# Predicting of soil properties with geostatistical and statistical models using a stratified regular sampling grid

Renato C. Taques – Incaper, Brazil

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

Eliana de Souza – Federal University of Viçosa, Brazil

Márcio R. Francelino – Federal University of Viçosa, Brazil

Nerilson T. Santos – Federal University of Viçosa, Brazil

The challenging steps of mapping soil include defining a sample design, finding suitable co-variates and selecting a predictive model. The primary objective of this study was to assess the accuracy of soil property predictions (pH, CEC, OM, clay, silt and sand) by assessing geostatistical (Ordinary Kriging (OK) and Regression-kriging (RK)) and statistical (Multiple Linear Regression (MLR) and Random Forest (RF)) models with a stratified sampling grid. The secondary objective was to determine which digital elevation model (5, 10, 30 or 90 m spatial resolution) derived the highest number of morphometric covariates correlating with soil properties. This study was carried out in the Rio Pongal Basin (~14,000 ha) which is located on the southern coast of the State of Espírito Santo in Brazil. Soil was collected from a depth of 15 to 30 cm using a sample scheme which combined 118 samples from a regular hexagonal grid (750 m/side) and 36 samples from a regular grid (<750 m/side). For model assessment, 20% of samples were used validation in an external validation. A total of fourteen maps of terrain attribute were derived from each DEM: elevation, slope, aspect, curvature, profile of curvature, multiresolution ridge top flatness (MRRTF), multiresolution valley bottom flatness (MRVBF), mean slope position (MSP), terrain ruggedness index (TRI), topographic wetness index (TWI), total solar radiation, direct solar radiation, diffuse solar radiation and duration of direct solar radiation. Using the t Test the correlation of terrain covariates and the soil attributes (p-value <0.001) was performed and it was selected the DEM of 5m as it presented the highest number of covariates correlated with the soils. A data mining approach considered correlation and collinearity analyses to select the most important set of predictive covariates among those derived from the digital elevation model (DEM), gamma spectrometry images and satellite imagery of Landsat-8. The data mining was used to select covariates for predicting soil property using RLM and RK, while prediction using RF model employed all covariates. The morphometric covariates were predominant in the group of selected predictors, for all soil properties and it was the only group of predictors for clay. Covariates derived from gamma spectrometry images were selected as predictors for three out of six soil attributes, while covariates generated with spectral data from the satellite image were selected as predictors for all soil attributes. For pH and silt, the selection combined predictors from morphometric, spectral and spectrometric data. The soil spatial variability analysis showed that with exception of pH, which showed no spatial pattern, all other variables showed spatial dependence for semivariogram adjustment. pH presented pure nugget effect, hence, it was only predicted using RLM and RF models. A part of pH, which had no marked difference in prediction using RF and RLM, for all other properties, predictions showed best performance by RF, followed by RK and MLR models. The best model (RF) explained round 20% of the variance of sand and silt, while for CEC, clay and OM, variance explained was 53%, 32% and 30% respectively. With this study, we conclude that using adequate sampling density, the success of predicting soil properties depend on the choosing model.

**keywords:** digital soil mapping, regular grid, sampling density, spatial variability, organic matter
Performance of a Less Expensive Radiometer for Estimating Soil
Organic Carbon

