td></tr><tr><td>Incorporating infrared spectroscopic data, land management, soil drainage and soil erosion observations into Bayesian framework for modelling soil erosion risk</td><td>23</td></tr><tr><td>Comparing airborne and terrestrial laser scanning DTMs for high resolution topsoil pH modelling</td><td>24</td></tr><tr><td>Multi-sensor data fusion for supervised land-cover classification through a Bayesian setting coupling multivariate smooth kernel for density estimation and geostatistical techniques</td><td>25</td></tr><tr><td>Uncertainty in soil properties from the hydrological point of view: a call for new types of soil maps?</td><td>26</td></tr><tr><td>Detecting soil microbial community shifts via field spectroscopy</td><td>27</td></tr><tr><td>Using Near Infrared Spectroscopy in determining the mineralogical variations of the London Clay Formation, Whitecliff Bay, Isle of White, UK.</td><td>28</td></tr><tr><td>Standardization of world soil profile data to support global mapping and modelling</td><td>29</td></tr><tr><td>Soil and Environment software, a tool for soil management</td><td>30</td></tr><tr><td>App Soil Calculator</td><td>31</td></tr><tr><td>Algorithms for quantitative pedology</td><td>32</td></tr><tr><td>Saskatchewan Soils: Access and improvements to soil information</td><td>33</td></tr><tr><td>Soil Spectral Library of Ethiopia (SSL-ETH), Version-I</td><td>34</td></tr><tr><td>Detailed predictive mapping of acid sulfate soil occurrence using electromagnetic induction data</td><td>35</td></tr><tr><td>Supplementing predictive mapping of acid sulfate soil occurrence with Vis-NIR spectroscopy</td><td>37</td></tr><tr><td>3D prediction of soil moisture using data from varying horizontal and vertical supports</td><td>38</td></tr><tr><td>Joint multifractal analysis of the influence of topography and soil texture on soil water storage</td><td>39</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>Multitemporal Soil Pattern Analysis for Organic Matter Estimation on Arable Fields using Multispectral Satellite Data</td>
      <td>40</td>
    </tr>
    <tr>
      <td>Spectral mixing for vis-NIR diffuse reflectance spectroscopy</td>
      <td>41</td>
    </tr>
    <tr>
      <td>Predictive mapping of the acidifying potential for acid sulfate soils</td>
      <td>42</td>
    </tr>
    <tr>
      <td>Can soil spatial prediction models from different areas be similar?</td>
      <td>44</td>
    </tr>
    <tr>
      <td>Thermal remote sensing for digital soil mapping</td>
      <td>45</td>
    </tr>
    <tr>
      <td>Optimal stratification for validation of digital soil maps</td>
      <td>46</td>
    </tr>
    <tr>
      <td>Spatial modeling of geomorphometric variables for natural hazard valuation to desertification in tropical zones</td>
      <td>47</td>
    </tr>
    <tr>
      <td>Use of drone high resolution images to quantify soil erosion</td>
      <td>48</td>
    </tr>
    <tr>
      <td>Predicting Scottish soil properties using X-ray powder diffraction</td>
      <td>49</td>
    </tr>
    <tr>
      <td>Digital Soil Mapping of soil properties across GB: case studies from Scotland and England</td>
      <td>50</td>
    </tr>
    <tr>
      <td>A routine chemometrics approach to estimate soil organic carbon in croplands exploiting LUCAS topsoil database.</td>
      <td>51</td>
    </tr>
    <tr>
      <td>Integration of GPR measurements with sparse textural data for characterizing forest soils: an application of data fusion in southern Italy (Calabria)</td>
      <td>52</td>
    </tr>
    <tr>
      <td>Determination of naturally occurring concentrations of trace elements in New Zealand soils</td>
      <td>53</td>
    </tr>
    <tr>
      <td>Mapping spatial variability of soil organic carbon, phosphorus and soil acidity in Zambia</td>
      <td>54</td>
    </tr>
    <tr>
      <td>A Method Research on Digital Soil Mapping Using ES-RS-GIS in Semi-arid Sandy Land: A Case Study of Horqin Left Back Banner</td>
      <td>55</td>
    </tr>
    <tr>
      <td>Soil classification of multi-horizontal profiles using support vector machines and vis-NIR spectroscopy</td>
      <td>56</td>
    </tr>
    <tr>
      <td>Using new sparsity genomic methods to improve soil chemometric models</td>
      <td>57</td>
    </tr>
    <tr>
      <td>Transferring and spiking of soil spectral models between two south Indian villages</td>
      <td>58</td>
    </tr>
    <tr>
      <td>Mapping the Impact of Zero Tillage on the Biophysical Properties of Soil</td>
      <td>59</td>
    </tr>
    <tr>
      <td>Analysis of total carbon in soils from Itatiaia National Park: relationship with profile attributes and terrain covariates</td>
      <td>60</td>
    </tr>
    <tr>
      <td>Proximal sensing of soil surface properties in relation to crusting, and rainfall-runoff processes: from portable to UAV-based platforms</td>
      <td>61</td>
    </tr>
    <tr>
      <td>The spatial variability of soil's plant-available water capacity, and its implications for site-specific management</td>
      <td>62</td>
    </tr>
    <tr>
      <td>Evaluating recent and sub-recent magnetic impact records of air pollution by combined soil and bio-magnetic monitoring</td>
      <td>63</td>
    </tr>
    <tr>
      <td>Ecosystem services provided by groundwater dependent wetlands in karst areas: carbon storage and sequestration</td>
      <td>64</td>
    </tr>
    <tr>
      <td>Spatial explicit prediction of soil organic matter using a hybrid model composed of random forest and ordinary kriging</td>
      <td>65</td>
    </tr>
    <tr>
      <td>Identifying soil management zones in a sugarcane field using proximal sensed electromagnetic induction and gamma-ray spectrometry data</td>
      <td>66</td>
    </tr>
    <tr>
      <td>Evaluating the potential of simulated soil clay content by SoilGen2 model as soft data in Regression Kriging in sparsely sampled areas</td>
      <td>67</td>
    </tr>
    <tr>
      <td>Orthogonalisation and standardisation as alternatives to improve predictions of soil properties and lime requirement using on-the-go Vis-NIR-SWIR spectroscopy</td>
      <td>68</td>
    </tr>
    <tr>
      <td>Use of GPR in evaluation of iron ore tailings deposition characteristics in the River Doce Basin - Brazil</td>
      <td>69</td>
    </tr>
    <tr>
      <td>A graphical user interface in R to perform preprocessing, multivariate modeling and prediction using spectroscopic data</td>
      <td>70</td>
    </tr>
    <tr>
      <td>Rapid detection of alkanes and polycyclic aromatic hydrocarbons (PAH) in oil-contaminated soils using visible near-infrared spectroscopy and chemometrics</td>
      <td>71</td>
    </tr>
    <tr>
      <td>A combination of soil sensors provides useful and efficient landscape genesis information for archaeological prospection</td>
      <td>72</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>Assessment of soil ecosystem services at landscape scale by direct soil monitoring and modelling.</td>
      <td>73</td>
    </tr>
    <tr>
      <td>Measuring functional pedodiversity using spectroscopic information</td>
      <td>74</td>
    </tr>
    <tr>
      <td>Slakes: A soil aggregate stability android application</td>
      <td>75</td>
    </tr>
    <tr>
      <td>Soil NIR-spectra and high-resolution satellite images to monitor the characteristics of active layer most related to permafrost thermal behaviour, Crater Lake CALM site, Deception Island, Marine Antarctica.</td>
      <td>76</td>
    </tr>
    <tr>
      <td>Large scale modelling of soil organic matter using DTM variables and geographically weighted regression</td>
      <td>77</td>
    </tr>
    <tr>
      <td>Laser scanner technologies to monitoring mountain peatlands recovering</td>
      <td>78</td>
    </tr>
    <tr>
      <td>Using a Portable XRF for Classifying Volcanic Paddy Soils of West Sumatra, Indonesia</td>
      <td>79</td>
    </tr>
    <tr>
      <td>Quantifying the uncertainty in a model reconstruction of a soilscape for archaeological land evaluation</td>
      <td>80</td>
    </tr>
    <tr>
      <td>Soil hydrological classification mapping in Scotland using DSM and Random Forests</td>
      <td>81</td>
    </tr>
    <tr>
      <td>GIS-based multivariate predictive models for gully erosion susceptibility mapping in calcareous soils</td>
      <td>82</td>
    </tr>
    <tr>
      <td>The I4S approach to site-specific soil fertility management based on proximal soil sensing</td>
      <td>83</td>
    </tr>
    <tr>
      <td>Effects of Measurement Protocols and Data Mining Techniques on Soil Proxy Model Extraction: A Czech Case Study</td>
      <td>85</td>
    </tr>
    <tr>
      <td>Soil organic carbon stocks prediction in Brazil.</td>
      <td>86</td>
    </tr>
    <tr>
      <td>Variation of soil property depth functions</td>
      <td>87</td>
    </tr>
    <tr>
      <td>Raster sampling of three soil profiles from Wisconsin, USA</td>
      <td>88</td>
    </tr>
    <tr>
      <td>High resolution modelling of soil organic carbon in West Greenland.</td>
      <td>89</td>
    </tr>
    <tr>
      <td>Past, present and future of physical, chemical and biological process knowledge in pedometrics.</td>
      <td>90</td>
    </tr>
    <tr>
      <td>Organic carbon in Swiss cropland soils 1985-2014</td>
      <td>91</td>
    </tr>
    <tr>
      <td>Seeing inside a pedologists head: are machine learning algorithms landscape specific?</td>
      <td>92</td>
    </tr>
    <tr>
      <td>The Pedon is at the Core of Digital Soil Morphometrics</td>
      <td>93</td>
    </tr>
    <tr>
      <td>Validation of the 250m Soil Grids in Canada</td>
      <td>94</td>
    </tr>
    <tr>
      <td>How universally is soil carbon increasing in New Zealand's hill country?</td>
      <td>95</td>
    </tr>
    <tr>
      <td>Soil texture estimation via mobile gamma-spectrometry: advanced evaluation using support vector machines</td>
      <td>96</td>
    </tr>
    <tr>
      <td>The power of Random Forest for the identification and quantification of technogenic substrates in urban soils on the basis of DRIFT spectra</td>
      <td>97</td>
    </tr>
    <tr>
      <td>Mapping the patterns of organic matter decomposition in a high mountain environment</td>
      <td>98</td>
    </tr>
    <tr>
      <td>End of kriging? Or how tree-based Machine Learning Algorithms can be used to generate more accurate spatial predictions with combined geographical and feature space covariates</td>
      <td>99</td>
    </tr>
    <tr>
      <td>Hyperspectral Imaging of Soil Cores</td>
      <td>100</td>
    </tr>
    <tr>
      <td>High resolution estimation of peat depth using electromagnetic induction in a Scottish peatland</td>
      <td>101</td>
    </tr>
    <tr>
      <td>3-D geostatistical interpolation of soil organic matter in the Netherlands</td>
      <td>102</td>
    </tr>
    <tr>
      <td>High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: a Comparison of Machine Learning and Multiple Linear Regression Models</td>
      <td>103</td>
    </tr>
    <tr>
      <td>Soil water nowcasting in 2-d and 3-d using electromagnetic conductivity imaging and the ensemble Kalman filter</td>
      <td>104</td>
    </tr>
    <tr>
      <td>Sequential creation of a generic soil classification system using taxa from existing systems</td>
      <td>105</td>
    </tr>
    <tr>
      <td>Developing desktop, on-line, and mobile applications to support soil experts and soil data end-users</td>
      <td>106</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>Mapping the Suitable Sites for Rice Production Using Analytical Hierarchy Process and Geographical Information System</td>
      <td>107</td>
    </tr>
    <tr>
      <td>Soil salinity assessment through novel application of satellite thermography</td>
      <td>108</td>
    </tr>
    <tr>
      <td>The German National Soil Inventory - Soil sampling for climate change abatement</td>
      <td>109</td>
    </tr>
    <tr>
      <td>Prediction of soil organic carbon fractions using near infrared reflectance spectroscopy</td>
      <td>110</td>
    </tr>
    <tr>
      <td>Developing Combined Soil-climate Indices for Crop Suitability Recommendations</td>
      <td>111</td>
    </tr>
    <tr>
      <td>Comparison of multinomial logistic regression and random forest classifiers in digital mapping of soil in western Haiti</td>
      <td>112</td>
    </tr>
    <tr>
      <td>Pre-processing of on-the-go mapping data</td>
      <td>113</td>
    </tr>
    <tr>
      <td>The use of proximal soil sensor data fusion and digital soil mapping for precision agriculture</td>
      <td>114</td>
    </tr>
    <tr>
      <td>Effect of different soil compaction levels on prediction of soil properties using MIR spectra in situ</td>
      <td>115</td>
    </tr>
    <tr>
      <td>Can organic carbon in soil cores be predicted by VNIR and MIR techniques in alpine landscape?</td>
      <td>116</td>
    </tr>
    <tr>
      <td>Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution</td>
      <td>117</td>
    </tr>
    <tr>
      <td>Validation of the coarse-scale remotely sensed soil moisture data by using ground measurements with a hybrid geostatistical downscaling method</td>
      <td>118</td>
    </tr>
    <tr>
      <td>Monitoring soil heavy metal distribution over three different time periods using trivariate linear mixed models</td>
      <td>119</td>
    </tr>
    <tr>
      <td>Estimating soil profile attributes with proximal sensors and a spectral inference system</td>
      <td>120</td>
    </tr>
    <tr>
      <td>Spatial modelling of soil carbon in Sri Lanka using sparse datasets with samples collected with different depth supports</td>
      <td>121</td>
    </tr>
    <tr>
      <td>The Effect of Topography on Spatial Variation in Soil Health</td>
      <td>122</td>
    </tr>
    <tr>
      <td>Application of colorimetric analysis of soils using flatbed scanners</td>
      <td>123</td>
    </tr>
    <tr>
      <td>Gamma radiometric mapping of soil texture at field and regional scale</td>
      <td>124</td>
    </tr>
    <tr>
      <td>Assessment of Uncertainty on a Digital Soil Map: a sensitivity analysis on the uncertainty indicators</td>
      <td>125</td>
    </tr>
    <tr>
      <td>Predicting and Mapping Total Si over the main territory of France</td>
      <td>126</td>
    </tr>
    <tr>
      <td>Comparison of methods to fill data gaps in soil profile databases</td>
      <td>127</td>
    </tr>
    <tr>
      <td>Optimizing spatial sampling for multiple objectives</td>
      <td>128</td>
    </tr>
    <tr>
      <td>Past, present and future of mathematical methods in pedometrics</td>
      <td>129</td>
    </tr>
    <tr>
      <td>Mapping root depth soil water in sub-Saharan Africa</td>
      <td>130</td>
    </tr>
    <tr>
      <td>Building a national (german) mid infrared database for soils</td>
      <td>131</td>
    </tr>
    <tr>
      <td>Have extractable phosphorus and potassium contents evolved in French agricultural soils since 2004?</td>
      <td>132</td>
    </tr>
    <tr>
      <td>Modelling the electrical conductivity of soil in the Yangtze delta in three dimensions</td>
      <td>133</td>
    </tr>
    <tr>
      <td>Sensing of soil organic carbon with portable spectrometers</td>
      <td>134</td>
    </tr>
    <tr>
      <td>How does particle size, water and excitation time affect proximal soil sensing by X-ray fluorescence?</td>
      <td>135</td>
    </tr>
    <tr>
      <td>Fine spatial resolution mapping of soil organic carbon in China</td>
      <td>136</td>
    </tr>
    <tr>
      <td>The anatomy of errors from soil property measurements and predictions (soil pH example)</td>
      <td>137</td>
    </tr>
    <tr>
      <td>Can conditioned latin hypercube sampling capture pedodiversity?</td>
      <td>138</td>
    </tr>
    <tr>
      <td>Spatial variations and controlling factors for estimation of soil organic carbon stocks in three rural soil areas of Taoyuan, Taiwan</td>
      <td>139</td>
    </tr>
    <tr>
      <td>Secondary soil information extraction from 3D soil mapping products</td>
      <td>140</td>
    </tr>
    <tr>
      <td>Estimation of surface soil moisture from remotely sensed data using random forests</td>
      <td>141</td>
    </tr>
    <tr>
      <td>Can large spectral libraries improve local calibrations of soil organic carbon?</td>
      <td>142</td>
    </tr>
    <tr>
      <td>Predicting the spatial pattern of soil particle-size distribution using a pedogenesis model</td>
      <td>143</td>
    </tr>
    <tr>
      <td>Soil Properties Prediction Using Diffuse Reflectance Spectroscopy, Attenuated Total Reflectance Spectroscopy and Fourier Transform Infrared Photoacoustic Spectroscopy</td>
      <td>144</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>Managing quantifiable uncertainty for digital land suitability assessments</td>
      <td>145</td>
    </tr>
    <tr>
      <td>Useful applications of conditioned Latin hypercube sample for digital soil mapping</td>
      <td>146</td>
    </tr>
    <tr>
      <td>Comparison of Multifractal parameters between binary and grayscale synthetic images</td>
      <td>147</td>
    </tr>
    <tr>
      <td>Development of a stakeholder-oriented communication strategy for raising acceptance of soil protection measures</td>
      <td>148</td>
    </tr>
    <tr>
      <td>Crossing the bridge between soil ecology and pedometrics at global scale</td>
      <td>149</td>
    </tr>
    <tr>
      <td>Contribution to the study of erosive dynamics in the Ghézala dam watershed (Northern Tunisia)</td>
      <td>150</td>
    </tr>
    <tr>
      <td>Pedometrics Quadrancentennial</td>
      <td>151</td>
    </tr>
    <tr>
      <td>Improvements in spatial soil sample design efficiency</td>
      <td>152</td>
    </tr>
    <tr>
      <td>Digital soil mapping in hilly relief area in Southeastern Brazil</td>
      <td>153</td>
    </tr>
    <tr>
      <td>Spatial data infrastructures for handling soil data</td>
      <td>154</td>
    </tr>
    <tr>
      <td>SoilML data exchange format and soil web services</td>
      <td>155</td>
    </tr>
    <tr>
      <td>Uncertainty and results stability of three digital soil mapping algorithms applied to the soil cover of a farm situated on the north of Udmurt Republic, Russian Federation</td>
      <td>156</td>
    </tr>
    <tr>
      <td>Applying the diagnostic approach for the definition of soil functions – a pilot example on carbon sequestration and storage</td>
      <td>157</td>
    </tr>
    <tr>
      <td>Information assessment of uncertainty of the soil’s isomorphism in pedons and elementary soil areas</td>
      <td>158</td>
    </tr>
    <tr>
      <td>A Novel Pedometrics-econometrics Approach to Assess Soil Carbon Capability</td>
      <td>159</td>
    </tr>
    <tr>
      <td>Modelling Pedo-Econometric carbon scores with VNIR spectroscopy</td>
      <td>160</td>
    </tr>
    <tr>
      <td>Predicting artificially drained areas by means of a selective model ensemble</td>
      <td>161</td>
    </tr>
    <tr>
      <td>Soil map disaggregation improved by soil-landscape relationships, area-proportional sampling and random forest implementation</td>
      <td>162</td>
    </tr>
    <tr>
      <td>Digital Soil mapping Based on Airborne Gamma-Ray Imagery and Fuzzy logic : a Case of Upper Pasak Watershed, Thailand</td>
      <td>164</td>
    </tr>
    <tr>
      <td>A 2D multifractal analysis based on detrended fluctuation analysis applied to El Pardo landscape</td>
      <td>165</td>
    </tr>
    <tr>
      <td>Implementing Pedometrics Outside the Discipline: Context, Translation and Scalability</td>
      <td>166</td>
    </tr>
    <tr>
      <td>Soil-landscape controls on the impact of extreme warm and dry events on terrestrial ecosystems within continental Europe and the Mediterranean Basin</td>
      <td>167</td>
    </tr>
    <tr>
      <td>Pedometric methods to optimize sampling and improve classification on a pilot site in the Mount Kenya region</td>
      <td>168</td>
    </tr>
    <tr>
      <td>Comparison between random forest and partial least square regression of on-line vis-NIR spectroscopy measurements of soil total nitrogen and organic carbon</td>
      <td>169</td>
    </tr>
    <tr>
      <td>Geochemical Signatures of Pristine Volcanic Ash and Soils from Krakatau as revealed by a Portable XRF spectrometer</td>
      <td>170</td>
    </tr>
    <tr>
      <td>Challenges in using mid-infrared spectroscopy for the determination of soil physical, chemical, and biochemical properties on undisturbed soil samples</td>
      <td>171</td>
    </tr>
    <tr>
      <td>Rapid sensing of petroleum-contaminated soils with mid infrared spectrometers</td>
      <td>172</td>
    </tr>
    <tr>
      <td>Predicting and Mapping Topsoil Black carbon of France</td>
      <td>173</td>
    </tr>
    <tr>
      <td>Using digital soil morphometrics to study landscape – vegetation pattern relations</td>
      <td>174</td>
    </tr>
    <tr>
      <td>Prediction of mineralizable nitrogen (N) in soils using ensembles of regression models</td>
      <td>175</td>
    </tr>
    <tr>
      <td>Insights in the possibilities of an electromagnetic induction sensor to map the military remains, buried in the former World War 1 front zone</td>
      <td>176</td>
    </tr>
    <tr>
      <td>Comprehensive evaluation of statistical approaches for digital soil mapping with large sets of environmental covariates</td>
      <td>177</td>
    </tr>
    <tr>
      <td>Effect of variable manure rate applications on grass yields and implementation of knowledge about soil conditions</td>
      <td>178</td>
    </tr>
    <tr>
      <td>Spatial prediction model applied to digital soil mapping of the Marinheiro stream watershed, Sete Lagoas (Brazil)</td>
      <td>179</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>Prediction of Soil Carbon Stock in Oxisols of the Eastern Plains in Colombia by VNIR Spectroscopy</td>
      <td>180</td>
    </tr>
    <tr>
      <td>Estimating regression parameters in the presence of spatial and temporal correlation: A case study to quantify costs of soil constraints to the Australian grains industry</td>
      <td>181</td>
    </tr>
    <tr>
      <td>DSM online service: from the soil to the cloud</td>
      <td>182</td>
    </tr>
    <tr>
      <td>Predicting soil organic carbon in Ap horizons in Sistan region, eastern Iran</td>
      <td>183</td>
    </tr>
    <tr>
      <td>Digital mapping of soil salinity in eastern Iran</td>
      <td>184</td>
    </tr>
    <tr>
      <td>Accounting for the measurement error to improve the accuracy of spatial modelling of soil carbon</td>
      <td>185</td>
    </tr>
    <tr>
      <td>Monitoring of salt content in soil profile by hyper spectral imaging spectroscopy</td>
      <td>186</td>
    </tr>
    <tr>
      <td>DSM based renewal of the Hungarian Soil Spatial Data Infrastructure</td>
      <td>187</td>
    </tr>
    <tr>
      <td>Soil, scale dependence and spatial variability: A new approach for assessing how soil variability changes with scale.</td>
      <td>189</td>
    </tr>
    <tr>
      <td>Comparison of Mid and Near Infrared Spectroscopy for Prediction of Soil Properties for a National Spatial Dataset</td>
      <td>190</td>
    </tr>
    <tr>
      <td>Combining inventory data with ancillary datasets to predict forest soil organic carbon</td>
      <td>191</td>
    </tr>
    <tr>
      <td>The Interactive Digital Soil Map of Sweden - a free web application for downscaling</td>
      <td>192</td>
    </tr>
    <tr>
      <td>Soil Microbial Diversity Across Different Agroecological Zones in New South Wales (NSW)</td>
      <td>193</td>
    </tr>
    <tr>
      <td>Spatial modelling of landscape heterogeneity in soil moisture content with the assimilation of optical and radar remote sensing data</td>
      <td>194</td>
    </tr>
    <tr>
      <td>Using in situ Vis-NIR combined with other sensing data to map clay content, soil organic carbon, and bulk density at the field scale</td>
      <td>195</td>
    </tr>
    <tr>
      <td>Geophysical mapping of wetlands using DUALEM - challenges and possibilities</td>
      <td>196</td>
    </tr>
    <tr>
      <td>Soil agrochemical monitoring - source for country-scale predictions and fertilization optimization</td>
      <td>197</td>
    </tr>
    <tr>
      <td>Digital soil mapping with Soil Land Inference Model (SoLIM) considering the spatial distance to soil samples</td>
      <td>198</td>
    </tr>
    <tr>
      <td>High resolution land-use classification toward more accurate digital soil mapping of malagasy soils.</td>
      <td>199</td>
    </tr>
    <tr>
      <td>Are data collected to support farm management suitable for monitoring soil indicators at the national scale?</td>
      <td>200</td>
    </tr>
    <tr>
      <td>Using combined model for soil pollution spatial analysis</td>
      <td>201</td>
    </tr>
    <tr>
      <td>Three-dimensional mapping of soil organic carbon (SOC) based on multi-scale digital terrain analysis and data mining in Jiangxi Province, PR China.</td>
      <td>202</td>
    </tr>
    <tr>
      <td>Soil Organic Carbon Content of Central Chile and Its Projection Under Climate Change Scenarios</td>
      <td>203</td>
    </tr>
    <tr>
      <td>Laser-induced breakdown spectroscopy (LIBS) for efficient quantitative determination of elemental plant nutrients in soils: A contribution to precision agriculture.</td>
      <td>204</td>
    </tr>
    <tr>
      <td>The use of Self Organizing Maps in hydrological modeling with SWAT</td>
      <td>205</td>
    </tr>
    <tr>
      <td>Spectroscopy and remote sensing for assessment of peatland degradation</td>
      <td>206</td>
    </tr>
    <tr>
      <td>Digital soil mapping of available water capacity for metropolitan France</td>
      <td>207</td>
    </tr>
    <tr>
      <td>Analyzing the effect of heavy metals' contamination on ecosystem services of urban soils in different functional zones of Moscow-city</td>
      <td>208</td>
    </tr>
    <tr>
      <td>A new look at soil phenofoms - definition, identification, mapping</td>
      <td>209</td>
    </tr>
    <tr>
      <td>Past, present and future of information technology in pedometrics</td>
      <td>210</td>
    </tr>
    <tr>
      <td>Mapping soilscapes using soil co-occurence networks</td>
      <td>211</td>
    </tr>
    <tr>
      <td>Approximation on the reconstruction of pore spaces in 3D CT soil images by cubic interpolation</td>
      <td>212</td>
    </tr>
    <tr>
      <td>Designing soil monitoring schemes for large areas based on high resolution digital soil mapping products: a case study from France</td>
      <td>213</td>
    </tr>
    <tr>
      <td>The use of Zipf's -Pareto law to describe the structure of soil cover of Bryansk Opolje</td>
      <td>214</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>Stacked generalization of statistical learners - a case study with soil iron content in Brazil 'spup' - an R package for uncertainty propagation analysis in spatial environmental modelling</td>
      <td>215</td>
    </tr>
    <tr>
      <td>Reflectance spectra and Land Surface Temperature integration obtained from Landsat on the soil granulometric quantification</td>
      <td>216</td>
    </tr>
    <tr>
      <td>Visualizing Soil Landscapes</td>
      <td>217</td>
    </tr>
    <tr>
      <td>Detection of soil microbial dynamics with high spatial and temporal resolution using infrared thermography and radiocarbon imaging</td>
      <td>218</td>
    </tr>
    <tr>
      <td>Exploring extrapolation risks of spatial prediction models at global, continental and regional scales</td>
      <td>219</td>
    </tr>
    <tr>
      <td>Proximal soil sensing: new tools for pedometricians</td>
      <td>220</td>
    </tr>
    <tr>
      <td>Spatial variability of peat soil properties for different sampling scales</td>
      <td>221</td>
    </tr>
    <tr>
      <td>Statistical analyses of landscape controls and vertical variability of soil organic carbon in permafrost-affected soils</td>
      <td>222</td>
    </tr>
    <tr>
      <td>Mapping Soil Properties for achieving Soil Functions</td>
      <td>223</td>
    </tr>
    <tr>
      <td>Refinement of soil maps of forested areas with help of geological maps, digital elevation model and remote sensing of vegetation</td>
      <td>224</td>
    </tr>
    <tr>
      <td>Analysing spatial patterns of soil pollution profiles in floodplain exposed to historical environmental load using correlation of proportional similarity matrices with spatial matrices</td>
      <td>225</td>
    </tr>
    <tr>
      <td>Near infrared index to assess soil texture and carbon content effects on soil hydrodynamic properties.</td>
      <td>226</td>
    </tr>
    <tr>
      <td>Mapping subsoil ripening using Bayesian Generalized Linear Modelling.</td>
      <td>227</td>
    </tr>
    <tr>
      <td>Investigating the effect of moisture for using field-portable X-ray fluorescence spectrometry for 2.5D high-resolution geochemical mapping</td>
      <td>228</td>
    </tr>
    <tr>
      <td>Desertification status mapping using recent machine language techniques</td>
      <td>229</td>
    </tr>
    <tr>
      <td>Updating digital soil maps with new data: a case study of soil organic matter in Jiangsu, China</td>
      <td>230</td>
    </tr>
    <tr>
      <td>Comparative examination of various uncertainty assessment approaches based on geostatistical approaches and machine learning algorithms</td>
      <td>231</td>
    </tr>
    <tr>
      <td>Predicting of soil properties with geostatistical and statistical models using a stratified regular sampling grid.</td>
      <td>232</td>
    </tr>
    <tr>
      <td>Performance of a Less Expensive Radiometer for Estimating Soil Organic Carbon</td>
      <td>233</td>
    </tr>
    <tr>
      <td>Proximal soil sensing - steps needed to be taken from research to real-world applications</td>
      <td>234</td>
    </tr>
    <tr>
      <td>Spatial Variations of Soil Organic Carbon Stocks and the Related Environmental Factors in Volcanic Ash Soils in Northern Taiwan</td>
      <td>235</td>
    </tr>
    <tr>
      <td>Estimation of current soil organic carbon stocks and evaluating the carbon sequestration rates under different management practices in arable soils of Taiwan</td>
      <td>236</td>
    </tr>
    <tr>
      <td>High-fidelity mobile proximal soil sensing (350-2500 nm) system</td>
      <td>237</td>
    </tr>
    <tr>
      <td>Towards a pedogenic approach in quantitative soil sampling and element stocks estimation</td>
      <td>238</td>
    </tr>
    <tr>
      <td>NDVI stratified sampling based on soil homogeneous areas. An application to rice crop in Babahoyo canton-Ecuador</td>
      <td>239</td>
    </tr>
    <tr>
      <td>Towards high-resolution modeling of global hydrology and the added value of detailed soil information</td>
      <td>240</td>
    </tr>
    <tr>
      <td>Proximal landfill sensing: new research grounds for high-resolution electromagnetic induction surveys</td>
      <td>241</td>
    </tr>
    <tr>
      <td>Characterizing soil organic matter, soil texture, and soil redistribution patterns in a frequently burned and a rarely burned prairie landscape</td>
      <td>242</td>
    </tr>
    <tr>
      <td>Topography reconstruction in eroding landscapes - a case study from a hummocky ground moraine in NE Germany (CarboZALF-D)</td>
      <td>243</td>
    </tr>
    <tr>
      <td>A new era: drone-borne gamma ray surveying to characterize soil</td>
      <td>244</td>
    </tr>
    <tr>
      <td>A new era: drone-borne gamma ray surveying to characterize soil</td>
      <td>245</td>
    </tr>
    <tr>
      <td>A new era: drone-borne gamma ray surveying to characterize soil</td>
      <td>246</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>Comparing UAV airborne and proximal measurements of a gammaspectrometer for soil texture mapping</td>
      <td>247</td>
    </tr>
    <tr>
      <td>Approaches for commercial Digital Soil Mapping, examples from South Africa</td>
      <td>248</td>
    </tr>
    <tr>
      <td>What is the value of understanding? Comparing mechanistic soil formation and geostatistical modelling.</td>
      <td>249</td>
    </tr>
    <tr>
      <td>Soil class mapping in the Quadrilátero Ferrífero, Brazil: a methodological approach of sampling and selection of covariates</td>
      <td>250</td>
    </tr>
    <tr>
      <td>Digital soil classes map of Minas Gerais State, Brazil</td>
      <td>251</td>
    </tr>
    <tr>
      <td>Modeling urbanization effect on soil functions in the New Moscow, Russia</td>
      <td>252</td>
    </tr>
    <tr>
      <td>Estimation of Soil Profile Properties Using Field and Laboratory VNIR Spectroscopy</td>
      <td>253</td>
    </tr>
    <tr>
      <td>The cost-efficiency of methods for monitoring soil organic carbon stock</td>
      <td>254</td>
    </tr>
    <tr>
      <td>Utilizing the Legacy Soil Data of Macedonia:The Creation of the Macedonian Soil Information System and its use for digital soil mapping and assessment applications</td>
      <td>255</td>
    </tr>
    <tr>
      <td>Mapping soil properties using a non-stationary variance geostatistical model</td>
      <td>256</td>
    </tr>
    <tr>
      <td>Modelling the mid-infrared information content of European soils</td>
      <td>257</td>
    </tr>
    <tr>
      <td>Prediction of Soil Organic Matter Using VisNIR and PXRF Spectroscopy</td>
      <td>258</td>
    </tr>
    <tr>
      <td>Potential of LUCAS for the development of regional-scale spectral models for the prediction of soil properties</td>
      <td>259</td>
    </tr>
    <tr>
      <td>Time sequence division of high standard farmland construction based on cultivated land quality uniformity in administrative village and obstacle factors</td>
      <td>260</td>
    </tr>
    <tr>
      <td>Is a national Vis-NIR library useful for filed scale predictions of soil type?</td>
      <td>261</td>
    </tr>
    <tr>
      <td>Hydro-geomorphic Spatial Modelling for Multi-scale Coastal Acid Sulfate Soil Mapping</td>
      <td>262</td>
    </tr>
    <tr>
      <td>A VNIR penetrometer for soil profile sensing</td>
      <td>263</td>
    </tr>
    <tr>
      <td>The Rapid Carbon Assessement Project: a modern soil carbon stock baseline for the conterminous United States</td>
      <td>264</td>
    </tr>
    <tr>
      <td>Predicting the soil adsorption behavior of two model Persistent Organic Pollutants (POPs) to the soil solid phase based on spectral data and multivariate statistical analysis</td>
      <td>265</td>
    </tr>
    <tr>
      <td>Fine-resolution mapping of soil carbon stock in Japanese forest based on machine-learning regression kriging</td>
      <td>266</td>
    </tr>
    <tr>
      <td>A MGWRK technique for mapping soil electrical conductivity in the Heihe River Basin, northwest China</td>
      <td>267</td>
    </tr>
    <tr>
      <td>Accounting for fieldwork costs in validation of soil maps: a comparison of design-based sampling strategies</td>
      <td>268</td>
    </tr>
    <tr>
      <td>Identifying soil landscape units at the district scale by numerically clustering remote and proximal sensed data</td>
      <td>269</td>
    </tr>
    <tr>
      <td>A prototype methodology for assessing within-field soil variation using digital soil mapping, legacy soil datasets and satellite imagery to aid precision farming</td>
      <td>270</td>
    </tr>
    <tr>
      <td>Teaching digital soil mapping as an example of contemporary environmental survey methods</td>
      <td>271</td>
    </tr>
    <tr>
      <td>Data mining of soil color database</td>
      <td>272</td>
    </tr>
    <tr>
      <td>Soil big data: Requirement and Potential in China</td>
      <td>273</td>
    </tr>
    <tr>
      <td>Development of China Digital Soil Map CDSM) at 1:50,000 Scale</td>
      <td>274</td>
    </tr>
    <tr>
      <td>Intelligent Mapping Tools (IMAT) for big soil data processing</td>
      <td>275</td>
    </tr>
    <tr>
      <td>Mapping soil properties for ecological and hydrological modeling in the typical inland river basin of northwest China</td>
      <td>276</td>
    </tr>
    <tr>
      <td>Digital mapping of soil carbon in a soil profile using image analysis</td>
      <td>277</td>
    </tr>
    <tr>
      <td>Review on the research of cultivated land quality monitoring in China</td>
      <td>278</td>
    </tr>
    <tr>
      <td>Methods comparison on prediction accuracy of soil properties derived from soil-landscape principles in a middle size watershed of Qinghai-Tibetan Plateau, China</td>
      <td>279</td>
    </tr>
    <tr>
      <td>Quantitative relationships between soil properties and hyperspectral characteristics and their possible application in soil classification in Qinghai Province, China</td>
      <td>280</td>
    </tr>
    <tr>
      <td>Rainfall magnitudes and digital soil mapping using land surface dynamic feedback patterns</td>
      <td>281</td>
    </tr>
  </tbody>
</table>
Evaluating Use of Ground Penetrating Radar and Geostatistic
Methods for Mapping Soil Cemented Horizon