Alexandre ten Caten - UFSC, Brazil

Ricardo Simao Diniz Dalmolin - UFSM, Brazil

Andre Carnieletto Dotto - UFSM, Brazil

Jean Michel Moura Bueno - UFSM, Brazil

Geisy Bahls Fogaca - UFSC, Brazil

Luiza Mantica Kreimeier - UFSC, Brazil

Soil is a very valuable natural resource. As the interface of all Earth spheres it plays a vital role to human survival, being the main source of food and of ultimate importance for atmospheric carbon sequestration. The main objective was to teste the capabilities of a lower cost spectrometer, operating in the 325-1075 nm, in providing valuable data to predict SOC through chemometric techniques. Soil samples were collected at 44 locations in central region of Santa Catarina State, Brazil. Sampling followed Globalsoilmap.net specifications from 0 to 200 cm earning a total of 249 samples (174C/75V). SOC (%) was determined through Walkley-Black wet digestion. For reflectance measurement all air-dried grounded samples were scanned using a FieldSpec HandHeld II spectrometer. For chemometric modeling high noise-to-signal ratio at the edges of spectra were removed (325-400 and 980-1075 nm), thus reflectance values from 400-980 nm were used. A set of nine spectral preprocessing techniques were tested: Smoothing, Binning, Absorbance, Detrend, Continuum Removal, Savitzky-Golay Derivatives, Standard Normal Variate, Multiplicative Scatter Correction, and Normalization. Followed by six spectroscopic modeling Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR), Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Network (ANN) and Kernel-Based Memory Learning (KBML). All data handling was carried out through a graphical user interface (GUI) called AlradSpectra developed in R programming language available at <https://github.com/AlradSpectra>. The attribute soil texture characterizes those soil samples as being mainly of Clay soil class. SOC varied from 0,02 - 4,83%, reaching those high carbon contends due to constant resupply of new organic material in vegetated areas in the more shallow samples. Predictions of SOC with Binning preprocessing coupled with SVM modeling reached the best results for this study with a R²≤0,80 and RMSE≤0,49%. Predictions using PLSR reached from second to fifth best results, with averaged results of R²≤0,72 and RMSE≤0,56%. PLSR reached good results nearly independently of the preprocessing technique. Prediction of SOC with ANN and MRL earned the worst results in between all tested models. Those results are very significant, especially if one takes into consideration that the spectrometer (~16,000 USD) used in this study, is four times less expensive than the more widely used spectrometer (~64,000 USD) capable of collecting a broader range of the spectrum. In addition, spectral data collected with such less expensive spectrometer can still yield better results through research in different accessories-spectrometer combinations, besides preprocessing and modeling strategies.

keywords: Diffuse reflectance spectroscopy, Proximal soil sensing, Soil reflectance
Proximal soil sensing – steps needed to be taken from research to real-world applications

Thomas Terhoeven-Urselmans – SoilCares Research, Netherlands

Herman Vedder – SoilCares Research, Netherlands

Dale Fletcher – University of Waikato, New Zealand

Sam Sarjant – SoilCares Research, Netherlands

Peter van Erp – SoilCares Research, Netherlands

Proximal soil sensing has great potential to satisfy worldwide demand for soil analytical and advisory services. Advantages include easy sample and sensor handling, low requirements on competence level of sensor operators, low investment and operating costs, and high reproducibility. Nevertheless, in order to migrate from research to real-world applications, the following crucial building blocks are required: i) a database of calibration samples, ii) well standardized wet-chemistry lab practises, iii) selection of stable spectrometers, iv) validated sample handling and scanning procedures, v) derivation of prediction models, and vi) monitoring of spectrometers in network and spectral quality. SoilCares Research developed the so-called “Lab-in-a-box” (LIAB) for soil testing by following these building blocks. The LIAB is based on sensor fusion of mid-infrared (MIR) and X-ray fluorescence (XRF) spectroscopy. The LIAB is used to produce information on soil composition, field and crop specific fertilization, and management recommendations. The fusion prediction models employed by the LIAB are hosted as a cloud service and are updated monthly. Model performances – r2, RMSEP (root mean standard error of prediction), and bias – on an independent validation set at the end of 2016 (n≤467-1230 depending on the soil property) are as follows: soil organic carbon (0.97, 3.1 g/kg, 0.6 g/kg), pH value (0.90, 0.4 pH units, 0.04 pH units), clay content (0.95, 3.4 g/kg, 0.2 g/kg), Mehlich-3 extractable P (0.78, 39 mg/kg, 8.6 g/kg), and total Sulphur (0.91, 0.11 mg/kg, 0.05 mg/kg). The spatial point density of calibration samples directly influences the RMSEP. With 20% point density, the RMSEP is on average 24% larger (ranging from 11-42%) than 100% point density for the above elements, although 60% or larger point density has little difference to 100%. The RMSEP is also range dependent for many elements. Fertilization recommendation schemes often rely on soil property range classes to produce single recommendations for the range instead of the absolute value. Because of this farmers and advisors find the percentage of correctly predicted samples per class the most important indicator. The LIAB is a scientifically founded, real-world application that provides information to about 15 customers in 10 countries. In this presentation we further expand upon the results obtained so far and the remaining challenges.

keywords: soil, mid-infrared spectroscopy, xrf-spectroscopy, sensor fusion, fertilization
Spatial Variations of Soil Organic Carbon Stocks and the Related
Environmental Factors in Volcanic Ash Soils in Northern Taiwan

Chun-Chih Tsui - Department of Agricultural Chemistry, National Taiwan University, Taiwan

Xiao-Nan Liu - Department of Agricultural Chemistry, National Taiwan University, Taiwan

Chen-Chi Tsai - Department of Forestry and Natural Resources, National Ilan University, Taiwan

Zueng-Sang Chen - Department of Agricultural Chemistry, National Taiwan University, Taiwan

It has been recognized that volcanic soils can store large amount of soil organic carbon (SOC). The Yangmingshan National Park (YMSNP) is the only one volcanic park in Taiwan which is characterized by complex terrain across the whole area (~11,000 ha). The objectives of this study were to estimate the SOC stocks to 1 meter depth of YMSNP and to evaluate the impact of some environmental factors on SOC stocks. Soil organic carbon, bulk density and stone content of different depths for 83 pedons and some of their environmental factors were collected from different landscape positions and vegetation types within YMSNP. The semivariograms were characterized and modeled using the GS+ software package. Parameters of the best fitting variogram models were used in the ordinary kriging interpolation to create the distribution maps of SOC stocks by using ArcGIS. Topographic wetness index (TWI), topographic ruggedness index (TRI) and slope were derived from a 20m-DEM. MODIS-based NDVI (normalized difference vegetation index) was used as a proxy of vegetation cover. Representative landform and aspect of each pedon was decided based on former studies in YMSNP. We used a generalized linear mixed model (GLMM) to evaluate the effects of elevation, aspect, slope, TWI, TRI, SOC content and NDVI on SOC stocks by performing the GLIMMIX procedure in SAS software package. The estimates of SOC stocks were 9.29 ± 0.43 kg C m⁻² for 0-30 cm, 12.68 ± 0.60 kg C m⁻² for 0-50 cm, and 17.09 ± 0.90 kg C m⁻² for 0-100 cm, respectively. Spatial distribution of the SOC stocks showed that higher values were occurred in the southern and western part of YMSNP, while soils in the northern and eastern part had relatively lower SOC stocks. The total SOC stock in the upper 1 m of soils in YMSNP is 1.55 Mt (megatons) and the average value is 137 ton C ha⁻¹. Landform, aspect, elevation and SOC content were controlling factors of SOC stocks regardless of the soil depth (p < 0.05). NDVI significantly affected SOC stocks in the upper 50 cm of soil pedon as well; on the other hand, slope also exerted a significant influence on SOC stock in deeper soil layers. A former research indicated that the elevation was an effective factor for predicting SOC stocks of YMSNP, and our study further confirmed the influence of other environmental factors in the study area.

keywords: soil organic carbon stock, volcanic ash soil, spatial distribution, environmental factors
Estimation of current soil organic carbon stocks and evaluating
the carbon sequestration rates under different management prac-
tices in arable soils of Taiwan

Chun-Chih Tsui - Department of Agricultural Chemistry, National Taiwan University, Taiwan

Shih-Hao Jien - Department of Soil and Water Conservation, National Pingtung University, Taiwan

Zueng-Sang Chen - Department of Agricultural Chemistry, National Taiwan University, Taiwan

Horng-Yuh Guo - Agricultural Chemistry Division, Taiwan Agricultural Research Institute, Council of Agriculture, Taiwan