Farideh Abbaszadeh Afshar – University of Jiroft, Iran

Shamsollah Ayoubi - Isfahan university of technology, Iran

Annamaria Castrignano - CRA — Research Unit for Cropping Systems in Dry Environments
(SCA), Bari, Italy

Depth to a root restricting (cemented) layer affects both soil moisture and nutrient availability. The knowledge of the variability of cemented layers within the soil profiles provides valuable information to decision-makers for agricultural activities. Soil surveys generally are time consuming, labor-intensive and costly, whereas geophysical and geostatistical methods offer a rapid, inexpensive and non-invasive approach to mapping soil characteristics. The ground penetrating radar (GPR) is a geophysical tool that is a high-resolution electromagnetic technique used for many applications, including assessment of groundwater resources, mineral exploration, archaeological, and environmental and agricultural studies. The agricultural applications may include such things as determining limited soil depth restricted by bedrock; hardpan and cemented horizon, water table depth, soil moisture content, and mapping shallow underground soil features affecting agricultural production. GPR combined with the geostatistical method could be an efficient and valid approach to provide large-scale measurements with fine-resolution data of substrate distribution. The objectives of this study are to use GPR data and to explore the capability of geostatistical methods to incorporate these auxiliary variables for mapping cemented horizon in an arid region, Kerman province located in south-eastern Iran. The measurements were performed using an impulse GPR system with a center frequency of 250 MHz along ten parallel transects of 100 m length and 10 m distance between two consecutive transects. The processing of GPR data was performed with ReflexW7.0 Software. The GPR data were interpolated with geostatistical analyses were done by using the software package ISATIS. The results of the field observations on the pedons at study site showed the cemented horizon at a depth of 35-45 cm. The GPR radargram also showed a distinct interface between the two contrasting materials which usually corresponded to the occurrence of a strong reflection event. The strong reflection corresponded approximately to the top of cemented horizon. In addition the 3D soil model of GPR amplitudes signal by kriging method reproduces the quite variable depth of the cemented horizon and the extent of its lateral and vertical variation. In this study, the potential of geophysical and geostatistical to predict and mapping a cemented horizon was evaluated in an arid region. The variation of cemented horizon provided valuable information that is crucial in decision making for agricultural and engineering activities. The main advantages of GPR and geostatistical methods are the speed of obtaining data and continuous images, and rather good possibility to identify zones with markedly different properties.

keywords: Cemented horizon, Geostatistic, Ground penetrating radar
# Digital soil mapping in areas of mussunungas: algoritmos comparission

Valdemir S. Abreu - Institute of Rural Development of Amapá, Brazil

Márcio R. Francelino - Federal University of Viçosa, Brazil

Elpídio I. Fernandes Filho - Federal University of Viçosa, Brazil

Eliana de Souza - Federal University of Viçosa, Brazil

Eliana E.S. Santos - Federal University of Viçosa, Brazil

Mussunungas is an ecosystem of physiognomic forms ranging from grasslands to woodlands. It occurs over Spodosols originated from sandstones of the Barreiras Group from the Tertiary period, and is situated across the Atlantic Rainforest domain of southern Bahia and northern Espirito Santo States in Brazil. Areas of Mussunungas are particularly important for water infiltration dynamics and is under threat as its vegetation is not recognized as belonging to the Atlantic Rainforest Biome. This study aims at comparing classification algorithms to produce soil class maps of an area where Mussunungas occurs. The study area is the conservation unity, RPPN Rio do Brazil, located in Porto Seguro, Bahia, with an area of 1100 ha. A digital elevation model derived from an ALOS-PALSAR image of 12.5m spatial resolution was used to derive morphometric maps, to be used together with covariates such as geomorphology, euclidean distance between the points soil points and indexes derived from Landsat-8 images, CBERS-4, and Sentinel-1 imagery. A soil survey was carried out using a mixed-sample scheme combining data from a free-survey with data from regular grid. A total of 203 soil points were described collecting samples for chemical and physical analyses. R software was used for the classification using the "caret" package. Three classifiers were tested: Extreme Gradient Boosting (xgbTree), C5.0, and Random Forest, with 10-fold cross-validation. From the six soil classes identified in the area, Haplic Lixisol are dominant, and as this occurs with inclusion of Dystric Cambisols, they were mapped together. Dystric Gleysols occurs associated to Fibric Histosols and was joined in the same map unity. Stagnic Podzols and Xanthic Ferralsols were thus each mapped as a single class. The xgbTree showed better Kappa (0.68), followed by C5.0 and Random Forest with 0.65 and 0.63, respectively. The similar performance of the classifiers showed no statistical difference. Larger confusion occurred between Xanthic Ferralsol and Haplic Lixisol. The user's accuracy of Podzols was good, varying from 81 to 83% amongst classifiers. The morphometric covariates, elevation above sea, slope height and normalized height showed the highest importance for soil prediction. A field validation of the maps and visual interpretation of the maps overlayed with satellite images showed that the areas were well delineated, proving a good performance of all classifiers for produce a soil class map for the area of this study. We concluded that techniques of digital soil mapping can be used for mapping areas of mussunungas over Podzols.