Accurate estimates of soil organic carbon (SOC) stocks is of crucial importance to detect the potential for carbon sequestration rate or emission rate under climate change. For Taiwan, spatial distribution map of SOC stocks has not yet been compiled. This study was the first attempt to generate a digital mapping of SOC stocks of whole arable soils in Taiwan by using a detail survey database (n > 110,000) which was conducted by Taiwan Agricultural Research Institute (TARI) between 2000 and 2010. Soil pedons were sampled by auger along a 250 m × 250 m cell-sized grid in the field, in other words, every 6.25 ha of the arable land has a representative soil pedon. The results showed that the average SOC stock in the upper 15 cm of arable soils was 2.86 ± 1.43 kg m⁻², 2.57 ± 1.44 kg m⁻² in the 15-30 cm layer, and 4.56 ± 2.99 kg m⁻² in the 30-50 cm layer. The total SOC stocks of arable land in Taiwan were 50.0 Tg, 86.7 Tg and 107.0-133.4 Tg in the 0-30, 0-50 and 0-100 cm soil layers, respectively. SOC sequestration rates of applying various practices in cultivated soils were estimated based on two long-term experiment studies in Taiwan. The annual increase rates of SOC was 0.46 ton C ha⁻¹ yr⁻¹ for organic farming and 0.83 ton C ha⁻¹ yr⁻¹ for applying compost with commercial inorganic fertilizer in the topsoil. Therefore, 0.74 to 1.33 million tons of carbon per year can be sequestrated by the cultivated soils in Taiwan. The COP21 Climate Summit in Paris recommended the "4 per 1000" program which advocate for increasing the global SOC pool by 0.4% per year. If the SOC stock of all the topsoil (0-30 cm) in Taiwan was estimated as 152.5 million tons, then the annual increase of SOC must be higher than 0.61 million tons (0.21 ton C ha⁻¹ yr⁻¹). According to our estimation, SOC sequestration rate of various practices in cultivated topsoil of Taiwan (0.46 to 0.83 ton C ha⁻¹ yr⁻¹) is much higher than that of global goal (0.21 ton C ha⁻¹ yr⁻¹).

keywords: soil organic carbon stocks, arable soil, carbon sequestration rate
High - fidelity mobile proximal soil sensing (350 -2500 nm) system

Nikolaos Tziolas – Aristotle University of Thessaloniki, Greece

Nikolaos Tsakiridis – Aristotle University of Thessaloniki, Greece

John Theocharis – Aristotle University of Thessaloniki, Greece

Eyal Ben-Dor – Tel Aviv University, Israel

George Zalidis – Aristotle University of Thessaloniki, Greece

Visible and infrared spectroscopy can effectively characterize the upper layer of the soil body and serves as a tool for proximal soil analysis. The spectra encode information on the inherent composition of soil, providing a unique capability to describe several important soil properties both quantitatively and qualitatively in a rapid, precise and inexpensive way.

This paper introduces a high fidelity mobile proximal soil sensing system (MSSS) allowing on-the-go measurements in open field conditions with a commercial full-range Vis-NIR spectroradiometer under standardized measuring conditions. For that purpose a mobile dark chamber with the ability to host any commercial spectroradiometer has been developed with a controlled and an independent illumination source that covers the effective spectral region of the spectroradiometer. The MSSS is towed by a tractor providing some advantages over conventional backpacker and soil penetrator systems such as the fast and high power data acquisition in a near- real time domain. The acquired data base can be then processed on board for the attribute in question and kriging into a thematic map, for the benefit of the end user.

The testing and validation of the measurement protocol have been carried out. To that end the incident light stability and homogeneity, the geometrical positioning of the fiber optic and limitations of the surface properties and dust evolved during the ride, have been studied and aligned in a series of test pilots.

A demonstration of the above MSSS capability has been carried out by modelling several soil properties (e.g. pH and soil organic matter) using a machine learning algorithm (Cubist) with one-leave-out cross validation. For that purpose we used proximal spectral data (60 samples) that were acquired from a test field in Greece, representing the demonstration area. The accuracy of the spectroscopic models ranged from R2 0.68 and RPD from 1.36. After extracting the high performance spectral based model we applied it to the spectral information obtained by the MSSS and generate a thematic maps of the selected soil properties. We encountered that soil roughness and moisture are playing a major role in the implementation of the calibration model and aimed at studding these effects further in the future in order to allow a reliable proximal sensing on-the-go application.