**keywords:** covariates, data mining, classification, Mussununga, Soil mapping
Sensing of farm and district-scale soil moisture content using a
mobile cosmic ray probe (COSMOS Rover)

Niranjan Acharige – The University of Sydney, Australia

Thomas Bishop – The University of Sydney, Australia

Brad Evans – The University of Sydney, Australia

Patrick Filippi – The University of Sydney, Australia

Edward Jones – The University of Sydney, Australia

Brendan Malone – The University of Sydney, Australia

Mario Fajardo – The University of Sydney, Australia

Uta Stockmann – The University of Sydney, Australia

In dryland cropping systems a grower only needs to know soil moisture at a few times during the year when a management decision needs to be made, for example at the start of the season when determining sowing and fertilizer rates. The information needs to be at the resolution of an agricultural field (or finer) and for the whole profile. Cosmic ray probes are a technology that can directly measure soil moisture at these resolutions, namely the horizontal footprint is a 150m radius and the depth of measurement is up to 0.7m. There is a now a mobile cosmic ray probe platform (COSMOS Rover) which could be used to provide a farm or district scale soil moisture map at key times in the year for growers. However, there are a number of methodological issues which need to be considered which we present in this work.

The first is a calibration issue which requires field measurements of soil moisture, soil carbon and soil lattice water content. These are prohibitively expensive for real-world applications and in this work, we compare the accuracy of the calibration between a detailed field survey as compared to using readily available data such as existing soil moisture probes and existing soil maps.

The second issue is how to interpolate the soil moisture measurements while accounting for the spatial support of measurements, and also variations in land use and other controllers of soil moisture within the measurement footprint. The measurement interval is 30 seconds so the horizontal support is 150m either side of the vehicle pathway over the travel distance of the vehicle in 30 seconds. For interpolation, we adopt an area-to-point kriging framework to account for the varying spatial support in addition to incorporating dense covariates (land use, geology) to improve our spatial predictions.

The approach is illustrated with a case study in Muttama creek catchment in eastern Australia where the dominant land uses are grazing and cropping.

keywords: Soil Moisture, COSMOS Rover, dryland cropping systems, calibration, spatial predictions
Proximal sensing of soil crack networks using three-dimensional
electrical resistivity tomography

Jason P Ackerson – Texas A&M University, Department of Soil and Crop Science, USA

Cristine L Morgan – Texas A&M University, Department of Soil and Crop Science, USA

Kevin J McInnes – Texas A&M University, Department of Soil and Crop Science, USA

Mark Everett – Texas A&M University Department of Geology and Geophysics, USA

Soil cracks function as primary conduits for water transport in expansive clay soils. Under-
standing and predicting the impact of crack networks on the hydrology of such soils is difficult due
in large part to the transient nature of crack networks. The size, position, and interconnected-
ness of soils crack networks changes with seasonal wetting and drying cycles. Currently, there are
limited tools for monitoring soil crack networks. In order to understand the impact of soil cracks
on hydrology, new tools are needed that can monitor the spatial-temporal dynamics of soil crack
networks. In this study we demonstrate the feasibility of electrical resistivity tomography (ERT)
as a non-invasive tool for proximal sensing of soil crack networks monitoring. Three-dimensional
ERT surveys were collected on a smectitic soil during a seasonal drying cycle. At the end of the
drying cycle, soil cracks were measured directly by in-filling cracks with cement and photograph-
ing cross-sections of the in-filled soil. Photographic data shows a good correlation with the ERT
images, demonstrating the utility of ERT as a tool for spatial-temporal monitoring of soil crack
networks.

keywords: proximal sensing, soil cracks, electrical resistivity tomography, ERT
Using digital microscopy for rapid determination of soil texture
and prediction of soil organic matter

Viacheslav Adamchuk - McGill University, Canada

Asim Biswas – University of Guelph, Canada

Long Qi - South China Agricultural University, China

Maxime Leclerc - McGill University, Canada

Bharath Sudarsan - Lizotte Machine Vision, Canada

Wenjun Ji – McGill University, Canada

Soil particle size analysis is an expensive analytical procedure. There are two primary methods accepted by certified soil analysis labs: hydrometer-based sedimentation and laser-based optical techniques. The first method requires substantial labour, while the second is conducted using costly equipment. This presentation describes a new system that is based on a commercially available digital microscope equipped with an array of light emitting diodes. It is deployed using a scratch resistant window in contact with measured soil. It takes just a few seconds to obtain a high quality image of dry, grounded and sieved soil that is typically used for conventional soil chemical tests. Our software uses an adopted wavelet decomposition technique to measure the percentage of particles within specified size intervals (e.g., sand, silt, and clay). Given that the level of magnification is known and the image resolution is around 2 micrometers, the instruments separate soil particles into their respected size category: clay - below 2 micrometers, sand - above 50 micrometers, and silt between clay and sand. The unique feature of this approach is that there is no need for sensor calibration and the results are expected to be comparable with those of conventional analytical soil laboratories. Based on our research, standard measurement errors range between 30 and 60 g per kg of soil, which is comparable to the observed dispersion among results obtained from different commercial soil labs. Furthermore, in addition to the image texture information component, image color was found to be suitable to predict soil organic matter content, which is another essential soil property when it comes to optimized management of soil resources. It was determined that, with proper calibration, soil organic matter content in mineral soil can be predicted with the standard error of measurements around 5-7 g per kg of soil. Based on these observations, we believe that the instrument developed can be used as an alternative to traditional soil particle size distribution techniques at a much lower cost and can preserve, or provide better measurement accuracy. Prediction of soil organic matter and in situ deployment of the instrument are additional options that could be pursued in certain environments.

keywords: Proximal soil sensing, machine vision, microscope, soil texture, soil organic matter
Analysis of complementarities of different spectral analytics to
sense soil properties

Viacheslav Adamchuk - McGill University, Canada

Wenjun Ji - McGill, Canada

Luc English - Logiag, Canada

Jacques Nault - Logiag, Canada

Qianjun Gan - McGill University, Canada

Ashraf Ismail - McGill University, Canada

Asim Biswas - University of Guelph, Canada

To date, the cost of soil sampling and analysis constitutes the greatest limitation for adjusting site-specific management of agricultural inputs according to local needs. New methods, with minimum distortion of proximal soil sensing, are being considered to replace the laborious and expensive wet chemistry analytical techniques. Spectra-based techniques, including visible, near-infrared, and mid-infrared spectroscopy, laser-induced break down spectroscopy (LIBS), as well as machine vision are among the most favoured. Individually each of these methods has provided positive results when attempting to predict specific physical and chemical soil properties. The goal of this presentation is to discuss the complementarities of these methods in order to optimize instrumentation and analytics for use in streamlining the analysis of solid soil samples. All of the measurements were performed using 35 compressed air dried soil samples and assessed in terms of measurement reproducibility and the accuracy of prediction. The results of leave-one-out cross validation of locally established prediction models were ranked among each combination of agronomic soil properties and measurement techniques as well as their combinations.

keywords: proximal soil sensing, soil spectrometry, LIBS, NIR, MIR
Long-term diachronic series for soil carbon saturation evidence.
A case study on volcanic soils of reunion island under sugarcane
crops.

Myriam Allo - CIRAD, Reunion

Alain Albrecht - IRD, Reunion

Patrick Legier - CIRAD, Reunion

Fabien Goge - CIRAD, Reunion

Magali Jameux - CIRAD, Reunion

Laurent Thuries - CIRAD, Reunion

Pierre Todoroff - CIRAD, Reunion

The goal of the international '4 per mille' initiative is to demonstrate that agricultural soils can play a crucial role for food security and climate change, and particularly in tropical areas where knowledge on soil carbon potential sequestration is still needed. The study is located at Reunion, a young tropical volcanic island in the Indian Ocean, 700km east of Madagascar, that presents a range of tropical soils comprising almost half of the 30 types recorded worldwide. Our work is focused on the drivers of soil organic carbon (SOC) content dynamics in the different types of volcanic soils under long-term sugarcane crops (more than 60% of the agricultural area). For doing so, a huge database on soil constituents has been mobilized. It was built over the last 20 years and represents more than 20 000 soil samples predominantly originated from sugarcane plots. Long-term diachronic series on SOC contents should be extracted from the database. Geolocated data combined with GIS tools allowed us to create a SOC map of Reunion and data mining tools, such as BRT, have been used to prioritize the drivers of SOC contents and evaluate the storage capacities of these young volcanic soils. In the conditions of the study, soil type was the main driver of SOC content, ahead of climate conditions and agricultural practices. Ferralsols, on the west and north coast, exhibit the lowest SOC content whereas Andisols, at higher altitudes, show the highest SOC contents for the 0-30cm layer. Long-term diachronic series showed almost constant SOC contents under sugarcane crops on the whole range of soils over time. Sugarcane cropping system produce high organic carbon inputs (residues and roots, 1.2 Mg C ha⁻¹y⁻¹) and agricultural practices in Reunion (mulching, one tillage every ten years on average) would maintain high SOC contents. All those considerations will suggest that soil carbon saturation is reached under sugarcane crops for all soil types. And hence, no more SOC storage is possible, but any land use change could decrease the soil organic carbon already stored in these soils. Soil carbon saturation rate, showed by long-term diachronic series, is therefore a better indicator than SOC content to develop soil carbon potential storage scenarios.

keywords: Soil organic carbon, tropical volcanic soils, long-term diachronic series, sugarcane, soil analyses database, data mining
# Concept of entropy in spatial distribution of vegetation in satellite images

Carmelo Alonso - Earth Observation Systems, Indra Sistemas S.A., Spain

Felix Cid-Diaz - Grupo Sistemas Complejos, UPM, Spain

Rosa M. Benito - Universidad Politecnica de Madrid (UPM), Spain

Ana Maria Tarquis - Universidad Politécnica de Madrid, Spain

The study of the dynamics of the vegetation cover is one of the most important applications of the observation of the Earth from the space and it is related to root zone soil moisture. Numerous studies have addressed the analysis of vegetation at different scales, spatial and temporal focusing on two aspects: the radiometric characterization of different types of vegetation and the spatial distribution of vegetation. The later is the result of a complex interaction between vegetation and certain environmental factors such as climate, animals and man activity. A previous step to understand the complex dynamics of the ecosystems is to be able to characterize the spatial patterns of this distribution in which soil play a major role.

In the present work we discuss the spatial distribution of vegetation based on entropy concept. However, there are different expressions related to it: entropy dimension, relative entropy, configuration entropy and configurational entropy per cell. Each one of them presents differences and complementary information.

In order to establish these comparisons, a multispectral image acquired on 8 August 2000 by the IKONOS satellite was selected. This satellite, in orbit from September 24-1999 at a height of 681 km, is capable of providing panchromatic images with a spatial resolution of the order of 1 m, and multispectral images, with 4 bands covering the visible region and the near infrared of the electromagnetic spectrum, with a spatial resolution of the order of 4 m. Both with a radiometric resolution of 2048 levels of gray (11 bits).

## References

Alonso, C., Tarquis, A.M., Zúñiga, I. and Benito, R. Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis. Nonlin. Processes Geophys., 24, 141-155, 2017.

Andraud, C., Beghdadi, A. and Lafait, A. Entropic analysis of random morphologies. Physica A, 207 (1-3), 208-212, 1994.

Murphy, B. What Does the Shannon Equation Really Mean? Pedometron, 39, 37-39, 2016.

Piasecki, R. Entropic measure of spatial disorder for systems of finite-sized objects. Physica A 277 157 - 173, 2000.

Shannon, C.E. A mathematical theory of communication. Bell System Technical Journal, 27, 379-423, 1948.

Tarquis, A.M., Bird, N. R. A., Whitmore, A. P., Cartagena, M. C. and Pachepsky, Y. Multiscale Entropy-based Analysis of Soil Transect Data. Vadose Zone Journal, 7(2), 563-569, 2008.

Tarquis, A.M., McInnes, K.J., Keys, J., Saa, A., Garcia, M.R. and Díaz, M.C. Multiscaling analysis in a structured clay soil using 2D images. J. of Hydrology 322, 236-246, 2006.

**keywords:** entropy dimension, relative entropy, configuration entropy, configurational entropy per cell, soil moisture
Digital Soil Mapping Method Based on the Similarity of Environ-
mental Covariates in the Spatial Neighborhood

Yiming An - Institute of Geographic Sciences and Natural Resources Research, Chinese Academy Sciences; University of Chinese Academy of Sciences, China

Cheng-Zhi Qin - Institute of Geographic Sciences and Natural Resources Research, Chinese Academy Sciences, China

A-Xing Zhu - Institute of Geographic Sciences and Natural Resources Research, Chinese Academy Sciences; Department of Geography, University of Wisconsin-Madison, China

Lin Yang - Institute of Geographic Sciences and Natural Resources Research, Chinese Academy Sciences, China

It is a prevailing way to use soil samples to build soil-environment relationships and then to predict the soil properties of the study area. An assumption widely used for building soil-environment relationship is that those locations with similar environmental conditions have similar soils. Based on this assumption, the soil property at each unvisited location can be predicted according to its environmental similarity to each individual soil sample, which is often computed based on environmental covariate values just on this locations and soil sample locations. The spatial neighborhood information is ignored in this way, although the soil at each location is also affected by the environmental condition of its surrounding area. With the assumption that the more similar the environmental conditions in the spatial neighborhood between two locations the more similar the soils are, a digital soil mapping method based on the spatial neighborhood similarity of environmental covariates was proposed in this research. The proposed method identifies the characteristic neighborhood size of each environmental covariate for each unvisited location and each soil sample and then calculate the spatial neighborhood similarity on individual environmental covariate between the unvisited location and each soil sample. The similarity on environmental condition between the unvisited location and each soil sample is computed by integrating the spatial neighborhood similarities on individual environmental covariates. The soil property value on each unvisited location is estimated to be the average of soil property values of soil samples weighted by the corresponding similarities on environmental condition. The prediction uncertainty is also provided. As a case study, the proposed method was applied to mapping soil organic matter (SOM) content (%) in the topsoil for an area (about 60 km²) in Heilongjiang province, China. The evaluation results with an independent soil sample set showed that the proposed method got higher accuracy due to its consideration of the spatial neighborhood information during computing the similarity of environmental condition.

keywords: digital soil mapping, soil-environment relationship, similarity, spatial neighborhood,
soil organic matter
# Multivariate and multi-layer soil mapping using structural equation modelling

Marcos Esteban Angelini - INTA, Instituto de suelos, Netherlands

Gerard B. M. Heuvelink - Soil Geography and Landscape group - ESG, Wageningen University & Research, Netherlands

Bas Kempen - ISRIC World Soil Information, Netherlands

Soil properties interrelationships are rarely included in digital soil mapping (DSM) because most DSM models take a univariate approach and map soil properties individually. In some cases multivariate approaches have been used to incorporate correlations between soil properties. Coking is a well-known example of such multivariate approach. A more recent approach is structural equation modelling (SEM), which has the advantage that it can incorporate pedologically-driven system interrelationships. We explored the use of SEM as a multivariate technique for DSM in a 23 000 km² study area in the Argentinian Pampas. The modelling process of SEM is driven by a conceptual model, which is translated to a mathematical model that is calibrated with empirical data. Since soil processes operate along the soil profile, SEM may be suitable for multiple layer soil prediction. Hence, theoretical relationships between soil properties at different horizons can be included and used for prediction. The objectives of this study were: (1) to apply SEM for multi-layer and multivariate soil mapping; (2) test SEM functionality for model improvement through model suggestions; and (3) assess whether the modelled covariation among soil properties matches the covariation observed in the data. We applied SEM to simultaneously model and predict the lateral distribution of the cation exchange capacity (CEC), organic carbon (OC) and clay content of three major soil horizons, A, B and C. We used petrological relationships between these soil properties to build a conceptual model. Next, we derived a mathematical model and calibrated it using environmental covariates and soil data from 320 soil profiles. Environmental covariates included digital elevation model derivatives, MODIS products and distance to river as a proxy of parent material distribution. Cross-validation of predicted soil properties showed that the highest amount of variance explained statistics were achieved for OC (24%) and for clay (60%) of the A horizon and CEC (50%) of the B horizon. We also assessed the covariation of soil properties and demonstrated that SEM reproduces the system error variance-covariance more accurately than multiple linear regression, which is generally applied in DSM. Accurate modelling of the covariation is important for stochastic simulation, such as required for uncertainty propagation analyses. We conclude that SEM can be used to predict several soil properties at multiple layers and, at the same time, provide graphical information of the relationships among system variables.

**keywords:** structural equation modelling, mechanistic models, soil property interrelationships, covariation assessment, conceptual models
Incorporating infrared spectroscopic data, land management, soil
drainage and soil erosion observations into Bayesian framework
for modelling soil erosion risk

Nikki Baggaley - James Hutton Institute, United Kingdom

Mads Troldborg - James Hutton Institute, United Kingdom

Jean Robertson - James Hutton Institute, United Kingdom

Estefania Pérez-Fernández - James Hutton Institute, United Kingdom

Allan Lilly - James Hutton Institute, United Kingdom

We have developed a framework for a Bayesian Belief Network (BBN) to model soil erosion. A BBN is a graphical probabilistic model which allows for the integration of different types of data from various sources as well as incorporating expert knowledge where data are lacking and explicitly accounting for uncertainty. We are currently developing the model to integrate observations of erosion, land management and soil spectroscopy. This work builds on an inherent erosion risk assessment based on soil drainage, topography and, as in many erosion risk assessments, where erodibility is defined as a function of topsoil texture (Lilly et al., 2002). A key aspect to this work is to improve the representation of soil erodibility in the model. To do this firstly we are exploring relationships between measured soil aggregate stability, land management and soil properties. Concurrently we are using infrared (IR) spectroscopy to provide additional information on the overall soil physical and chemical composition, in particular, the composition of clays and organic matter which influence erodibility. Ultimately, we aim to develop a robust and rapid method to predict those soil characteristics which define erodibility in-field using FTIR spectroscopy as a means to monitor soil quality.

keywords: Soil erosion, Bayesian Belief Network, Infrared spectroscopy
Comparing airborne and terrestrial laser scanning DTMs for high
resolution topsoil pH modelling

Andri Baltensweiler - Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Switzerland

Lorenz Walthert - Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Switzerland

Terrestrial Laser Scanning (TLS) is increasingly used to create very high resolution digital terrain models (DTMs). However, little is known about the accuracy of TLS derived DTMs covering several hectares in heterogeneous environments. We investigated the accuracy of DTMs derived from TLS data and compared them to conventional, high-quality airborne laser scanning (ALS) based DTM s. Two different interpolation methods, TIN and IDW, where used to create DTM s with cell sizes ranging from 0.2 to 4 m. Furthermore, we examined the effect of the different DTM resolutions, accuracies and acquisition techniques on topsoil pH prediction models. The pH models were based on linear regression models with only terrain attributes as covariables. The study area is characterized by a complex mirco-topography which is covered by dense evergreen forest and dense ground vegetation. The pH of the topsoil varies in the study area between 3.5 and 7.0.

The results showed that up to a resolution of one meter, the TLS based DTM s were more ac-
curate compared to the ALS DTM s for both interpolation methods. The accuracies of DTM s were
also reflected in the performance of the pH models. Generally, the model performance decreased
with decreasing resolution. However, the TIN based models with cell sizes of 0.2 m had a lower R2
than the models with 0.5 m resolution. The best soil pH model was based on a 0.5 m DTM derived
from TLS. It explained 62% of the pH variance whereas the best ALS derived model explained
50% of the variance and was also based on a cell size of 0.5 m.

The pH models showed that submeter DTM resolutions are required to predict topsoil pH accurately in the study area because the pH varied over a large range within short distances. We conclude that TLS data significantly improve DTM s and pH models compared to the models based on high-quality ALS data in heterogeneous environments.

keywords: DTM, Topsoil pH modelling, Terrestrial laser scanning, Airborne LiDAR, Accuracy,
Terrain attribute
Multi-sensor data fusion for supervised land-cover classification
through a Bayesian setting coupling multivariate smooth kernel
for density estimation and geostatistical techniques

Emanuele Barca - Water Research Institute of the Italian Research Council (IRSA-CNR), Italy

Annamaria Castrignanò – Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria,
Research Unit for Cropping Systems in Dry Environments, Bari, Italy

Sergio Ruggieri – National Research Council of Italy, Institute for Agricultural and Forest Systems in the Mediterranean, Italy

Gabriele Buttafuoco – National Research Council of Italy, Institute for Agricultural and Forest Systems in the Mediterranean, Italy

The data fusion is a growing research field, which finds a natural application in the remote sensing, in particular, for performing supervised classifications by means of multi-sensor data. From the theoretical standpoint, to address such an issue, the Bayesian setting provides an elegant and consistent framework. Recently, a methodology has been successfully proposed incorporating a geostatistical non-parametric approach for improving the estimation of the prior probabilities in the scope of the supervised classification. In this respect, a limitation affecting the Bayes computation in the multi-sensor data is the naïve approach, which considers independent all the sensor measurements. Obviously, such hypothesis is unsustainable in practice, because different sensors can provide similar information. Therefore, an enhancement of the previous described method is proposed, introducing the smooth multivariate kernel method in the Bayes framework to furtherly improve the probability estimations. A peculiar advantage of the smooth kernel approach concerns the fact that it is inherently non-parametric and consequently overcomes the multinormality data hypotesis. A case study is presented based on the data coming from the AQUATER project.

keywords: Data fusion, Remote sensing, Multivariate kernel density estimation, Geostatistics
# Uncertainty in soil properties from the hydrological point of view:
a call for new types of soil maps?

Gabriele Baroni - Helmholtz Centre for Environmental Research, Germany

Matthias Zink - Helmholtz Centre for Environmental Research, Germany

Rohini Kumar - Helmholtz Centre for Environmental Research, Germany

Luis Samaniego - Helmholtz Centre for Environmental Research, Germany

Sabine Attinger - Helmholtz Centre for Environmental Research, Germany

Soil properties play an important role in modeling land surface hydrological processes. However, due to the strong variability detected at all spatial scales the characterization of the soil variability remains a crucial challenge, especially over large areas. For this reason, in several studies, soil parameters are inferred indirectly based on hydrological measurements (e.g., streamflow, soil moisture) to improve the predictive capability of the hydrological models. For that purpose, several approaches and strategies are available in literature. However, the characterization of the uncertainty itself has received much less attention. The aim of the study is to assess the effect of different uncertainties in soil properties on simulated hydrological states and fluxes at different spatial and temporal scales. The study is conducted based on the data collected in the Neckar catchment (Germany). The original soil map is perturbed based on three methods. These perturbation methods introduce the same error (variance) but with different spatial structures (correlation lengths) that are assumed to not be resolved in the original soil map. The generated soil properties are used as input for the distributed hydrological model mHM (www.ufz.de/mhm). The model outputs (e.g., river discharge and soil moisture) are aggregated to different spatial and temporal scales. The results show how the three perturbation methods produce different results depending on the observation scale. Streamflow acting as an integrative hydrological response of the catchment is affected only by the perturbation of long spatial structure. Soil moisture represents more local hydrological conditions and it is affected by the small-scale variability introduced. The study underlines the importance of a correct characterization of the uncertainty in soil properties. By that, soil maps with additional information regarding the unresolved soil spatial variability would provide a strong support to hydrological modelling applications.

**keywords:** uncertainty, catchment, hydrological model
Detecting soil microbial community shifts via field spectroscopy

Harm Bartholomeus – Wageningen University, Netherlands

Gera Hol – NIOO, Netherlands

Reliable information on plant and soil health is important for early detection and prevention of pests, diseases or abiotic stresses. While diseases in agricultural systems can often be reliably assessed via remote sensing, it is unknown to what extent subtler changes in soil biodiversity could be detected. Plants have a multitude of positive and negative interactions with soil microorganisms which can all affect plant quality. Therefore, it seems plausible to detect shifts in soil microbial communities remotely by measuring plants response, on the leaf level or even above. We tested the hypotheses that 1) plants growing with different microbial communities will vary in leaf hyperspectral reflectance, and 2) the spectra from plant communities can be used to derive microbial communities. We measured hyperspectral reflectance patterns of Achillea millefolium and Trifolium repens and the whole mixed plant community with 7 plant species, growing on sterilized soils inoculated with field soil. Microbial communities varied in composition caused by serial dilution, resulting in decreasing bacterial diversity. Largest differences in the leaf vegetation indices were found between the most diverse soils and the non-inoculated control soils. The community level measurements showed stronger treatment signals than the leaf measurements. We will discuss the potential and constraints for detecting changes in soil microbial communities via plant hyperspectral reflectance.

keywords: soil microbial biodiversity, field spectroscopy, plant proxies
Using Near Infrared Spectroscopy in determining the mineralogical variations of the London Clay Formation, Whitecliff Bay, Isle of White, UK.

Ibrahim Bashar - School of Earth and Environmental Sciences, University of Portsmouth, UK,
United Kingdom

Andy Gibson - School of Earth and Environmental Sciences, University of Portsmouth, UK, United Kingdom

Nick Koor - School of Earth and Environmental Sciences, University of Portsmouth, UK, United Kingdom

Andrew Gale - School of Earth and Environmental Sciences, University of Portsmouth, UK, United Kingdom

Suggested geotechnical variations within the various lithologies of the London Clay Formation had been attributed to its mineralogical variations which required its constituent minerals to be further examined. Near Infrared reflectance spectroscopy has been extensively used in the prediction of various soil properties, identification of clay minerals and related soil chemical properties. The study uses the technique of Near Infrared (NIR) spectroscopy on soil samples obtained from a section of the cliff and foreshore exposures of the London Clay Formation at Whitecliff Bay, Isle of Wight, UK. The technique helps in the identification and quantification of clay minerals, particularly those that are responsible for susceptibility of the Formation to expansion and shrinkage. The soil samples were measured at reflectance intervals of 1300 nm to 2500 nm using ASD LabSpec 5000 spectrometer with the resultant spectra subjected to analysis using the statistical method and The Spectral Geologist (TSG) software. Results showed that the London Clay mineralogy is dominated by clay groups such as; Kaolins, White Micas, and Smectites. The research recognizes and identifies the mineralogy within the London Clay Formation by bringing together data relating to composition, mineralogy, and reflectance which helps provide valuable information on the geotechnical interpretation of the Formation using spectral techniques.

keywords: Near-infrared Spectroscopy, Mineralogy, London Clay Formation
Standardization of world soil profile data to support global map-
ping and modelling

Niels Batjes - ISRIC - World Soil Information, Netherlands

Eloi Ribeiro - ISRIC - World Soil Information, Netherlands

Johan Leenaars - ISRIC - World Soil Information, Netherlands

Ad van Oostrum - ISRIC - World Soil Information, Netherlands

Procedures for collecting, compiling, standardizing/harmonizing and subsequently providing quality-assessed world soil profile data to the international community, as developed in the framework of WoSIS (World Soil Information Service), are described. Harmonization, as defined by the Global Soil Partnership (GSP), involves "providing mechanisms for the collation, analysis and exchange of consistent and comparable global soil data and information". Areas of harmonization include those related to: a) soil description, classification and mapping, b) soil analyses, c) exchange of soil data, and d) interpretations. Seen the breadth and magnitude of the task, so far we have focused on developing and applying procedures for handling and standardizing legacy soil profile data, with special attention for the selection of soil properties considered in the Global-SoilMap specifications: organic carbon, pH, texture (sand, silt, and clay), coarse fragments (> 2 mm), cation exchange capacity, electrical conductivity, bulk density, and water retention. These properties are commonly considered in digital soil mapping and can be used to address a wide range of global issues, such as food security, combatting land degradation, and adaptation and mitigation to climate change.

The standardized data are served to the international community using two formats. Static snapshots in TXT format, with a time stamp and identifier (doi), are provided to allow for consistent citation purposes. For example, the 'July 2016' snapshot includes standardized data for some 94,000 profiles. Newly standardized data are gradually added to a dynamic version of the dataset that can be accessed '24/7' using WFS connection in GIS applications (some 109,000 profiles as of March 2017); the number of measured data for each property varies between profiles and with depth. Both the static and dynamic versions are freely available at: http://www.isric.org.

Future releases of WoSIS will consider a wider range of soil properties (e.g. content of nitrogen, phosphorus and other (micro) nutrients), including data derived from soil spectrometry. Instrumental to enhanced usability and accessibility of data managed in WoSIS will be the continued harmonization of soil property values and further standardization of soil analytical method descriptions. Development and testing of such procedures, in partnership with data providers, will allow for the fulfilment of (future) demands for global soil information, and enable further collation of standardized soil data shared by partners and third parties.

keywords: soil profile data, data sharing, standardization, harmonization
Soil and Environment software, a tool for soil management

Francisco Bautista – Centro de Investigaciones en Geografía Ambiental, Universidad Nacional Autónoma de México, Mexico

Angeles Gallegos - Skiu, Scientific Knowledge in Use, México, Mexico

Government agencies dedicated to environmental protection often do not have soil scientists so it is necessary to generate easy-to-use tools to interpret soil properties in the evaluation of the environmental functions of soils. The objective of this work was the elaboration of a software by means of which the properties of the profile of the soil become interpretations of the environmental functions of the soils. Soil and Environment (SE) software can be used to: a) evaluate the environmental functions of soils; and b) to elaborate degradation and conservation scenarios based on the erosion process. The data required to operate the software are: thickness of horizons, bulk density, stoniness, organic carbon, cation exchange capacity and texture, and others. The environmental functions of the soil that can be evaluated with SE are: a) allowing deciding to select the best sites for housing construction considering their damping power of contaminants such as heavy metals and organic substances; b) To select the soils suitable as habitat for wild flora and fauna; c) To select sites for aquifer recharge; d) Identify soils that stores over of the organic carbon contributing to reduce climate change; e) Identify soils with archaeological importance (human history, such as ancient temples or buildings); f) To appreciate soils with geological importance (history of the land, such as soils with the bones of prehistoric animals, soils with evidence of ancient sea beds, etc.); g) Identify soils with greater aptitude for food production. The evaluation results of environmental functions and predictive models can be presented by graphs. Export of tabular and graphical information is possible as well as the spatial reference data into the GIS. Friendly interface for data input and output and database management is designed for users who do not know SQL query language.

keywords: Environmental functions of soils, Soil properties, Software
# App Soil Calculator

Francisco Bautista – Centro de Investigaciones en Geografía Ambiental, Universidad Nacional Autónoma de México, Mexico

Angeles Gallegos – Skiu, Scientific Knowledge in Use, Mexico, Mexico

The Soil Calculator app has a database to store the properties of the soil, the data that can be registered and stored in the app are: a) profile information (profile name, geographic location, weather, photography and Site observations); b) basic properties (number and name of horizons, depth, aggregate stability, volume of coarse fragments, bulk density, textural class and pH); and c) auxiliary properties (nitrogen, carbon, K₂O, P₂O₅, cation exchange capacity, NO₃⁻, field capacity and permanent wilting point). Soil Calculator offers aids to the user to capture the basic properties of the soil; these aids were created based on the guidelines for the description of soils in the field of FAO and the USDA. Soil Calculator allows calculations at the soil profile level for the amount of fine earth, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity and nitrates in units of weight per unit area, either per hectare or square meter. It also offers the conversion of units between the International System and the English System. Soil Calculator is a tool for professionals related to the study of soils, such as farmers, foresters, urban planners, agricultural entrepreneurs, architects, experts and non-experts in the soil science. Soil calculator is available for free download in the Play Store

keywords: Soil properties, Database, Mobile app
Algorithms for quantitative pedology

Dylan Beaudette – USDA-NRCS, USA

Pierre Roudier – Landcare Research, New Zealand

The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into an R package. Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks. These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb. Components of the AQP project currently serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.

keywords: R, Software, Visualisation
Saskatchewan Soils: Access and improvements to soil information

Angela Bedard-Haughn – Department of Soil Science, University of Saskatchewan, Canada

Ken Van Rees – Department of Soil Science, University of Saskatchewan, Canada

Murray Bentham – Department of Soil Science, University of Saskatchewan, Canada

Paul Krug – Department of Soil Science, University of Saskatchewan, Canada

Darrel Cerkowniak – Agriculture and Agri-Food Canada, Canada

Brandon Heung – Simon Fraser University, Canada

Kent Walters – Department of Soil Science, University of Saskatchewan, Canada

Tom Jamsrandorj – Department of Computer Science University of Saskatchewan, Canada

Ralph Deters – Department of Computer Science, University of Saskatchewan, Canada

Saskatchewan, a province in western Canada, has a total area of 651,900 km² and a population of approximately 1.2 million people, nearly half of whom live in the two largest cities. The land base is dominated by agriculture in the southern half and boreal forest in the north. The sparse population, coupled with intensive resource use, places high demand on the province's soils with limited capacity for management oversight. At present, although most of the provincial soil survey information can be downloaded for viewing on a GIS platform, the files are not user-friendly and hence are under-utilized. Most people still rely on paper or pdf copies of the original soil surveys. In addition, most of the provincial soil maps are at a scale of 1: 100,000, which limits their usefulness for landscape modeling and precision agriculture applications.

To overcome these limitations, we have undertaken to improve the accessibility and quality of Saskatchewan's soil information. The first phase of the project is the development of an open source, user-friendly platform for viewing and querying our soil information, with the ability to develop themed maps for several of the most commonly requested soil properties, and the option to query a full suite of properties for the province as a whole and/or for specific locations. This platform is designed to be user-friendly for an audience ranging from farmers to policy makers and for applications from education to research. It also includes the option to upload information. At this time, the focus is on facilitating the uploading of georeferenced point files (photos, publications, datasets), but the capacity for uploading new soil survey data is also in development. The latter is of particular relevance given the need for refining the resolution of our soil information. The second phase of the project involves testing and implementing digital soil mapping procedures that are feasible given the challenges we face in Saskatchewan. 1) We lack a high-resolution digital elevation model that captures the variable Prairie Pothole topography. 2) We lack sufficient point data to take full advantage of the emerging data driven techniques. The ultimate goal will be to develop DSM methodologies that can be implemented on a local to regional scale, leveraging the increasing use of drones for topographic mapping to gradually build a refined map at the 1: 10,000 scale or better. This presentation will provide an overview of our progress and challenges to date.

**keywords:** Saskatchewan, digital soil mapping, crowdsourcing, spatial scale, Prairie Pothole Region, Canada
Soil Spectral Library of Ethiopia (SSL-ETH), Version-I

Tegbaru Bellete – Ethiopian Agricultural Transformation Agency (ATA), Ethiopia

Tekalign Mamo – Ethiopian Agricultural Transformation Agency (ATA), Ethiopia

Erik Karltun – Swedish University of Agricultural Science (SLU), Sweden

Selamyihun Kidanu – Ethiopian Agricultural Transformation Agency (ATA), Ethiopia

Infrared spectroscopy (IR) is currently being used in the Ethiopian Soil Information System (EthioSIS) project with the aim of predicting soil properties for Digital Soil Fertility Mapping on a large amount (>100 000) of soil samples. The objective of using the IR spectra, especially Mid-infrared (MIR) is primarily to be able to reduce analytical cost and secondly to reduce the problem of inter-laboratory variation in wet chemistry analysis. After air-drying and sieving the soil through a <2 mm sieve the samples have been analyzed for nutrients using the Mehlich-3 multi-nutrient extraction method and the extract has been analyzed with Inductively Coupled Plasma (ICP) for Ca, K, Mg, Na, P, S, Al, B, Cu, Fe, Mn, Zn, Co, Si, and Mo. CN analyzer is used to analyze the C and N contents, and the pH of the soils was determined in H2O using 1:2 soil to solution ratio using pH-meter. In this study, 61,331 soil samples collected from 80% of current crop land and 20% potential agricultural lands were collected from 540 administrative districts of Ethiopia. The samples represent topsoil, a depth of 0 - 20 cm for annual croplands and 0 – 60 cm for perennial croplands. The < 2mm sieve samples were further sub-sampled and sub-grinded to a mesh size of 0.5 mm, all the soil samples were scanned with replicates using a Bruker-TENSOR 27 spectrometer with the HTS-XT accessory. The spectra were captured using diffuse reflectance mode in the parts of MIR and NIR region (7,000 – 600 cm⁻¹). All the soil spectra were averaged and preprocessed using vector normalization, first derivative using the Savitzky-Golay smoothing method and CO₂ region is removed. Kennard-Stone algorithm was used to select 20% calibration and 80% validation datasets. The reference soil dataset was log-transformed to best fit to partial least squares regression (PLSR) model and the model performed better in predicting C with R² ≤ 0.91; Si with R² ≤ 0.91; N with R² ≤ 0.87 and pH with R² ≤ 0.52; and the model performed poor in predicting B, P, Na, S and Mn. MIR soil spectral library of Ethiopia can be used to monitor spatiotemporal variabilities and digitally map some of the soil properties quickly and at a low cost.

keywords: SSL-ETH, soil properties, IR spectra, ICP, prediction, digital soil mapping
Detailed predictive mapping of acid sulfate soil occurrence using
electromagnetic induction data

Amélie Beucher – Aarhus University, Denmark

Anton Boman – Geological Survey of Finland, Finland

Stefan Mattbäck – Åbo Akademi University, Finland

Henrik Nørgaard – Aarhus University, Denmark

Mogens Greve – Aarhus University, Denmark

Acid sulfate soils are often called the nastiest soils in the world (Dent & Pons, 1995). Releasing a toxic combination of acidity and metals into the recipient watercourses and estuaries, these soils represent a crucial environmental problem. Moreover, these soils can have a considerable economic impact through the resulting corrosion of concrete and steel infrastructures, or their poor geotechnical qualities.

Mapping acid sulfate soil occurrence thus constitutes a key step to target the strategic areas for subsequent environmental risk management and mitigation. Conventional mapping (i.e. soil sampling and subsequent pH measurements) has typically been used for acid sulfate soils. Recently, supervised classification modelling techniques were assessed for mapping acid sulfate soil occurrence and demonstrated promising predictive results at catchment or regional extent (Beucher et al., 2015, 2016).

Since acid sulfate soils contain large amounts of soluble salts, they yield strong electromagnetic (EM) anomalies, appearing as diffuse and round-shaped high electrical conductivity (EC) areas. EM induction data collected from an EM38 proximal sensor hence enabled the refined mapping of acid sulfate soils over a field (Huang et al., 2014).

Measuring the apparent soil electrical conductivity (ECa) can provide data on the spatial variation of soil salinity, which is associated with acid sulfate soil occurrence, but also of soil texture. The spatial distribution of different acid sulfate soil material types (clay, silt, sand, etc.) may have a great influence on the related environmental hazards (e.g. leaching of acidity) and their spatial variability at the extent of a field.

The present study aims at developing an efficient and reliable method for the detailed predictive mapping of acid sulfate soil occurrence. Different machine learning approaches will be assessed over a field located in western Finland, using soil observations and various environmental predictors (Quaternary geology maps, EM data collected from a DUALEM proximal sensor, and remote sensing data, such as airborne gamma-radiometric data, a LiDAR-based Digital Elevation Model and different terrain parameters derived from it).

Preliminary results show that soil texture variation could not be identified since fine-grained sediments homogeneously cover the study area. An inversion software called Aarhus Workbench (Auken et al., 2015) was also applied to create 2-D models of EC from the measured ECa. These EC models could enable detecting the transition zone, which represents the most acidic layer overlying the reduced parent sediment horizon (i.e. the sulfide reservoir with a high acidifying potential). This information appears as critical in the management of environmental risks related to acid sulfate soils.

Auken, E., Christiansen, A. V., Fiandaca, G., Schamper, C., Behroozmand, A. A., Binley, A., Nielsen, E., Effersø, F., Christensen, N. B., Sørensen, K. I., Foged, N., and Vignoli, G., 2015. An overview of a highly versatile forward and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric data. Exploration Geophysics, 2015, 223-235.

Beucher, A., Siemssen, R., Fröjdö, S., Österholm, P., Martinkauppi, A., Edén, P., 2015. Artificial neural network for mapping and characterization of acid sulfate soil: Application to Sirppujoki River catchment, southwestern Finland. Geoderma 247-248, 38-50.
Beucher, A., Adhikari, K., Madsen-Breuning, H., Greve, M.B., Österholm, P., Fröjdö, S., Jensen N.H., Greve, M.H., 2016. Mapping acid sulfate soils in Denmark using legacy data and LiDAR-based derivatives. Emerging frontiers in Pedometrics. Special Issue, Geoderma. In press.