**keywords:** soil spectroscopy, mobile soil sensor, soil spectral library, proximal soil sensing, soil monitoring
Towards a pedogenic approach in quantitative soil sampling and
element stocks estimation

Martin Valtera - Silva Tarouca Research Institute for Landscape and Ornamental Gardening, Czech Republic

Pavel Šamonil – Silva Tarouca Research Institute for Landscape and Ornamental Gardening, Czech
Republic

Quantitative soil sampling for element stocks estimation is a time and cost demanding work, while comparability of the results is usually limited. The sampling is typically based either on collection of undisturbed soil samples of known volumes (core method), or on estimating the volume and the mass of soil excavated from a pit (excavation method; ISO 11272:1998). However, there are several limits of these methods that handicap their application in soil science. The separate core method usually underestimates the contents of larger stones (and roots) not included in the samples, while the excavation method requires complicated volume measurements due to cavities and asymmetries in the pit walls or sampling depths, which usually restricts soil sampling to fixed depth intervals. The proposed modified approach takes advantages of the two standardized methods, providing accurate estimation of bulk-densities, as does the core method, while maintaining the accuracy in coarse fragments quantification, as enables the excavation method.

The following variables can be measured for each soil horizon or sampling interval, and used for the estimation of element stocks: (1) element concentration in the fine earth; (2) bulk density of the soil fraction excluding stones (arbitrary classification); (3) the mass proportion of gravel ( 2 mm) in the stone-less fraction; (4, 5) total volumetric proportion of stones and living roots not included in the element stock; and (6) thickness of the soil horizon or sampling interval. Moreover, this method enables reliable estimation of element stocks even for wavy or irregular soil layers, such as often the diagnostic horizons, so the associated analytical data are also applicable for classification purposes and pedogenic inferences. Moreover, the separation of horizons is a key prerequisite for reliable interpolation of soil data within sampled intervals, thus enabling comparability of the results among studies and datasets that use different sampling depths. The proposed method may provide good performance especially in soils with low or moderate rock fragment contents, and/or with scattered occurrences of boulders, but may fail in very stony soils or soil layers, where the core extraction is not feasible.

keywords: bulk density, stone content, soil horizons, excavation method, sampling design
# NDVI stratified sampling based on soil homogeneous areas. An application to rice crop in Babahoyo canton-Ecuador

Omar Valverde - Ceigram Universidad Politécnica de Madrid, Spain

Alberto Garrido - Dept of Agricultural Economics and Social Sciences, ETSIAAB, UPM, Spain

Antonio Saa-Requejo - Dept of Agricultural Production, ETSIAAB, UPM, Spain

Ana Tarquis - Dept of Applied Mathematics, ETSIAAB, UPM, Spain

In index based insurance, it is very important to define a threshold on the index that correlates well with losses in the entire insurance area application. Sometimes, the application area, usually defined by political boundaries, includes heterogeneous zones mainly in soil properties. For this reason, it is necessary to consider these possible differences in the study area, before to establish one global representative index thresholds.

In a previous study (Valverde-Arias et al., 2017), we generated an Agro-ecological Homogeneous Zones (AHZ) map in this region by principal component analysis. This map has defined seven areas with similar soil characteristics on rice crop in Babahoyo canton. We used AHZ as stratification criterion to find out, if we should use one index for the entire study area, or if we need to define more than one index.

From the seven AHZ, we only kept the more representative of them, which were f7 and f15 that reach the 91% of the total rice crop area in this canton. Over the area of f7 and f15, we generated random points that cover at least the 10% of the study area. NDVI_16days_Layer (MODIS_MOD13Q1V6) imagery set were downloaded and processed, 2 images for each month during the crop cycle (January to May) during 14 years (2001 to 2014) that included campaigns with low rice yield due to drought. The corresponding Normalized Difference Vegetation Index (NDVI) values were calculated for the sample points in each year, as an index to evaluate the rice crop stage. On this set several statistical analyses were done.

The results distinguish years in four categories according accumulated NDVI (NDVI acc) in low, low medium, upper medium and high corresponding statistically with the yield registered in those years. NDVI acc at f7 (2.32) and at f15 (2.46) areas were statistically different pointing out the importance of soil properties. At the same time, f7 area accumulated more low years (4 years) than f15 (0 years), which shows that f7 area is riskier than f15 for rice crop. The implications of these results will be discussed in the based index insurance context.