Dent, D.L., Pons, L.J., 1995. A world perspective on acid sulphate soils. Geoderma 67, 263-276.

Huang, J., Nhan, T., Wong, V.N.L., Johnston, S.G., Murray Lark R., Triantafilis, J., 2014. Digital Soil Mapping of a coastal acid sulfate soil landscape. Soil Research 52, 327-339.

**keywords: Acid sulfate soils, Digital soil mapping, Electromagnetic induction**
Supplementing predictive mapping of acid sulfate soil occurrence
with Vis-NIR spectroscopy

Amélie Beucher – Aarhus University, Denmark

Yi Peng – Aarhus University, Denmark

Maria Knadel – Aarhus University, Denmark

Mogens Greve – Aarhus University, Denmark

Releasing acidity and metals into watercourses, acid sulfate soils represent a critical environ-
mental problem worldwide. Identifying the spatial distribution of these soils enables to target the
strategic areas for risk management.

In Denmark, the occurrence of acid sulfate soils was first studied during the 1980's through conventional mapping (i.e. soil sampling and the subsequent determination of pH at the time of sampling and after incubation, the pyrite content and the acid-neutralizing capacity). Since acid sulfate soils mostly occur in wetlands, the survey specifically targeted these areas.

Recently, a digital soil mapping approach was assessed to create a predictive map for potential acid sulfate soil occurrence in the wetlands of Jutland (c. 6500 km²; Beucher et al., 2016). An Artificial Neural Networks method was applied using 8000 soil observations and 16 environmental variables, including geology, landscape type and terrain parameters.

Visible-Near-Infrared (Vis-NIR) spectroscopy constitutes a rapid and cheap alternative to soil analysis, and was successfully utilized for the prediction of soil chemical, physical and biological properties. In particular, the Vis-NIR spectra contain diagnostic features for hydroxides, clay minerals, iron oxides and iron sulfates which are typically present in acid sulfate soils (Shi et al., 2014). Soil spectroscopy may thus efficiently supplement the mapping of acid sulfate soil occurrence.

The present study aims at predicting acid sulfate soil occurrence in the Skjern River catchment (c. 2500 km²). Different machine learning approaches will be assessed using soil and environmental data, together with laboratory Vis-NIR spectral data available for the study area. Absorbance values (400–2500 nm) were measured for 600 soil samples with a DS2500 instrument (Peng et al., 2015). The spectral data were summarized using principal component analysis (PCA). The first two principal components (PC) explained 99% of the variability in the spectra. Kriging was applied to upgrade PC scores information from point to image scale for further use within the acid sulfate soil occurrence modelling.

Beucher, A., Adhikari, K., Madsen-Breuning, H., Greve, M.B., Österholm, P., Fröjdö, S., Jensen N.H., Greve, M.H., 2016. Mapping acid sulfate soils in Denmark using legacy data and LiDAR-based derivatives. Emerging frontiers in Pedometrics. Special Issue, Geoderma. In press.

Peng, Y., Xiong, X., Adhikari, K., Knadel, M., Grunwald, M., Greve, M.H., 2015. Modeling soil organic carbon at regional scale by combining multi-spectral images with laboratory spectra. PLoS ONE 10 (11).

Shi, X.Z., Aspandiar, M., Oldmeadow, D., 2014. Reflectance spectral characterization and mineralogy of acid sulphate soil in subsurface using hyperspectral data. International Journal of Sediment Research 29, 149-158.

keywords: Acid sulfate soils, Digital soil mapping, Vis-NIR spectroscopy
3D prediction of soil moisture using data from varying horizontal
and vertical supports

Thomas Bishop – The University of Sydney, Australia

Thomas Orton – University of Queensland, Australia

Niranjan Acharige – The University of Sydney, Australia

Soil moisture is a key property for managing and modelling our environment for many end users from hydrologist to farmers to climatologists. Due to its importance there are multiple approaches being used to estimate or measure it in the field. Examples include direct measurements with soil moisture probes and radar estimates of soil moisture. Each has their advantages and disadvantages and for the most part these are related to the spatial support over which the measurements are made. In this work we define the spatial support as the vertical and horizontal area over which the estimate is made. In the case of soil moisture probes we effectively have a point horizontal support as in most cases the measurement volume is quite small, and in most cases sensors are arrayed vertically giving discrete point measurements throughout the soil profile. In terms of mapping soil moisture, probes are generally sparse in density. Radar estimates of soil moisture offer full spatial coverage and have a horizontal support of 250m and up to 20-40 km, however their vertical support is the first few cms of the soil profile. Other types of measurements are also available, e.g. cosmic ray probes which has a horizontal support of 250 m and vertical support of ~0.3-0.6 m. The issue is then how to use all of different types of soil moisture data in one model while accounting for their varying spatial supports.

To achieve this we present an approach which allows estimates of model parameters for datasets where observations have different spatially supports, and subsequent prediction with this data at any support vertically and horizontally. The approach is based on area-to-point kriging with residual maximum likelihood estimation of model parameters. We use 2 case studies with different soil moisture data streams. These include point measurements from soil moisture probes, remotely-sensed radar estimates, measurements from cosmic ray probes and whole-profile estimate of soil moisture from a water balance model.

keywords: spatial support, linear mixed models, soil moisture
# Joint multifractal analysis of the influence of topography and soil texture on soil water storage

Asim Biswas – University of Guelph, Canada

Multifractal analysis describes the variability and heterogeneity in the distribution of a variable by characterizing and summarizing the variability across scales. Joint multifractal analysis has been widely employed to characterize scale relationships between two variables co-existing along a single geometric support. In this study, the joint multifractal analysis was carried out for three variables coexisting in the same geometric support in order to describe the influence of topography (relative elevation) and soil texture (sand content) on water storage within the soil profile. Soil water storage was measured down to 1.4 m depth along a transect of 576 m long from the hummocky landscape of central Saskatchewan, Canada along with sand content and relative elevation. Joint multifractal analysis was conducted considering both the strange attractor formalism and the method of moments. The variability in soil water storage, sand content and relative elevation were scale dependent. The spatial variability in relative elevation was strongly reflected on water storage across analyzed spatial scales but the joint multifractal spectrum for sand content and water storage suggested a lower degree of correlation. The change in multifractality was also observed when both relative elevation and texture varied highly. This clearly demonstrated the capability of joint multifractal analysis to completely characterize the scaling behavior among three variables.

**keywords:** scaling, joint multifractal, three factors, soil water storage, topography, soil texture, geometric support
Multitemporal Soil Pattern Analysis for Organic Matter Estima-
tion on Arable Fields using Multispectral Satellite Data

Gerald Blasch – Newcastle University, United Kingdom

James A. Taylor – Newcastle University, United Kingdom

Due to the high socioeconomic and ecological relevance of soils, qualitative and quantitative
soil data at multiple scales are urgently required for land and resource management, soil protection
strategies, and more specific tasks in precision agriculture. For precision agriculture, soil data at
multiple scales is important to help optimise farm inputs. Therefore, remote sensing data analysis
can provide innovative, inexpensive and rapid tools for soil property prediction.

High-temporal, high-spatial resolution multispectral satellite imagery and time series offer great potential for soil information extraction, soil patterns derivation, mapping of potential site-specific management zones and soil surface units. The common problem with a single satellite image is the existence of heterogeneous spatial reflectance patterns associated with different land uses and vegetation conditions. This means static soil reflectance patterns persist aside from temporal patterns caused by crop types, vegetation phenology and field management practices. To reduce the disturbance of the temporal patterns, a Multitemporal Soil Pattern Analysis (MSPA) method for soil organic matter (OM) prediction using RapidEye time series has been developed and applied to German production fields. The method transferability was tested within the same soil landscape, showing a generally low prediction error (RMSE: 0.3 OM-%).

We present the MSPA method, which makes it possible to i) separate the static soil pattern from the temporal pattern, ii) identify most suitable bare soil images, and iii) evaluate the spatiotemporal variability of the soil reflectance pattern. This enables the generation of functional soil maps based on stable soil reflectance pattern at the field and the multiple-field landscape scale. The method consists basically of following steps: (1) selection of best suitable datasets (bare soil images) out of satellite time-series using automated classification based on NDVI thresholds; (2) soil reflectance pattern detection using standardised principal component analysis; (3) evaluation of spatiotemporal soil pattern stability using statistical per-pixel analysis; (4) functional soil mapping based on statistical analysis and stepwise exclusion of temporal effects. To enhance the operability of the MSPA approach, a further development is needed from a RapidEye-sensor independent approach to a hybrid method capable of utilising imagery from multiple satellite imaging sensors, such as RapidEye, Gaofen-2 and Sentinel-2. Furthermore, the MSPA method applicability to diverse soil landscapes is being studied on different geographical regions in China and the UK with greatly varying a) soil landscape genesis, b) climatic conditions, and c) cropping systems. Results from these studies will be presented.

**keywords:** Precision agriculture, Multitemporal analysis, Reflectance Pattern, Bare soil, Soil organic matter, Remote sensing, Sentinel-2, RapidEye, Gaofen-2
Spectral mixing for vis-NIR diffuse reflectance spectroscopy

Christina Bogner – University of Bayreuth, Germany

Anna Kühnel – Technical University of Munich, Germany

Diffuse reflectance spectroscopy has been widely used for rapid prediction of different soil prop-
erties. Nowadays soil spectra and corresponding reference soil properties can be accessed from
global spectral libraries. However, to ensure accurate predictions at local scale, local data need
to be incorporated into the model. To reduce the effort of acquiring local data, we suggest to
use spectral mixing and to generate synthetic spectra. Those spectra can be used additionally to
actually measured local spectra to increase the weight of local information. We use the Synthetic
Minority Oversampling Technique (SMOTE) originally introduced by Chawla et al. (J. artif. in-
tell. res., 2002, 16, 321-357) for classification problems and further developed by Torgo et al.
(Progress in Artificial Intelligence, Springer, 2013, 378-389) for regression. SMOTE linearly mixes
two soil spectra and calculates reference soil properties by a weighted average. We conducted a
laboratory study to evaluate whether this method is suitable to generate useful synthetic spectra
with accurate reference soil properties. Therefore, we mixed different soil samples with known
reference carbon content and measured the spectral reflectance of the mixed soil samples. We
generated synthetic spectra with the same mixing ratio to compare with the measured ones. Our
first results show that SMOTE with its linear mixing assumption can indeed be used to produce
synthetic spectra. However, their quality varies according to the similarity of original spectra
and the mixing proportion. We present the results from our laboratory study and show how the
synthetic spectra can be used for modelling of soil organic carbon.

keywords: vis-NIR spectroscopy, soil organic carbon, spectral mixing
Predictive mapping of the acidifying potential for acid sulfate soils

Anton Boman – Geological Survey of Finland, Finland

Amélie Beucher – Dept. of Agroecology, Aarhus University, Denmark

Stefan Mattbäck – Dept. of Geology and Mineralogy, Åbo Akademi University, Finland

Henrik Nørgaard – Dept. of Agroecology, Aarhus University, Denmark

Rainer Rosendahl – Pro Agria Österbottens Svenska Lantbrukssällskap r.f., Finland

Mogens Humlekrog Greve – Dept. of Agroecology, Aarhus University, Denmark

Developing methods for the predictive mapping of the potential environmental impact from acid sulfate soils is important because recent studies (e.g. Mattbäck et al., under revision) have shown that the environmental hazards (e.g. leaching of acidity) related to acid sulfate soils vary depending on their texture (clay, silt, sand etc.). Moreover, acidity correlates, not only with the sulfur content, but also with the electrical conductivity (EC) measured after incubation. Electromagnetic induction (EMI) data collected from an EM38 proximal sensor also enabled the detailed mapping of acid sulfate soils over a field (Huang et al., 2014).

This study aims at assessing the use of EMI data for the predictive mapping of the acidifying potential in an acid sulfate soil area in western Finland. Different supervised classification modelling techniques, such as Artificial Neural Networks (Beucher et al., 2015), will also be evaluated to generate predictive maps.

In the study area, an EMI-survey using a DUALEM proximal sensor was carried out during the autumn of 2016. The collected apparent soil electrical conductivity (ECa) measurements were interpolated using ordinary kriging. An inversion software called Aarhus Workbench (Auken et al., 2015) was used to create 2-D models of EC from the measured ECa. An unsupervised classification method was applied on the interpolated ECa map and LiDAR-based elevation data to partition the study area into homogeneous units. A soil sampling scheme was carried out based on this information, samples being taken from each unit.

From every sampling site, five soil cores were taken down to two meters depth with a manually operated soil corer; one primary soil core in the middle and four soil cores distributed evenly around the primary soil core. The primary soil core was used for detailed characterization of soil properties (grain size, structure, texture, field-pH, oxidation depth, ground water level) and acidifying potential (incubation-pH and titratable incubation acidity) whereas the four other cores were used for checking the soil variability. Soil observations from the primary cores are used as calibration and validation data within the modelling.

In addition to the EMI data, the present study relies on other environmental predictors: Quaternary geology maps and remote sensing data, such as airborne gamma-radiometric data, a LiDAR-based Digital Elevation Model (DEM), and different land surface parameters derived from this DEM (e.g. slope gradient, distance to channel network, flow accumulation and wetness index).

Preliminary results show that the acidifying potential is generally high and that it varies within the soil cores. The interpolated ECa map and EC models both appear to indicate the field drainage system. EC models could also enable detecting the transition zone, which constitutes the most acidic layer overlying the anoxic horizon (i.e. the sulfidic parent material with a high acidifying potential). Additionally, soil texture variation could not be identified because fine-grained sediments homogeneously covered the study area.

Auken, E., Christiansen, A. V., Fiandaca, G., Schamper, C., Behroozmand, A. A., Binley, A., Nielsen, E., Effersø, F., Christensen, N. B., Sørensen, K. I., Foged, N., Vignoli, G., 2015. An overview of a highly versatile forward and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric data. Exploration Geophysics, 2015, 223-235.

Beucher, A., Siemssen, R., Fröjdö, S., Österholm, P., Martinkauppi, A., Edén, P., 2015. Artificial neural network for mapping and characterization of acid sulfate soil: Application to Sirppujoki
River catchment, southwestern Finland. Geoderma 247-248, pp 38-50.

Huang, J., Nhan, T., Wong, V.N.L., Johnston, S.G., Murray Lark R., Triantafilis, J., 2014.
Digital Soil Mapping of a coastal acid sulfate soil landscape. Soil Research 52, 327-339.

Mattbäck, S., Boman, A., Österholm, P. Hydrogeochemical impact of coarse-grained post-glacial acid sulfate soil materials. Under revision in Geoderma.

keywords: Acid sulfate soils, Predictive mapping, Acidifying potential
Can soil spatial prediction models from different areas be similar?

Lubos Boruvka - Czech University of Life Sciences Prague, Czech Republic

Radim Vasat - Czech University of Life Sciences Prague, Czech Republic

Oldrich Vacek - Czech University of Life Sciences Prague, Czech Republic

Vit Penizek - Czech University of Life Sciences Prague, Czech Republic

Asa Gholizadeh - Czech University of Life Science, Czech Republic

Jakub Houska - Czech University of Life Sciences Prague, Czech Republic

Spatial prediction of soil properties in various scales is necessary for numerous applications and assessment. There is a vast number of prediction models in digital soil mapping differing in their complexity, target soil properties, structure of auxiliary data, resolution etc. This is caused both by available data and by different natural conditions. This contribution aims in an assessment of differences between soil spatial prediction models based on the same structure of input data, but developed for different regions.

Models for spatial prediction of soil organic matter (SOM) and clay content in soils were sepa-
arately developed for three different districts of the Czech Republic, namely (i) Rychnov nad
Kněžnou, ranging from flat area on the west to mountainous regions on the east, with expected
principal influence of topography, (ii) Litoměřice, characterized by variable geology, and (iii) Tá-
bor, with variable topography combined with frequent hydromorphic conditions. Available legacy
soil data were used, supported by all possible primary and secondary relief parameters (altitude,
slope, aspect, horizontal and vertical curvature, contributing area, topographic wetness index,
etc.), land use from the CORINE (COoRdination of INformation on the Environment) database,
and information from geological maps. Several model types were applied, including regression
trees, random forests, multivariate adaptive regression splines, artificial neural networks, and geo-
statistics. The output of the models were tested by leave-one-out cross validation, as well as on an
independent validation dataset.

The models built in different regions, though generally similar, differed particularly in the selection, importance and weight of individual environmental covariates, confirming thus different role of individual soil forming factors in different conditions. An attempt to combine the models to obtain a more universal prediction less dependent on the particular region was done. The combined models will be further tested on larger areas.

keywords: digital soil mapping, spatial prediction, auxiliary data, environmental covariates
Thermal remote sensing for digital soil mapping

Colby Brungard – New Mexico State University, USA

Nathan Lopez-Brody – New Mexico State University, USA

Incoming solar radiation reaching the earth’s surface is reflected, absorbed, or transmitted. Reflected visible-near Infrared solar radiation has been extensively used to derive environmental covariates for digital soil mapping. Remote sensing which measures energy emitted in the mid-, and thermal-infrared range (i.e., thermal remote sensing) may be useful as a digital soil mapping covariate, but this has been little explored. We investigate the utility of thermal remote sensing for deriving covariates for digital soil mapping. We first briefly discuss challenges and opportunities in acquiring thermal remote sensing imagery and review how thermal imagery can be transformed into physically-based covariates. We then demonstrate the use of land surface temperature and thermal inertia derived from thermal remote sensing as physically-based covariates for modeling soil depth in a semi-arid environment.

keywords: Thermal Remote Sensing, Digital Soil Mapping, Soil Depth
Optimal stratification for validation of digital soil maps

Dick Brus - WUR, Biometris, Netherlands

Lin Yang - 1State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Resources Research, Chinese Academy of Sciences, China

A-Xing Zhu - Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, China

The quality of soil maps can best be estimated by collecting additional data at locations selected by probability sampling. The data can be used in design-based estimation of map quality measures such as the spatial mean of the squared prediction errors (MSE). In areas with large differences in accessibility it can be attractive to account for these differences in selecting sampling locations. Sampling at lower densities in remote areas may increase the sampling efficiency. One of the options is stratified random sampling. We consider the situation where no ancillary point-specific information is available on the prediction errors. A novel method is presented that can be used to construct optimal strata using an estimate of the point-specific sampling costs. The optimal strata are constructed for optimal allocation of the total sample size to the strata, assuming a constant variance of the prediction errors within the strata. The minimization criterion is the sum of the variance of the estimated MSE and a penalty term. This penalty term equals 0 when the expected total costs are less than or equal to the available budget, and is proportional to the difference between expected total costs and the available budget otherwise. The stratum boundaries and the total sample size are optimized by simulated annealing. A sequence of vectors with stratum boundaries (cost values) and of sample sizes is generated. For the vector with stratum boundaries this is done by selecting randomly one stratum boundary and replacing this boundary by a randomly selected new boundary within the interval defined by the neighbours of the selected stratum boundary. The stratification method is tested in a simulation study and a real-world case study in Anhui, PR China. In the simulation study the point-specific costs are simulated by drawing from an exponential distribution with rate parameter 0.1. The simulated costs were multiplied by a constant (1, 5 and 10) to obtain cost distributions with different variances, and a constant was added (5, 10 and 50) representing fixed sampling costs. In the case study a map of the accessibility costs, modelled as a function of hiking costs and car travel costs, is used. The proposed optimization method gave plausible results in limited computing time (< 1 minute). We aim at extending the method so that prior knowledge about the prediction error, e.g. in terms of prediction error variances, is exploited in the optimal stratification.

keywords: probability sampling, design-based estimation, mean squared error, accessibility, sampling costs, simulated annealing
Spatial modeling of geomorphometric variables for natural hazard
valuation to desertification in tropical zones