## References

Valverde Arias, O., Garrido, A., Villeta, M. and Tarquis, A.M. Homogenisation of a soil properties map by principal component analysis to define index agricultural insurance policies. Geoderma, 2017, https://doi.org/10.1016/j.geoderma.2017.01.018.

Valverde Arias, O., Tarquis, A.M. and Garrido, A. Rice crop risk map in Babahoyo canton (Ecuador). Geophysical Research Abstracts, 18, EGU2016-614, 2016.

**keywords:** Soil, sampling, stratify, NDVI, index-based insurance, rice
Towards high-resolution modeling of global hydrology and the
added value of detailed soil information

Rens L.P.H. van Beek - Department Physical Geography, Utrecht University, Netherlands

Lars Killaars - Department Physical Geography, Utrecht University, Netherlands

Arno Bouwman - PBL Netherlands Environmental Assessment Agency, Netherlands

Michel Bakkenes - PBL Netherlands Environmental Assessment Agency, Netherlands

Stefan van der Esch - PBL Netherlands Environmental Assessment Agency, Netherlands

Ben ten Brink - PBL Netherlands Environmental Assessment Agency, Netherlands

Jetse Stoorvogel - Soil Geography and Landscape Group, Wageningen University, Netherlands

Edwin H. Sutanudjaja - Department Physical Geography, Utrecht University, Netherlands

Niko Wanders - Department Physical Geography, Utrecht University, Netherlands

Ruud J. van der Ent - Department Physical Geography, Utrecht University, Netherlands

Joyce H.C. Bosmans - Department Physical Geography, Utrecht University, Netherlands

Marc F.P. Bierkens - Department Physical Geography, Utrecht University, Netherlands

Towards high-resolution modelling of global hydrology and the added value of detailed soil
information

Over the past decade global hydrological models been applied increasingly at finer spatial resolutions and under more detailed scenarios to investigate the impact of global environmental change. Increasingly, scenarios do not only focus on anthropogenic climate change but also explore expected trends in water demand and land cover change. This provides more detailed information on future pathways and allows for the identification of potential hotspots of water scarcity. Yet, these detailed scenarios are not balanced by information on soil conditions. Typically, global scale hydrological models continue to rely on the coarse, pedon-scale information FAO Digital Soil Map of the World or use regional datasets such as CONUS. As a consequence, the quality of the information is globally inconsistent and disconnected from the more detailed information on land cover and land use, which is available at the local scale. In a recent development, the information of the FAO Digital Soil Map of the World has been downscaled using local information (e.g., S-World, Stoorvogel et al. 2016). This leads potentially to a more detailed soil parameterization that can be consistently linked to changes in the land cover conditions.

In order to investigate the sensitivity of a global-scale hydrological model to an improved soil parameterization and to quantify the added value in terms of local information and uncertainty, we apply the global-scale hydrological model PCR-GLOBWB with different parameterizations derived from the S-World product and existing pedotransfer functions (Balland et al., 2008). This allows us to quantify the soil parameterization at different resolutions, ranging from the original FAO pedon-scale to 5 arc minutes, and explore the resulting changes in terms of water availability and demand for different scenarios; to this end, we combine soil conditions with combinations of SSPs and RCPs representing the consensus of future developments of socio-economic developments and climate change over the 21st century. In this way, we are able to quantify the added value of more detailed soil parameterizations in comparison to the expected changes of climate change, land cover change and water demand. On a basin scale, changing soil conditions has a small but appreciable effect on the median discharge that is of the same order as that of land cover change but distinctly lower than that of climate change. However, the influence of changing soil conditions becomes more important when considering low flows, i.e. under drought conditions. This change can be explained by the ability of the soil to accommodate rainfall, leading to a change in the overall runoff volume and the partitioning of net rainfall into fast runoff at the surface or delayed runoff through the groundwater system. This also explains the associated changes in soil moisture storage and water availability to crops which is amplified locally. With this information, we can improve
the representation of soils in global hydrological models and inform policy makers on consistent trends and the underlying uncertainty.