Jeiner Buitrago Escobar – National University from Colombia, Colombia

Yolanda Rubiano Sanabria – National University from Colombia, Colombia

Diana Lucia Correa – Corpoica, Colombia

This work aimed to generate a spatial model to estimate the natural hazard to desertification in tropical areas, using geomorphometric variables, used as hazard indicators for the desertification process. The selected geomorphometric attributes are obtained from the digital elevation model (DEM), and correspond to relief indicators showing trends of degradation, gratification and hydrological modeling of the landscape, associated to flows such as runoff, erosion, sedimentation, accumulation of materials and surface wetness. The calibration of the model was performed in the Amaime river basin in the Valle del Cauca department (Colombia - South America) from the STRM (Shuttle Radar Topography Mission) with a spatial resolution of 30 m. The attributes: slope, curvature (horizontal and vertical), topographic wetness index (TWI) and factor length-slope (L-S) were calculated. The data were processed using the algorithms of SAGA 2.0.8 (System for Automated Geoscientific Analyzes, University of Gottingen, Germany) for the calculation of the geomorphometric parameters and hydrological modeling. The results were graded using indicators tables elaborated in this research, with the assignment of dimensionless values to pre-established ranges and categories, regarding the degree of participation in the causes or processes of degradation and desertification. The calibration allowed to establish that the spatial modeling of geomorphometric indicators identifies and groups areas with similar level of geomorphometric hazard that forms part of the natural hazard that causes desertification processes in tropical zones. For the Amaime river basin, the natural hazard is conditioned by the expression of the topographic attributes in the different relief units. Areas with a high geomorphometric hazard occupy 44.8% of the area and comprise piedmont and mountain positions, where slopes ranging from slightly to strongly steep (> 25%) predominate. As a consequence, with the slope predominating the slope length and inclination factor length-slope (L-S) is classified as medium to high, a condition found mainly on hillsides and mountain tops, where the loss of materials and moisture may be greater Magnitude favoring processes of degradation. 36.8% of the basin (38,457 ha) has a low hazard level, and correspond to sectors of deposit of materials and humidity located in the flat part and in smaller area between the valleys of the broken zone.

keywords: DEM, Land degradation, Hazard, Geomorphometric
Use of drone high resolution images to quantify soil erosion

Stéphane Burgos – Bern University of Applied Sciences, School of Agricultural, Forest and Food Sciences HAFL, Switzerland

Dorothea Noll – haute école de viticulture et œnologie, Switzerland

Precision agriculture has recently experienced substantial development and provides new man-
agement tools for the farmer and for soil protection. Here we present a case study that demonstrates
the potential of remote sensing using drone as a new tool for sustainable agriculture. Aerial images
were taken by a drone and for each flight an orthophoto and a digital surface model (DSM) were
obtained. A digital terrain model (DTM) was then computed based on the DSM. These two models
enable to compute a differential depth model (DDM) allowing the calculation of the eroded volume
on open land. This DDM also enables automatic detection of erosion gullies based on the resulting
depth difference. In this study, we show that such methods based on new technologies constitute
useful tools for the farmer. The use of these different models, coupled to the RGB images enable
(i) to understand the water flows on open land thanks to the DSM and (ii) to improve agriculture
sustainability by differentiating cultural practices leading to increased erosion in a given place
from those limiting the erosion risk. Moreover, such results also allow understanding the spatial
causes of erosion (roads, plought rills). Adequate management measures can then be taken on the
catchment scale in order to limit the loss of soil, which can be regarded as an almost non-renewable
resource, considering that it takes one century to build one centimetre of soil.

keywords: *Drone, erosion, high resolution images, detection, GIS*
# Predicting Scottish soil properties using X-ray powder diffraction

Benjamin Butler – The James Hutton Institute, United Kingdom

Stephen Hillier – The James Hutton Institute, United Kingdom

The terrestrial environment, and its human exploitation, both depend on the ability of soil to perform key functions. Well-functioning soils sustain ecosystems and are vital to life in the Earth's critical zone. As demands placed on soil increase, there is a pressing need for its sustainable management, a prerequisite of which is up-to-date soil information. In recent years the use of spectroscopic techniques as a rapid, cost-effective and reproducible measure of multiple soil properties has gathered momentum. Such 'digital' approaches can reduce the cost of soil mapping by an order of magnitude compared to conventional surveying, therefore their application to monitoring soil over greater spatial and temporal resolutions is particularly valuable.

In contrast to spectroscopic techniques, the application of X-ray powder diffraction (XRPD) to the prediction of soil properties has yet to be realised. The use of XRPD in soil analysis is typically associated with identification and quantification of the crystalline mineralogy from the diffracted signal, whilst there is also a diffuse background signal related to amorphous constituents. The combination of crystalline and amorphous information in each soil diffractogram can be utilised in the same way that visible and infra-red spectra have been used to produce predictive models of soil properties.

Here, XRPD was used to measure 1,246 Scottish soil samples taken from an objective 20 km surveying grid used for the National Soil Inventory of Scotland. Organic soil horizons were excluded for this investigation, leaving 844 mineral soil samples. Cubist, a machine learning algorithm, was then used to develop calibrations for the prediction of 8 soil properties (total carbon, total nitrogen, pHH2O, cation exchange capacity, aqua-regia extractable potassium, and the sand, silt and clay size fractions) from the XRPD measurements. Data were separated into calibration (75%) and test (25%) sets by random sampling. Upon applying the calibration models to the test sets, the R2 of the Cubist predictions (compared to laboratory measurements) ranged from 0.51 (pH2O) to 0.91 (carbon). For direct comparison, Cubist models were also computed for the same soil samples measured using near infra-red spectroscopy (NIRS). Evaluation of both calibration models shows the use of XRPD to outperform NIRS (higher R2, lower root-mean-square error) for the prediction of all tested soil properties except for pHH2O and base saturation. The results ultimately highlight the potential for XRPD to be used for the prediction of soil properties, and also in developing our understanding of soil property – mineralogy relationships.

**keywords:** Soil mineralogy, Soil properties, Data mining, X-ray powder diffraction
# Digital Soil Mapping of soil properties across GB: case studies from Scotland and England

Grant Campbell – The James Hutton Institute, United Kingdom

Ronald Corstanje – The James Hutton Institute, United Kingdom

Jacqueline Hannam – Cranfield University, United Kingdom

Allan Lilly – The James Hutton Institute, United Kingdom

Helaina Black – The James Hutton Institute, United Kingdom

Soil data harmonisation is a prerequisite for digitally mapping soils across Great Britain (GB) as, historically, soils data were collected by two separate survey organisations operating in Scotland and in England and Wales. It is therefore fundamental to firstly create a unified dataset of soil properties prior to developing models to predict those properties across GB. After investigation of the laboratory and sampling methods across Scotland and England and Wales, a dataset of key soil chemical and physical properties was derived. This study focusses on the depth distribution of these soil properties in two test areas: north eastern Midland Valley in Scotland and western England for six standard depth intervals specified in the GlobalSoilMap project (0-5, 5-15, 15-30, 30-60, 60-100, 100-200cm). The depth function of these soil properties was modelled using a mass preserving spline and predictions of soil property values across the areas were trained using Multivariate Adaptive Regression Splines (MARS) which were then deployed to the test areas. The model used a range of environmental covariates, based on the SCORPAN model (McBratney, 2003) and maps were generated at a resolution of 100m cells. The results for each soil property at their specific depths are presented. The models performed well in these areas though they had different covariates. Further work is needed to investigate whether these models can be developed and applied to the whole of GB.

**keywords:** data harmonisation, Great Britain, Soil properties, GlobalSoilMap, Multivariate Adaptive Regressive Splines (MARS)
# A routine chemometrics approach to estimate soil organic carbon in croplands exploiting LUCAS topsoil database.

Fabio Castaldi - UCL, Belgium

Bas van Wesemael - UCL, Belgium

Sabine Chabrillat - GFZ German Research Centre For Geosciences, Germany

Caroline Chartin - UCL, Belgium

The importance of spectroscopy for the quantification of the soil organic carbon (SOC) content and other soil properties is reflected by the increasing number of large soil spectral libraries collected in the world. In this regard, we tested two different routine chemometrics approaches to estimate SOC in three local spectral libraries (Belgian loam belt, Wallonia and Luxembourg), without the need for chemical laboratory analyses. In the first approach we used a regional library (REQUASUD) collected within the Wallonia region (Belgium) and currently used for routine soil fertility analysis, while the second approach is based on a standardized multivariate model exploiting the European topsoil database (LUCAS) in order to predict soil properties in a comparable manner across Europe.

In the light of the huge variability of the spectral responses to SOC content and composition, a rigorous classification process is necessary to subset large spectral libraries and to avoid the calibration of global models failing to predict local variation in SOC content. In this regard, the LUCAS topsoil database was subset into seven soil classes using a clustering analysis based on a large number of soil properties. Consequently, the samples belonging to each class were used to calibrate specific partial least square regression (PLSR) models to estimate SOC content in the three local libraries.

The REQUASUD library provided a satisfactory overall accuracy: RMSE is 4.6 g kg⁻¹ and the RPD 2.10. However, the accuracy reached by REQUASUD model is lower than that obtained using the LUCAS dataset, except for Belgian loam belt, where the statistics were identical.

Using the LUCAS database, SOC was estimated with a good accuracy both within each local library (RMSE: 1.2 ÷ 5.4 g kg⁻¹; RPD: 1.41 ÷ 2.06) and for the samples of the three libraries together (RMSE: 3.9 g kg⁻¹; RPD: 2.47). The use of LUCAS database could improve the estimation accuracy provided by a regional model, allowing, in theory, to estimate SOC everywhere in European croplands according with a uniform approach.

**keywords:** Soil organic carbon, Spectral library, Croplands, LUCAS topsoil database, chemometrics
# Integration of GPR measurements with sparse textural data for characterizing forest soils: an application of data fusion in southern Italy (Calabria)

Annamaria Castrignanò – Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria,
Research Unit for Cropping Systems in Dry Environments, Bari, Italy

Ruggiero Quarto – Earth and Geoenvironmental Sciences department, University of Bari Aldo
Moro, Bari, Italy

Francesco Quarto – Geoprospector srl, Barletta, Italy

Gabriele Buttafuoco – National Research Council of Italy, Institute for Agricultural and Forest
Systems in the Mediterranean, Rende CS, Italy

Geophysical sensors, coupled with traditional laboratory analyses, are useful to study soil properties over large areas with fine spatial resolution. The study was aimed to evaluate the potential of ground penetrating radar (GPR) to characterize forest soils of a catchment in southern Italy (Calabria). The catchment (139 ha) is located in the Sila Massif at a mean elevation of 1130 m a.s.l. and mainly covered by Calabrian pine. Chestnut and crops also occur. The bedrock consists of weathered Paleozoic granodiorithic rocks whereas terraced Holocene sands crop out in the eastern sector of the catchment. The soils are classified as Typic Xerumbrepts and Ultic Haploxeralf (USDA).

GPR data were collected with a monostatic system with a central frequency of 250MHz (Noggin Sensors & Software Inc, Mississauga, Ontario, Canada), using a global positioning system (GPS) device for recording profile location. The data were processed to enhance the signal to noise ratio and corrected to compensate for changes in surface elevation, and depth conversion. Temporal amplitude maps (time slices) were transformed to depth maps by using the estimated velocity of radar wave propagation of 9.3 cm/ns. In addition, 140 soil samples were collected up to 0.20-m depth and analysed in laboratory for soil texture. A multivariate data fusion geostatistical approach was used to map the GPR signal amplitude over the whole catchment up to the soil depth surveyed by GPR using a local DEM of 5-m scale resolution and texture as auxiliary information.

Preliminary results showed higher attenuation values of GPR signals near the valley sides and where colluvial soils with higher clay content occur, intermediate attenuation in the shallow soils near the water divide, whereas higher amplitude of GPR signal resulted where loose sandy soils crop out. The spatial relation was inverted at the deeper depths. The work demonstrates the potential of non-invasive geophysical method for forest soil characterization and the issue is worth being investigated further.

**keywords:** Data fusion, Geostatistics, Forest soil, Ground penetrating radar, Bonis catchment
# Determination of naturally occurring concentrations of trace elements in New Zealand soils

Jo-Anne Cavanagh - Landcare Research New Zealand, New Zealand

Stephen McNeill - Landcare Research New Zealand, New Zealand

In the last decade, largely due to legislative imperatives, there has been a substantial effort placed in determining the ambient background concentrations of various soil trace elements for managing land, especially in Europe. These studies have typically involved large-scale systematic sampling efforts, with detailed data analysis using spatial techniques. In New Zealand no similar studies have been conducted, although there are regulatory requirements for knowledge of background concentrations of naturally occurring elements for the purposes of managing land. Previous New Zealand studies on background soil concentrations have been undertaken at regional levels and have used different pedological and geological groupings to define background concentrations, which does not allow for the assessment of inter-regional similarities and differences in concentrations and the factors influencing these. This in turn, limits the ability to predict likely concentrations in locations for which no data is available. We note that there are numerous terms used to define the 'background' concentrations of soil trace elements, which are often used interchangeably or defined differently in different contexts.

To fill this data gap, we collated existing data from Regional Council soil quality monitoring programmes, regional background soil studies, and one regional grid-based sampling programme. This yielded highly clustered regional data with varying land use across New Zealand. The site-based trace element data was complemented with data from three GIS-based polygon spatial databases that regionally and/or nationally delineate areas interpreted to have distinct common pedological and/or geological properties. Specific parameters were extracted from these databases to provide some assessment of the geological origins of the soil, since the underlying geology is generally regarded as a major contributor to the geochemical signals in soils and surficial material. The range of trace elements varied greatly between regions, but our analysis was limited to arsenic, cadmium, chromium, copper, lead, nickel, and zinc.

Land-use effects were evident for most trace elements considered, with the effects observed differing for the individual trace elements. Spatial correlation was evident, due to the clustered nature of the samples, and the predicted spatial concentration distributions of log-concentration were formed using a Generalised Least Squares model. A rock-group based parameter was found to provide the best fit and was used to generate predicted background concentration distribution. These predictions provide a first-pass estimate of trace element background concentrations across most of New Zealand with data available at https://lris.scinfo.org.nz/ to facilitate usage by consultants and regulators.

**keywords:** Soil trace elements, Background concentrations, Spatial models
# Mapping spatial variability of soil organic carbon, phosphorus and soil acidity in Zambia

Lydia Chabala – University of Zambia, Zambia

Antony Chapoto – Indaba Agricultural Policy Research Institute, Zambia

Olipa Lungu – University of Zambia, Zambia

Successful agricultural crop production is highly dependent on the soil nutrient status and configuration. However, the spatial distribution of most soil parameters has been mapped only to a limited extent in Zambia. This entails that the country depends on global estimates of soil parameters for various applications. This study was carried to map the spatial distribution of soil organic carbon (SOC), phosphorus and acidity. The study was based on 1715 geocoded field data points across Zambia at which soil samples were collected. The soil samples were analysed for oxidizable carbon using the Walkley-Black procedure. The soil reaction (pH) was determined by the electrometric method while the available phosphorus (P) was determined by the Bray and Kurtz 1 method. Ordinary kriging (OK) was applied to model the spatial distribution of available P and soil pH with spherical variograms. Given that the data points were sparsely distributed across the entire country, OK was selected as the method of interpolation since there was adequate data for computation of variograms. Results indicated that there was a variation in the spatial distribution of available P, SOC and soil pH across the country. The average standard error was 0.5660 which was approximately equal to the RMSE of 0.5644 in the OK model used to map soil pH, and the RMSE standardized close to one (1). This means that the predicted soil acidity map was correctly assessing the variability of soil pH across the country. In the case of SOC which was mapped with IDW, the mean prediction error was -0.0035 indicating that the predictions were not biased. However, the prediction errors in the OK model for soil P were very large with the RMSE standardized of 0.7 indicating that the model used was underestimating the variability of P at locations that were not sampled. Therefore the SOC and soil pH maps could be used to guide management strategies for soil acidity and SOC. The map for soil P can be used to refine sampling strategy so as to improve its reliability.

**keywords:** spatial variability, soil pH, Soil organic carbon, available phosphorus
A Method Research on Digital Soil Mapping Using ES-RS-GIS in
Semi-arid Sandy Land: A Case Study of Horqin Left Back Banner

Li Chao – China Agricultural University, China

Zhang Fengrong – China Agricultural University, China

Wen Tiansheng – China Agricultural University, China

Xie Zhen – China Agricultural University, China

Abstract: The digital soil mapping (DSM) has been one of the hottest issues in soil science, while the conventional soil survey mapping need a lot of manpower and material resources. DSM is based on the relationship of soil and landscape. This study explored methods for DSM in semi-arid sandy area, taking into consideration pedogenesis and soil distribution. Horqin Left Back Banner, located in Inner Mongolia, is a typical semi-arid sandy area. The distribution of soil types is mainly determined by terrain in the county as climate and parent material are consistent. For example, Swamp soil is around lakes, Meadow soil is in periphery of the lake and between dunes. With the high salinity of groundwater, Meadow soil develops into Saline-alkali soil. Because Swamp soil, Meadow soil and Saline-alkali soil are subject to the impact of groundwater, soil moisture is high. Aeolian sandy soil is in the upper part of dune without the impact of groundwater and has the largest distribution in Horqin Left Back Banner. According to vegetation coverage and soil fixed condition, Aeolian sandy soil can be divided into shifting, semi-fixed and fixed. Although the terrain is the mainly controlling feature, it is difficult to use DSM to obtain soil map without high precision terrain data. The remote sensing data is sensitive to soil moisture and vegetation coverage; therefore, we took a new remote sensing data analysis as DSM with the knowledge of soil genetic characteristics and distribution in this study. Swamp soil was interpreted according to drought condition in springtime. Meadow soil and Saline-alkali soil were classified in terms of reflectance of image. Shifting, Semi-fixed and Fixed aeolian sandy soils were distinguished by vegetation coverage in summer when the vegetation growth was well in one year. Based on ES, GIS and RS, DSM results were as follows: Aeolian sandy soil, Meadow soil, Swamp soil and Saline-alkali soils take percentages of 83.29%, 15.68%, 0.49% and 0.54%. In Aeolian sandy soil, Shifting, Semi-fixed and Fixed aeolian sandy soil take percentage of 0.62%, 19.47% and 79.91%.

keywords: Semi-arid region, Soil genetic classification, Digital soil mapping
Soil classification of multi-horizontal profiles using support vector machines and vis-NIR spectroscopy

Songchao Chen - Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University & INRA InfoSol Unit & UMR SAS, INRA, AGROCAMPUS OUEST, France
Wanzhu Ma - Institute of Soil and Fertilizers, Zhejiang Academy of Agricultural Sciences, China
Dongyun Xu - Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, China

Shuo Li - CSIRO Land and Water, Bruce E. Butler Laboratory & Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Australia

Wenjun Ji - Department of Bioresource Engineering, McGill University, Canada

Zhou Shi - Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, China

The need for rapid and inexpensive techniques for high-resolution soil information has led to improvements over traditional methods, and in particular those based on visible near-infrared (vis-NIR) spectroscopy. While vis-NIR has been used for soil classification for some preliminary studies, how to combine spectral information from soil profiles remains a substantial challenge. This study was undertaken to investigate the potential of vis-NIR to discriminate soil classes on profiles containing various soil horizons.

We took 130 soil profiles at Zhejiang province, of which were classified in the field at suborder level according to Chinese Soil Taxonomy (5 soil orders and 10 suborders). Subsoil samples were taken by diagnosis layers (A, B and C). Support vector machine (SVM) algorithm was used to determine the soil classes, by analyzing quantitatively their diffuse reflectance spectra in the vis-NIR range. For SVM is a binary classification algorithm, the qualitative analysis was conducted by combining the votes of each sample from the same profile and the class got most votes in one profile was defined as their predicted soil class. Readily available variables (soil color) and well-predicted properties (soil organic matter, soil texture and pH) using vis-NIR spectra were added as auxiliary information.

Using synthesized model (spectra plus auxiliary soil information), SVM produced better classification performances at soil order level and suborder level (accuracy were 68.29% and 63.51% respectively) than spectra independently (accuracy were 60.69% and 58.54% respectively). They suggest that vis-NIR spectroscopy combining votes gained from SVM could make an essential contribution to the identification of soil classes in an effective approach of soil classification even when profiles contain various soil horizons.

keywords: Soil Classification, Multi-horizontal Profiles, Vis-NIR spectroscopy
Using new sparsity genomic methods to improve soil chemometric models

Christopher M. Clingensmith – University of Florida, USA

Sabine Grunwald – University of Florida, USA

Suhas Wani – ICRISAT, India

Diffuse reflectance spectroscopy (DRS) is a multi-collinear, multivariate problem that attempts to relate the reflectance response of a soil sample to its measured properties. Partial least squares regression remains the default and most reliable parametric method for prediction of published soil DRS studies. However, this method has drawbacks in that all bands included contribute to the final model. While the number of bands used can be reduced by using known absorption regions, it is difficult to know a priori which bands are related to the target property, as there can be complex interactions between minerals, organic matter, and particle size. The problem then becomes how to reduce the number of variables while simultaneously improving results.

To address this issue, new methods from the field of genomics, that deal with very high di-
mensional multi-collinear datasets, were evaluated for application in soil DRS modeling. The first
method, sparse partial least squares regression (SPLSR), is an extension of PLSR that imposes
sparsity on the dimension reduction step of PLSR so that at each step of the algorithm, it searches
for relevant variables through optimization of direction vectors. Sparsity is imposed by a threshold-
ing parameter, which must be tuned by cross-validation along with the number of latent variables.
The second method, heteroscedastic effects model (HEM), is an extension of ridge regression. Like
genomics, chemometrics is interested in retaining variables that are relevant, however weak they
may be. Similar to ridge regression, HEM applies a shrinkage factor across all variable but in-
stead of the shrinkage factor being equal across variables, a larger penalty is leveraged against
unimportant variables thereby reducing their influence on the model output.