**keywords: Soil parameterization, Global hydrological models, Pedotransfer functions, Scenario modelling, Sensitivity analysis**
# Proximal landfill sensing: new research grounds for high-resolution electromagnetic induction surveys

Ellen Van De Vijver - Ghent University, Belgium

Marc Van Meirvenne - Ghent University, Belgium

Landfills are the ultimate example of man-made soil. A bottom-up inventory performed in 2015 revealed that Europe counts between 150 000 and 500 000 landfills. The vast majority of these sites date from before the current stringent regulations on landfill construction and management. Because of lacking environmental protection measures, many are likely to head for costly remediation scenarios. Former landfills typically were created outside the boundaries of urban settlements. However, due to the progressive urbanization in the recent decades, many landfill sites are now being included in urban (re)development projects. The spatial heterogeneity of landfills poses obvious objections to conventional localized sampling, in addition to the risk of damaging possible landfill liners preventing contamination through leaching. Non-invasive data collection via proximal sensing can provide high-resolution images of the landfill subsurface. The first applications of EMI for landfill investigation go back to the early 1990s (De Carlo et al., 2013), yet, most previous studies focus on landfill-related environmental problems as groundwater contamination and methane gas emissions. The recently established concept of enhanced landfill mining (ELFM) provides an incentive to aim attention to landfill characterization in function of sustainable site redevelopment and potential resource recovery (Jones et al., 2013).

This paper presents a compilation of four case studies on the value of high-resolution electromagnetic induction surveying in view of ELFM. The landfills considered differ in size (from 1 to 10 ha), construction setting (waste disposal in wetland areas to backfilling of former quarries) and type of waste (construction and demolition debris to municipal solid waste). Challenges are identified in terms of practical implementation, data processing and interpretation and opportunities are highlighted for the general application of proximal soil sensing in complex urban environments.

## References

De Carlo, L., Perri, M. T., Caputo, M. C., Deiana, R., Vurro, M., and Cassiani, G. (2013). Characterization of a dismissed landfill via electrical resistivity tomography and mise-à-la-masse method. Journal of Applied Geophysics, 98, 1-10.

Jones, P. T., Geysen, D., Tielemans, Y., Van Passel, S., Pontikes, Y., Blanpain, B., Quaghebeur, and Hoekstra, N. (2013). Enhanced Landfill Mining in view of multiple resource recovery: a critical review, Journal of Cleaner Production, 55, 45-55.

**keywords:** Landfill investigation, Multi-receiver electromagnetic induction, Enhanced landfill min-
ing, Urban redevelopment
# Characterizing soil organic matter, soil texture, and soil redistribution patterns in a frequently burned and a rarely burned prairie landscape

Ilona van der Kroef – Wageningen University, Netherlands

Cathelijne Stoof – Wageningen University, Netherlands

Arnaud Temme – Kansas State University, USA

The determination of spatial patterns of soil properties is encountering challenges such as short distance heterogeneity. Although, Statistical modelling of soil characteristics with landscape properties has proved to be a promising tool, our knowledge of soil-landscape relations remains incomplete. One complicating factor is the fact that such relations differ substantially between climate zones, between geological settings and between ecosystems. To increase our knowledge, and to aid statistical modelling of soil characteristics, I have examined the patterns of and relations between soil characteristics, soil redistribution processes (e.g. erosion and deposition), and landscape properties within two watersheds in a Long-Term Ecological Research Site, the Konza Prairie, Kansas.

The Konza Prairie is a semi-natural and frequently burned tall grass prairie, which is subdivided in 65 watersheds with different grazing and burning management. The studied watersheds are similar in geology, topography and hydrology, but fire frequencies vary between yearly and once every 20 years. To characterize and quantify soil characteristics (e.g. soil organic matter and texture) and soil redistribution, composited soil and bulk density samples were taken on the basis of a Conditional Latin Hypercube sampling design. During this field work, a range of soil and landscape properties were as well visually evaluated. Afterwards, the composited soil samples were analysed on water content, root mass fraction, stone fraction, soil organic matter, soil texture, and Plutonium-239 activity within a laboratory. These lab established characteristics, the field analyses, and auxiliary data were combined in a database and analysed. Statistical analyses included among others ANOVA, linear regression, regression kriging and spatial autocorrelation.