We applied these methods to datasets collected from two agricultural villages in southern India, Kothapally and Masuti, and compared them to PLSR. In Kothapally, SPLSR improved model metrics for clay, pH, and available soil phosphorus, copper, and zinc, while HEM improved model metrics for clay, SOC, pH, TN, and available soil potassium, phosphorus, and boron. In Masuti, SPLSR improved model metrics for silt, sand, SOC, pH, and available soil copper, while HEM improved model metrics for clay, sand, IC, and available soil copper. The results demonstrate that SPLSR and HEM can improve prediction results compared to the standard method PLSR. Although the improvement was small in these case studies, these methods may prove useful for predictions on different types of soils.

keywords: *Diffuse reflectance spectroscopy*, *chemometrics*, *visible-near-infrared*, *sparsity*
Transferring and spiking of soil spectral models between two south
Indian villages

Christopher M. Clingensmith – University of Florida, USA

Sabine Grunwald – University of Florida, USA

Suhas Wani – ICRISAT, India

Soil spectral prediction models are typically calibrated and validated using data collected from a specific geographic region. Models can be transferred to new regions in an attempt to extend the model but they typically suffer from increased uncertainty in estimations. Spiking has been proposed as a means of alleviating the issue of uncertainty when transferring models. Spiking involves taking a subset of samples from the target area, recalibrating the model with the subset, and applying the new model to the target area. Multiple methods have been devised mostly using a larger national or regional level model with several local sets.

We evaluate a local-to-local assessment of transferring and spiking using local datasets from two agricultural villages in southern India separated by 300 km. Both villages have similar climatic conditions (semi-arid tropics) and soils. From each village, ~255 topsoil samples were taken, analyzed, and scanned in the visible-/near-infrared (VNIR) spectral range. Spiking was done in both directions with each village serving as the source and target area. The initial calibration model was generated from the source area using 70% of the samples. The target dataset was then split 50/50 into the spiking and validation datasets. From the spiking dataset, subsets were generated that represent 1, 2.5, 5, 10, 15, 20, 30, 40, and 50% subsets. The spiking subset generation was performed using random subsetting and systematic subsetting, where samples are selected along the distribution of the soil property. Each larger subset was ensured to contain the samples from the next smallest subset so that direct comparisons could be made. Unspiked and spiked models were then applied to the target validation set.

Results show that clay and sand models transfer relatively well with (R2: 0.63-0.81), albeit with higher root mean square error (RMSE) (7.71-18.3%). After 5% spike, results improved and leveling off after 30% spike. SOC and IC models did not transfer well (R2:0.01 - 0.27). Spiking improved models for SOC and IC but only in one direction of spiking with results leveling off at 30% spike. Total nitrogen models improved with spiking but only in one direction. Models for exchangeable potassium and soil available boron did not see much improvement with spiking, even at higher levels. Soil available Zn only improved in one direction with systematic sampling. Our results show that spiking improves models for soil properties with discernable spectral features with 30% of samples needed to improve prediction results.

keywords: Spiking, Diffuse reflectance spectroscopy, Visible-near-infrared, Chemometric
# Mapping the Impact of Zero Tillage on the Biophysical Properties of Soil

Hannah Cooper - University of Nottingham, United Kingdom

Murray Lark - British Geological Survey, United Kingdom

Sofie Sjogersten - University of Nottingham, United Kingdom

Sacha Mooney - University of Nottingham, United Kingdom

Approximately 40% of the world's land surface, and up to 68% in the UK is managed by ploughing to provide optimal conditions for crop establishment and growth. As ploughing involves the mechanical mixing of soil to a depth 20 cm, it can degrade soil structure increasing the chances of soil erosion, and so contributing to the pollution of water bodies. These concerns have led to increased interest in conservation agriculture including zero tillage, a practice defined as planting into unprepared soil whilst disturbing less than a third of the soil surface. Zero tillage presents many potential benefits, including increased soil organic matter, greater aggregate stability and reduced soil erosion. There is, however, conflicting literature on its potential to sequester more carbon and reduce greenhouse gas emissions. We report a study to assess how zero tillage alters soil biophysical properties on paired fields of ploughed and zero tilled soils across the East Midlands of England, and map these variations and estimate their effects across the region. The study entailed sampling close pairs of fields, one in zero tillage and the other under conventional cultivation. As a consequence of the sites being selected opportunistically, after surveys to find farmers willing to collaborate, all analyses are model-based. Preliminary results indicate an increase in organic matter content, microbial biomass carbon and water content under zero tillage, with a decrease in global warming potential, increasing over time in comparison to ploughed soils. Ploughed soils, imaged by X-ray Computed Tomography had a more porous structures in the top 5 cm whilst soils under zero tillage the upper layers were more consolidated. Further investigation into the effect of macro scale variables by modelling greenhouse gas fluxes and analysis of X-ray images will demonstrate the key mechanisms driving the biophysical changes between zero tilled and ploughed soils, study the climate change mitigation potential of zero tillage and enhance our understanding of the relevant processes at larger scales.

**keywords:** Zero tillage, Greenhouse gas emissions, X-ray computed tomography, Carbon, Co-kriging
# Analysis of total carbon in soils from Itatiaia National Park: relationship with profile attributes and terrain covariates

Elias Mendes Costa - Federal Rural University of Rio de Janeiro (UFRRJ), Brazil

Helena Saraiva Koenow Pinheiro - Federal Rural University of Rio de Janeiro (UFRRJ), Brazil

Robson Marcondes - Federal Rural University of Rio de Janeiro (UFRRJ), Brazil

Lúcia Helena Cunha Dos Anjos - Federal Rural University of Rio de Janeiro (UFRRJ), Brazil

Itatiaia National Park (INP) was the first conservation unit established in Brazil, in order to protect endemic species, and due to singularity of landscapes features and ecology and geomorphology, unusual for Brazilian Southeastern region. Despite many studies in the INP, with botanical and faunal themes, knowledge about soils is still scarce. In this sense, the study goal was to analyze, using R software and Algorithms for Quantitative Pedology package (AQP), the variability of some soil properties (C, N, H+Al) and their relationship with terrain attributes, as a support for characterize the profiles and to define units for soil digital mapping in the INP high altitude Plateau, Rio de Janeiro State. The soil dataset was compounded by 53 soil profiles, with a total of 231 horizons/layers, and the total carbon content (C), total nitrogen (N) and potential acidity (H+Al) contents along the soil profile were analyzed. The terrain attributes comprised topographic covariates and remote sensing data. The total carbon and total nitrogen were superior in the folic and histic horizons (O and H, respectively), due to the Histosols typical process of formation. The potential acidity showed a larger range in the folic horizons, with values varying between 5 cmolc.dm 3 and 45 cmolc.dm 3, and there was a general decrease of C, N, and H+Al with depth. Exception for the Histosols with drainage restrictions (histic horizons) and located in the lower portion of valleys, with colluvial deposition and different sources of organic material. The mineral soils were deeper, showed lower potential acidity and usually were covered by Atlantic forest vegetation, differing from the Histosols that have a sparse vegetation typical of high altitude fields. The INP soils are predominantly shallow, have high contents of C and H+Al, and are highly vulnerable to degradation, an aspect that should be taken in account in the park's management plan. The slice-wise method allowed to represent the soil attribute values in a 1 cm layer, in the soil profile collection. This is useful to study soil attributes variability along soil depth and the inter relations between soil attributes. The correlation between C and terrain attributes showed positive trends regarding to elevation (above sea level), and negative for data obtained from the spectral bands (RapidEye sensor). The information obtained from the soil survey and analyzed through AQP package is valuable to support decision-making regarding the park's management plan, as well as providing material for future research.

**keywords:** Quantitative pedology, Histosols, Soil attributes
# Proximal sensing of soil surface properties in relation to crusting, and rainfall-runoff processes: from portable to UAV-based platforms

Giacomo Crucil – Université catholique de Louvain, Belgium

The adverse impacts of surface runoff and erosion processes are well known. At present, accurate and reliable assessments of runoff and soil loss are complicated by the fact that the relevant underlying mechanics are strongly affected by small-scale spatial and temporal variability. Consequently, runoff and erosion models, as a support for mitigation practices, are limited in their predictive capabilities as the required input data is typically not available.

Given the recent development in light-weight (imaging) spectroscopy sensors, we performed a pilot study to assess the capability of a set of UAV-borne sensors to detect changes in key soil surface properties affecting runoff and erosion: these include soil crusting stage, micro-scale roughness and residue cover.

During a first phase, a set of laboratory experiments with a rainfall simulator and soil box-plot samples have been carried out to artificially generate soil crusts in structural and depositional stages. Water and sediment discharge have been collected and measured during the rainfall simulation, before and after crust development. The evolution of particles segregation and reflectance changes of the crust has then been captured, under sunlight conditions, with three multi-spectral cameras operating in the VIS and NIR domain, differing for band selection, band width and image resolution (Parrot Sequoia, Tetracam, Canon S110 NIR) and one miniature spectrometer (OceanOptics STS series). A ASD FieldSpec was used for reference measures. A similar experiment was carried out under real-life conditions on a bare, agricultural field, before and after natural rainfall events. With the same sensors, at this time mounted on a UAV, we acquired images at different altitudes (thus with different GSD – Ground Sampling Distance) of permanent plots, to evaluate the capability to distinguish crust types and residue cover through multi-spectral analysis, and roughness through photogrammetry.

Preliminary results from the laboratory experiment show that specific spectral signatures can be employed to distinguish crusted from non-crusted soil, also in accordance with previous studies. On the field, uncertainties on spectral results in crust detection raise with the GSD, while roughness and residue cover can be determined with acceptable accuracy. Finally, and based on this analysis, we discuss the potential and limitations of UAV-borne spectral sensors to detect the spatial and temporal variability in soil surface conditions.

**keywords:** proximal sensing, soil surface, rainfall-runoff, soil crusting, imaging spectroscopy, UAV
# The spatial variability of soil's plant-available water capacity, and its implications for site-specific management

Yash Dang – The University of Queensland, Australia

Thomas Orton – The University of Queensland, Australia

Matthew Pringle – Queensland Dept Science, IT and Innovation, Australia

Ram Dalal – The University of Queensland, Australia

The plant-available water capacity (PAWC) of soil is an important determinant of yield, and has been identified as one of the major sources of variability in grain yield within a field. Knowledge of the spatial variability of PAWC within a field is useful for site-specific management. However, field measurements of PAWC are costly and time consuming. We measured the spatial variability of apparent electrical conductivity (ECa) using Geonics EM38 sensor at times when soil the profile was wet and dry, in order to infer PAWC for three cropping fields near Biloela (Site 1; 64 ha; 24°33' S, 150°54' E), near Goondiwindi (Site 2; 257 ha; 28°18' S, 150°31' E) and near Moree (Site 3; 189 ha; 29°03' S, 149°81' E) in north-eastern Australia. The simple regression model showed a significant relationship between ECa (measured in both vertical and horizontal dipole modes at the wet and dry profiles) and profile volumetric moisture to 1.5 m. The coefficient of determination between measured soil water and profile-average ECa in vertical dipole mode were substantially higher than profile-average ECa in horizontal dipole mode, therefore, we used profile-average ECa in vertical dipole mode to predict soil profile's PAWC. Variability of predicted PAWC with measured PAWC showed reasonably good agreement (r2≤0.29-0.49). The areas with low PAWC matched reasonably well with areas that had higher concentrations of subsoil Cl- and higher surface-soil exchangeable sodium and/or magnesium. We delineated potential management classes using spatial and temporal variability of grain yield and ECa in vertical dipole mode. To determine if management by potential management classes was justifiable over conventional uniform field management, we conducted on-farm replicated experiments and simulated grain yield using a farming system model APSIM (Agricultural Production SIMulator) run for different growing seasons for winter wheat (1970-2012). Consistent with on-farm experiments, the simulation results justified dividing Site 1 and Site 3 into management classes, though differences between the simulated yields for the two zones at Site 2 were not significant. Results from on-farm experiments also suggested that application of ameliorants in low PAWC areas, and varying fertilizer nutrients input with spatial variability of PAWC, could improve profitability, and also reduce nitrate-N losses. The results underpin the value of PAWC at fine spatial resolution, for farm-scale decision making to improve economic and environmental outcomes.

**keywords:** Plant-available water capacity, Electromagnetic induction, Management classes, Agricultural Production SIMulator, On-farm experiments
Evaluating recent and sub-recent magnetic impact records of air
pollution by combined soil and bio-magnetic monitoring

Ynse Declercq - Ghent University, Belgium

Roeland Samson – University of Antwerp, Belgium

Filip Tack - Ghent University, Belgium

Philippe De Smedt - Ghent University, Belgium

Particulate matter (PM) from several emission sources typically exhibits an elevated level of magnetism compared to uncontaminated soils or plants. Through prolonged deposition of these particles, historical pollution can be reflected by an increase in soil magnetism, whereas the seasonal emission impact is captured in the vegetation. Our study combined soil magnetic susceptibility mapping with bio-magnetic vegetation monitoring to assess the impact of airborne pollution and to distinguish between sub-recent and recent air pollution, respectively.

Magnetic measurements were conducted across a 90 ha pinewood near the Ghent (BE) harbor area, situated on a large coversand ridge. The diamagnetic sandy soil rendered only negligible magnetic influence, so that anthropogenic enhancement of soil magnetism could easily be discerned from the natural background signal. Topsoil and downhole mapping revealed enhanced magnetic signals in the upper, organic soil horizont, while the spatial surface distribution revealed a clear pattern of higher values towards the harbor area. Lastly, the correlation between the magnetic signal and heavy metal concentrations in soil samples was investigated, revealing correlations up to 0.88 for selected elements. As such, the magnetic soil map can be interpreted in terms of a qualitative pollution distribution map.

While extreme magnetization was also attested on understorey vegetation samples (blackberry
and fern leaves), the observed spatial distribution was more diffuse, potentially relating to the
complex short-term deposition and throughfall processes of PM in the forest. In addition, poorer
correlations were observed between the magnetic signal of the vegetation and the heavy metal
loadings (up to 0.66). This could possibly relate to the abovementioned processes, to recent
changes in industrial emission patterns or to the internal uptake of heavy metals by plants.

These preliminary analyses show the potential of evaluating the impact of recent and sub-
recent airborne pollution by soil magnetic proxies and bio-magnetic monitoring. However, specific
issues (e.g. upscaling, varying land use, vegetation and soil heterogeneity) need to be taken into
account when optimizing the reproducibility of such an approach. Along with a presentation of the
preliminary analyses of combined mapping of soil and vegetation magnetization, we will therefore
discuss the potential and pitfalls of the approach.

keywords: environmental magnetism, bio- and soil monitoring, anthropogenic pollution
# Ecosystem services provided by groundwater dependent wetlands in karst areas: carbon storage and sequestration

Fabio Delle Grazie - Trinity College, Ireland

Laurence Gill - Trinity College, Ireland

Turloughs are topographic depressions in Irish karst, which are intermittently flooded on an annual cycle via groundwater sources and have substrate and/or ecological communities characteristic of wetlands. Turloughs are designated a Priority Habitat in Annex 1 of the EU Habitats Directive (92/43/EEC) as well as GWDTEs under the Water Framework Directive (WFD).

These ecosystems, if properly managed, yield a flow of services that are vital to humanity, including the production of goods (e.g., food), life support processes (e.g., water purification), and life fulfilling conditions (e.g., beauty, recreation opportunities), as well as the conservation of options (e.g., genetic diversity for future use). One of these services is the sequestration of carbon which is linked to climate change, one of the most serious environmental threats facing humanity.

The InVEST software was used to estimate the potential for carbon storage and sequestration. InVEST is a multi-module tool developed to inform decisions about natural resource management. Maps of land use and stocks in four carbon pools (aboveground biomass, belowground biomass, soil, dead organic matter) from previous surveys were used to estimate the amount of carbon currently stored in the landscape. Additional data on the market or social value of sequestered carbon and its annual rate of change, and a discount rate were also used in an optional model that estimates the value of this ecosystem service to society.

**keywords:** Turloughs, groundwater dependent wetlands, Irish karst, ecosystem services, soil carbon sequestration, climate change
Spatial explicit prediction of soil organic matter using a hybrid
model composed of random forest and ordinary kriging

Xunfei Deng - Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, China

Xiaonan Lu - Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, China

Zhouqiao Ren - Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, China

Wanzhu Ma - Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, China

Zhihua Ni - Agricultural Technology Extension Service Center of Zhejiang Province, China

Soil organic matter (SOM) has been identified as a key element in soil fertility and also in the global carbon cycle. SOM content of 0-20 cm topsoil is the essential to regional agro-ecosystem models. Reliable assessment of the spatial distribution of SOM content and its dynamics provides a valuable base against subsequent evaluation of soil measurements and agro-ecosystem models. Most digital soil mapping (DSM) relied on spatial correlation (e.g., Kriging, IDW) only, which is not always effective for DSM. Hence, this should be improved by integrating external auxiliary data. This study conducted a extensively soil investigation on cropland of Zhejiang, eastern China from 2007 to 2008, and a total of 35,571 soil profiles was collected. Soil properties, including SOM, soil total nitrogen, soil pH, bulk density and cropping pattern, were measured/recorded in laboratory tests/soil investigation. The satellite-derived data, such as nomalized difference vegetation index (NDVI), elevation, and land cover, was also extracted and integrated with soil properties and climate data as the external auxiliary data. Then, A hybrid method (RFOK), which is composed of random forest (RF) and ordinary kriging (OK), was adopted to evaluate spatial characteristic of SOM concentration. In this RFOK method, RF was used to address the association between external auxiliary data and SOM content, OK was used to address spatial correlation among the residuals generated in the RF stage. Meanwhile, the SOM spatial distribution was also interpolated by using ordinary kriging (OK) method for comparison purposes. The results showed that the prediction accuracy of SOM by using RFOK with the inclusion of auxiliary information in the hybrid model (R2≤0.73, RMSE≤5.63) was better than using only OK (R2≤0.56, RMSE≤8.09). The partial dependence plot showed that soil total nitrogen, bulk density, the climatic factors are considered as the most important indicators, while the effect of the soil texture, slope and elevation on the SOM content is negligible. It was also observed that the mean SOM concentration ranged between 2.3 g kg-1 and 46.8 g kg-1 in the top 20 cm, and the spatial distribution of the SOM content with a 250m spatial resolution in eastern China was achieved. Incorporating satellite-derived data, soil properties and land cover information can improve accuracy of SOM mapping and this is expected to be useful in future DSM activities.

keywords: random forest, nomalized difference vegetation index (NDVI), regression model, residues kriging, variable importance
Identifying soil management zones in a sugarcane field using prox-
imal sensed electromagnetic induction and gamma-ray spectrom-
etry data

Claire Dennerley – UNSW Australia, Australia

Jingyi Huang – UNSW Australia, Australia

Rod Nielson – Herbert Cane Productivity Services Pty Ltd, Ingham Qld 4850, Australia, Australia

Michael Sefton – Herbert Cane Productivity Services Pty Ltd, Ingham Qld 4850, Australia, Australia

John Triantafilis – UNSW Australia, Australia

Over 70% of the Australian sugarcane industry operates in alluvial-estuarine areas characterised
by sodic and infertile soils. There is a need to supply ameliorants and improve fertilisers and
minimise off-farm pollution to the Great Barrier Reef. Therefore, information is required about
the spatial variation in soils. However, traditional approaches are cost-prohibitive. Herein we
showed how a digital soil mapping (DSM) approach can be used to identify soil management
zones. In the first instance, ancillary data, including electromagnetic induction and gamma-ray
spectrometry data were collected. Using a fuzzy k-means clustering algorithm management zones
from two to six were identified. Using restricted maximum likelihood (REML) analysis of various
topsoil (0–0.3m) and subsoil (0.6–0.9m) physical (e.g. clay) and chemical (e.g. exchangeable sodium
percentage [ESP], exchangeable calcium and magnesium) properties, 3 zones were determined from
minimising the mean squared prediction error. To manage the moderately sodic topsoil ESP of
zones 3A and 3C and sodic 3B, different gypsum requirements were prescribed. Lime can also
be added differentially to address low exchangeable Ca in zone 3A, 3B and 3C. With regard
to exchangeable Mg, zones 3A and 3C do not require any fertiliser, whereas zone 3A requires
the addition of a moderate amount. The results were consistent with percentage yield variance,
suggesting the lower yield in 3C due to topsoil sodicity and strongly sodic subsoil with higher
clay content. We concluded that the DSM approach was successful in identifying soil management
zones and can be used to improve structural stability and soil fertility.

keywords: DUALEM-421S, RS-700, fuzzy k-means, digital soil mapping, sodicity, yield
Evaluating the potential of simulated soil clay content by SoilGen2
model as soft data in Regression Kriging in sparsely sampled areas

Thuy Doan – University of Ghent, Belgium

Peter Finke – University of Ghent, Belgium

Tien Tran Minh – Soils and Fertilizers Research Institute, Viet Nam

Models for soil evolution are progressively becoming invaluable tools to provide soil information. SoilGen2 model is one of such models with capabilities to simulate soil formation over multi-millennia time scale. This research aims to evaluate the potential of simulated soil clay content by SoilGen2 model as soft data in Regression Kriging in sparsely sampled areas. An area of 201 km² was chosen in the North of Vietnam to collect 100 soil observations which were then analyzed for Clay content of the topsoil in the laboratory. Besides, SoilGen model was used to simulate the Clay content for 2000 years at the same locations, using a uniform substrate (both spatially and in depth) as initial status. The simulated clay contents (soft data) then replaced, after an error correction by linear regression, the measured clay contents (hard data) in the input dataset for regression kriging with 4 proportions: 100% of hard data, 50% of hard data and 50% of soft data, 25% of hard data and 75% of soft data, 100% of soft data. In regression kriging, the hard data will get the weight 1 and the soft data get a weight representing 1/1+variance. There are six predictive covariates linked to the soil formation factors including geology, land use, elevation, slope, topographic wetness index (TWI), land use normalized difference vegetation index (NDVI). In order to estimate modelling uncertainty and compare model performance for different dataset combinations, a 10-fold cross validation scheme was computed. The results in terms of RMSE and ME are calculate for each dataset combination to evaluate the added value of simulated data in Regression kriging. The research results may help to improve the efficiency of Digital Soil Mapping by adding soft data in developing countries like Vietnam where the funds for soil sampling are very limited.

keywords: SoilGen2, Regression Kriging, Clay content
Orthogonalisation and standardisation as alternatives to improve
predictions of soil properties and lime requirement using on-the-go
Vis-NIR-SWIR spectroscopy

Marston Héracles Domingues Franceschini – Wageningen University, Netherlands

José Alexandre Melo Demattê – University of São Paulo, Brazil

Lammert Kooistra – Wageningen University, Netherlands

Harm Bartholomeus – Wageningen University, Netherlands

Rodnei Rizzo – University of São Paulo, Brazil

Caio Troula Fongaro – University of São Paulo, Brazil

José Paulo Molin – University of São Paulo, Brazil

Reflectance spectroscopy has increasingly been proposed as an alternative to describe soil prop-
erties. Acquisition of on-the-go spectral data in the field has several advantages over static ap-
proaches, like deriving information with high spatial density. However, some issues arise from this
acquisition approach due to sensor movement and heterogeneous soil condition in the field. Meth-
ods for correcting for these matters, like external parameter orthogonalization (EPO) and direct
standardization (DS), have been applied so far mainly to correct for "in situ" factors when static
field measurements were taken. In this study, EPO, DS and orthogonal signal correction (OSC)
are tested in the context of on-the-go spectra acquisition for prediction of soil properties, in special
properties related to liming (i.e. pH in CaCl2, pH in SMP buffer, organic matter, calcium and
magnesium content, potential acidity, sum of basis, cation exchange capacity and its saturation by
basis, lime requirement and moisture content). Correction methods slightly improved prediction
accuracy of lime requirement, for instance, with RMSE reduction from 1.43 to 1.17 ton ha⁻¹, for
study site 1 after OSC, and from 0.59 to 0.44 ton ha⁻¹, for study site 2 after DS. However, models
based on laboratory data still had considerably better performance with RMSE of 0.99 and 0.43
ton ha⁻¹ for site 1 and 2, respectively. "Global" (i.e. one general correction model for a given
field) or "specific" models (i.e. multiple correction models, derived according to clusters indicated
by fuzzy k-means algorithm applied to OSC components) performed considerably worse in com-
parison to other studies, and application of "specific" models did not improved correction results.
Probably on-the-go data acquisition causes inconstant issues and interaction between them can
occur, which decreased efficiency of the correction methods tested. Considering the high sensi-
tivity of predictions based on field data to the approach used to interpolate spectral information
and the relatively poor performance of correction methods, more investigation is need to improve
predictions based on on-the-go spectral readings using external parameters removal. Homogeneous
spatial distribution of factors not related to the properties of interest (i.e. external factors), or at
least in a degree allowing correction, may not happen when current measurement systems are used
and these need further development considering these issues.

keywords: soil chemical properties, proximal soil sensing, external parameter orthogonalization, direct standardization, orthogonal signal correction.
Use of GPR in evaluation of iron ore tailings deposition charac-
teristics in the River Doce Basin - Brazil

Eliana Elizabet Dos Santos – University Federal of Viçosa, Brazil

Márcio R. Francelino – University Federal of Viçosa, Brazil

Elpídio I. Fernandes Filho – University Federal of Viçosa, Brazil

Carlos E.G.R Schaefer – University Federal of Viçosa, Brazil

Greissz E. Marques – University Federal of Viçosa, Brazil

Valdemir S. Abreu – University Federal of Viçosa, Brazil

Felipe C. Santana – University Federal of Viçosa, Brazil

The disruption of the Fundão dam in Mariana-MG-Brazil in 2015 led to the spill of 46 million m³ of iron ore tailings in the Rio Doce basin, one of the most damaging in Brazilian history, and worldwide. Knowing the dynamics of tailings deposition features and depth, along the terraces and floodplains, under traditional agro-pastoral uses, is fundamental for developing tailored recovery plans. The objective of this work was to evaluate the best electromagnetic frequency to identify the depth and internal features of the sedimentary tailings in a tributary of the Rio Doce through the Ground Penetration Radar (GPR). GPR techniques allow detecting different layers of materials according to their dielectric properties. GPR emits and receives electromagnetic waves through antennas that, when processed in a computational environment, generate radargrams where it is possible to distinguish and analyze the behavior of soil characteristics in subsurface. The study area is located at coordinates 20.29635 ° S / 043.19537 ° W. We used the GPR model SIR-3000 and 3 antennas with frequencies of 200, 400 and 900 MHz, using ranges of 7, 3 and 1 meter respectively. A 10-fold constant was adopted for the tailing, classified as sandy loam by textural analysis. A transect of 36 meters perpendicular to the river bank was done to evaluate the behavior of the tailings. Undisturbed samples were collected at depths 0-10 and 10-20 cm and soil density and moisture of the tailings were evaluated by volumetric method. The GPR data were processed in the Radan program 7, in which radargrams obtained were submitted to filters to remove noise and improve the electromagnetic signal. It was observed that the deposition depth varied from 10 cm to 1 meter, with a clear limit with the subsurface truncated horizon of the underlying soil. At between 10-22 meters from the river bank, we noticed a typical signal in the radargram, where large differences in grain size and moisture of the tailings were detected. This anomaly is observed at a depth of 5.5 meters, and captured by the 200 MHz antenna. With the 400 MHz antenna that penetrated to the depth of 2.5 meters it was possible to better distinguish wetter areas, while with the antenna of 900 MHz gave the best radargram detail at 80 cm range. Moisture analyzes in the horizon of 0-10 cm and 10-20 cm were 0.13% and 0.15%, respectively, with no significant differences. However, the radargram showed great contrast between the tailings deposit (wetter) and the underlying soil. The 900 MHz antenna allowed a better distinction of the surface material, but no resolution for estimating the depth, easily identified by the 400 MHz antenna. Thus the combined use of 900 and 400 MHz antennas were efficient for the in situ analysis of the tailings depth and features.

keywords: Dielectric constant, Iron mining, Technosols
A graphical user interface in R to perform preprocessing, multi-variate modeling and prediction using spectroscopic data

Andre C. Dotto – UFSM, Brazil

Ricardo S. D. Dalmolin – UFSM, Brazil

Alexandre ten Caten – UFSC, Brazil

Diego J. Gris – UFSM, Brazil

Alrad Spectra is a graphical user interface (GUI) implemented in R programming language. It uses spectroscopic data to process the spectra and then generate models to predict the Y variable. The GUI was developed to accomplish tasks such as perform a large range of spectral preprocessing techniques, implement several multivariate calibration methods, statistics assessment, graphical output, validate the models using independent data sets, and predict unknown Y variables. Alrad Spectra has four main modules: Import Data, Spectral Preprocessing, Modeling, and Prediction. Data file can easily be loaded by the Import Data module. In Spectral Preprocessing module, the preprocesses available are smoothing, binning, absorbance, detrend, continuum removal, Savitzky-Golay derivatives, standard normal variate, multiplicative scatter correction, and normalization. The spectral curves and data table of each preprocessing can be saved whenever required. In the Modeling, predictive models can be built by different multivariate calibration methods such as partial least squares regression, support vector machines, random forest, artificial neural network, and Gaussian process regression. The Prediction module is implemented in order to predict the Y variable using the built models. The capacity of performing multiple tasks, being free and open-source, easy to operate, and requiring no initial knowledge of R programming language are features that make Alrad Spectra a useful tool for general public, researches, precision agriculture managers, and for the usage in analytical laboratories. The implementation of Alrad Spectra is demonstrated by applying visible near-infrared reflectance spectroscopy for soil organic carbon prediction. The greatest SOC prediction performance was achieved by partial least-squares regression model with standard normal variate spectral preprocessing (R²≤0.81, RMSE≤0.51%, RPIQ≤2.84).

**keywords:** Chemometrics analysis, Visible Near-infrared spectroscopy, R programming, Soil organic carbon
Rapid detection of alkanes and polycyclic aromatic hydrocarbons
(PAH) in oil-contaminated soils using visible near-infrared spec-
troscopy and chemometrics

Reward Douglas – CRANFIELD UNIVERSITY, United Kingdom

Said Nawar - CRANFIELD UNIVERSITY, United Kingdom

Carmen M. Alamar - CRANFIELD UNIVERSITY, United Kingdom

Frederic Coulon - CRANFIELD UNIVERSITY, United Kingdom

Abdul M. Mouazen - GHENT UNIVERSITY, Belgium

The recent developments and applications of rapid measurement tools (RMT) such as visible
near-infrared (vis-NR) spectroscopy can provide ‘fit for purpose’ and cost effective data for inform-
ing risk assessment and managing oil-contaminated sites. While vis-NIR spectroscopy has been
used to detect and quantify on-site hydrocarbons in soils and sediments, it is not appropriate for
the elucidation of hydrocarbon compound structures and therefore advanced chemometrics meth-
ods are needed to expand the applicability of vis-NIR spectroscopy. In the present study, 74 oil
contaminated soil samples collected from the Niger Delta were scanned using an analytical spectral
device spectrophotometer with a spectral range of 350-2500 nm. The vis-NIR signal for each soil
sample was then analyzed using partial least squares regression (PLSR) and random forest (RF)
regression. Reference alkanes and PAH fingerprints of the oil-contaminated samples were deter-
mined using sequential ultrasonic solvent extraction followed by gas chromatography- coupled to
mass spectrometry (GC-MS) analysis. Prior to the model development, spectra were subjected
to pre-processing including successively, noise cut, maximum normalization, first derivative and
smoothing. The pre-processed spectra were divided into calibration (75%) and validation (25%)
sets. The alkane and PAH concentrations determined by GC-MS and the vis-NIR based spectra
were subjected to PLSR and RF with leave-one-out cross-validation (LOOCV) to establish calibra-
tion models. Results showed that RF calibration models for both alkanes and PAH outperformed
PLSR, indicating that vis-NIR signal acquisition followed by RF can provide a rapid and cost
effective means to inform risk assessment for oil-contaminated sites.

keywords: Total petroleum hydrocarbons, vis-NIR spectroscopy, chemometric methods, site inves-
tigation
A combination of soil sensors provides useful and efficient land-
scape genesis information for archaeological prospection

Reinier Ellenkamp – RAAP Zuid Nederland, Netherlands

Fenny van Egmond - Wageningen Environmental Research &amp; ISRIC - World Soil Information,  
Netherlands

Timothy Saey - ORBit, Department of Soil Management, Ghent University, Belgium

Marc van Meirvenne - ORBit, Department of Soil Management, Ghent University, Belgium

Ronald Koomans - Medusa Explorations, Netherlands

A combination of EMI, GPR, gammaspectrometry and archaeological soil profiling was used in a commercial project to map the landscape genesis, soil lithology and the archaeological potential of a 50 ha development area. This was more efficient and yielded better and more detailed results than a traditional archaeological mapping with twice the budget could have done. The area consists of recently equalised river terraces of the river Meuse in the Netherlands which have formed by a meandering river on top of braided river sediments that have been effected by wind erosion. The three sensor techniques were applied on a 10 m grid. 30 ha were measured with a MS4000 gammaspectrometer and 300 MHz Zond GPR and 30 ha were measured with a Dualem 21S EMI system. The zones had a 5 ha overlap. The raw geophysical data were used for planning the archaeological test pits and soil profile transects perpendicular to the variation (196 soil profiles of 0-2 m). These results were then used to interpret the geophysical data. This provided a detailed insight in paleo- and current landscape genesis and lithology up to 2 to 4 m depth. Based on both paleolandscape and current disturbances the archaeological potential was derived and verified using 300 new soil profiles. This validation showed that 80 % of the lithological variation in this complex area had been mapped. The high level of detail, the improved understanding of the spatial structure and the joint coordination of geophysical and archaeological mapping allowed for a better comprehensive geomorphogenical and archaeological interpretation than would have been possible otherwise. The techniques applied, although of limited use separately, complemented each other in depth range and precision, spatial detail and type of soil texture information. This led to a high quality result within a limited budget. The gammaspectrometer raw data provided a reliable variation map of the topsoil, concurrent with soil type variation. The GPR showed detailed soil textural layering and accurate depths in combination with the profiles. Especially the archeologically relevant gravel layer was mapped well. The EMI yielded detailed shallow soil texture variation, some old ditches and recently disturbed zones. It also provided rough texture indications for the deeper soil layers. As a recommendation to the Province of Limburg, the client, an optimal workflow has been formulated using GPR, EMI and archaeological profiling. The results are also useful in the geotechnical planning of the development area.

keywords: Soil sensing, EMI, GPR, Gammaspectrometer, Archaeology, Lithology, Prospecting, Geomorphogenesis, Lithogenesis, River-terrace
# Assessment of soil ecosystem services at landscape scale by direct soil monitoring and modelling.

Yosra Ellili - UMR SAS, INRA, AGROCAMPUS OUEST, France

Christian Walter - UMR SAS, INRA, AGROCAMPUS OUEST, France

Didier Michot - UMR SAS, INRA, AGROCAMPUS OUEST, France

Pascal Pichelin - UMR SAS, INRA, AGROCAMPUS OUEST, France

Blandine Lemercier - UMR SAS, INRA, AGROCAMPUS OUEST, France

Soil is a natural capital that provides several ecosystem services that ensure human wellbeing and sustainable socio-economic development. Among international researchers, the landscape scale is considered the relevant scale to assess soil ecosystem services. This spatial scale allows a bio-physical evaluation to take into account both landscape features and management practices. The aim of this study was to develop a methodology to quantitatively evaluate and map two environmental regulation ecosystem services - carbon sequestration and erosion regulation. In a 10 km² study area in north-western France (Brittany), sixty-four soil observations were selected using the conditioned Latin hypercube sampling method. This approach used elevation, a topographic wetness index, natural gamma emission of potassium, and grassland frequency over 15 years to select points with a good representation of the study area. The biophysical quantification of climate-regulating ecosystem service was based on the changes in the soil organic carbon (SOC) stock over a period of seven years. The assessment of erosion regulation ecosystem service was based on the observed difference between potential and present soil erosion, both estimated by the Revised Universal Soil Loss Equation (RUSLE). To characterize spatial distribution of climate-regulating ecosystem service, two approaches were used. The first was to spatialize the changes in SOC stock using a soil digital mapping approach with the Random Forest method and a calibration dataset of 64 points. The second was to assign a value of the indicator for each type of land use. Uncertainties related to the soil ecosystem services estimations were also investigated using the Monte Carlo approach. The main results of this study were: 1) inherent soil properties especially soil waterlogging and land use trajectory were the most explanatory factors for the spatial distribution of carbon sequestration; 2) the erosion regulation ecosystem service was highly correlated with topographical factors; 3) interactions (situation of synergy and antagonism) between selected soil ecosystem services were mainly dependent on landscape features and land use. Overall, providing a biophysical evaluation of soil ecosystem services in coherence with agronomic decision scales, is still a challenge. Moreover, multiscale assessment of soil ecosystem services, taking account of the various soil processes evolution over time, allows comparison of actual situations with the five dimensions of soil security. These projections have implications for future planning monitoring of agro ecosystems, and can be integrated into land use decision-making.

**keywords:** Direct soil monitoring, Soil ecosystem services, Landscape scale, Conditioned Latin hybercube sampling method, Uncertainty propagation
# Measuring functional pedodiversity using spectroscopic information

Mario Fajardo – The University of Sydney, Australia

Alex McBratney – The University of Sydney, Australia

Budiman Minasny – The University of Sydney, Australia

Pedodiversity studies greatly depend on the discretization of the soil continuum via various soil classification standards. Soil taxonomic systems are constructed considering soil properties and their organization in the soil profile. Therefore, it seems logical that the pedodiversity of an area could be calculated by considering the simultaneous variation of multiple soil properties within that area. With the aim of creating a bridge between these two lines of thinking, this study presents the development of two new indices of pedodiversity (HULLdiv and EIGENdiv) which consider continuous variables as input.

The soil input information tested was a) Visible and Near Infrared (Vis-NIR) reflectance values and b) values of five soil properties predicted from Vis-NIR spectra. Both indices were employed to measure the pedodiversity at different extents in two perpendicular transects containing 27 (North to South) and 22 (East to West) locations respectively in New South Wales (NSW), Australia. Each location considered natural and intervened land use. The indices successfully represented the pedodiversity of the area of study, however they were different depending on the input soil information i.e., raw Vis-NIR and predicted properties. HULLdiv was affected by extreme soil observations and as a result its discrimination power between areas with different soil diversity was inferior.

On the other hand, EIGENdiv represented well the pedodiversity of an area, despite the extreme observations. The results showed good agreement with conventional methods for assessing pedodiversity i.e., Shannon's and Simpson's indices. However, the new indices were more discriminating by being able to better represent the landuse effect. This study represents the first attempt to measure pedodiversity in a continuous way using Vis-NIR information.

The previous results are presented in Fajardo et al. (2017). This work will give an insight of the practical use of these indices as well as examples using other datasets not presented in the original publication.

Fajardo, M.P., McBratney, A.B., Minasny, B., 2017. Measuring functional pedodiversity using spectroscopic information. CATENA 152, 103-114.

**keywords:** Pedodiversity, Functional Diversity, Multidimensional space, Convex Hull, Eigenvector
Slakes: A soil aggregate stability android application

Mario Fajardo – The University of Sydney, Australia

Alex McBratney – The University of Sydney, Australia

Budiman Minasny – The University of Sydney, Australia

Damien Field – The University of Sydney, Australia

We have developed a new methodology for the assessment of soil slaking in the form of a
smart phone application (Android). First, we had developed an image recognition algorithm that
measures the projected area of soil aggregates immersed in water at regular intervals. Our results
showed that the kinetics of the slaking process can be effectively modelled using a three coefficients
model (a, b and c), which are closely related to selected soil properties and land-use.

Coefficient a, is equivalent to the maximum slaking potential of the samples, and is found to be linearly related to exchangeable sodium, pH, clay percentage, calcium/magnesium and total carbon/nitrogen, and non-linearly related to total carbon. The coefficients b and c reflect the initial slaking and the slaking rate respectively and these were found to be linearly related to nitrogen and total carbon.

The originally methodology was presented in Fajardo et al. (2016) using a dataset covering great part of the agro-ecological variability of New South Wales (NSW), Australia. In this dataset, the coefficient a, was significantly lower in the natural sites reflecting a higher aggregate stability in those soils.

Having observed the potential of the methodology, we developed a smart phone application that is capable to measure the projected area of soil aggregates immersed in water in time, fit a model and return coefficients that can be finally used as a soil aggregate stability indicator.

Fajardo, M., McBratney, A.B., Field, D.J., Minasny, B., 2016. Soil slaking assessment using image recognition. Soil and Tillage Research 163, 119-129.

keywords: Android, Image recognition, Soil Aggregate stability, Digital Soil Morphometrics