The presented results encompass information on soil organic matter, soil texture, soil redistribution patterns related to topography (e.g. slope and curvature) and hydrology (e.g. Topographic Wetness Index). These illustrate the impact of burning on soil characteristics at a landscape scale. Finally, I will discuss possible drivers causing variation in soil-landscape relations between previous research and the two watersheds.

**keywords:** Soil organic matter, SOM, Soil texture, Soil erosion, Soil deposition, Topography, Slope, Fire management, Kansas
Topography reconstruction in eroding landscapes – a case study
from a hummocky ground moraine in NE Germany (CarboZALF-
D)

Marijn van der Meij - Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Soil Landscape Research, Netherlands

Arnaud Temme - Kansas State University, Department of Geography, USA

Jakob Wallinga - Wageningen University, Soil Geography and Landscape group, Netherlands

Wilfried Hierold - Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Soil Landscape Research, Germany

Michael Sommer - Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Soil Landscape Research, Germany

Erosion processes often change the topography to a point where the original landforms are not
recognizable anymore. This forms a problem in soil and landscape evolution modeling, because the
evolving topography might change the direction and rates of soil and landscape forming processes.
Several studies used the truncation of soil profiles to reconstruct the initial topography, with a
constant initial soil thickness as reference. However, the effects of this constant thickness on the
quality of the reconstructed topography were not quantified.

We present a set of paleotopographies for the hummocky terrain of ground moraines in north-
east Germany. The closed kettle hole catchment of landscape laboratory CarboZALF-D, with more
than 260 profile descriptions, enabled us to develop a mass balance of erosion on the slopes and
sedimentation in the central depression. Next to that, we used several fixed initial soil thicknesses
as reference to estimate erosion. These thicknesses were derived from soil profiles which can be
regarded as non-eroded. The amount of erosion or sedimentation was estimated by comparing
the reference soil thickness with interpolated current soil and colluvium thicknesses. A special
emphasis was put on how to handle censored soil descriptions. The reconstructed topographies
were validated using cross-validation and by counting the amount of mispredictions of erosion and
sedimentation positions.

The reconstruction with the average reference soil thickness of 1 m gave the best result. This depth was comparable with the average undisturbed soil thickness observed in the surrounding regional Quillow catchment (250 km²) and is in line with a global trend between decalcification depths and initial CaCO3 content and rainfall. However, the assumption of a constant initial thickness induced errors on locations where the real initial thickness was lower. Also the small scale variations in erosion or sedimentation patterns provided errors in the reconstruction. The large data set from CarboZALF enabled the identification of these small patterns. Random subsets of the data did not result in higher errors, but did increase the variation in estimated eroded and deposited mass. Erosion processes smoothed the slopes and filled the hillslope sinks and the former wetland in the depression, impacting the hydrological system. Recent erosion seems to contribute only for a small part to the total erosion and sedimentation in the area, indicating that past erosion events played a much more dominant role than previously thought.

keywords: paleotopography, CarboZALF-D, Erosion, Landscape change
A new era: drone-borne gamma ray surveying to characterize soil

Steven Van der Veeke - Medusa Sensing BV, Netherlands

Han Limburg - Medusa Sensing BV, Netherlands

Marco Tijs - Medusa Sensing BV, Netherlands

Henk Kramer - Wageningen Environmental Research & ISRIC - World Soil Information, Netherlands

Jappe Franke - Wageningen Environmental Research & ISRIC - World Soil Information, Netherlands

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

Soils and sediments can be characterized or “fingerprinted” by their content of naturally occurring radioactive isotopes of potassium, uranium and thorium. These isotopes emit small but measurable amounts of gamma radiation. This radiation can be measured in the lab as well as in the field using a gammaspectrometer. The amount of radiation is a direct measure of the texture of the topsoil (0-30 cm). Gamma radiation mapping therefore is a valuable tool to gain insight in the textural variation of for instance agricultural fields.

The last two decades, the use of gamma-ray systems is slowly becoming a commodity in agricultural mapping. However, present gammaspectrometers are still relatively bulky and need expert knowledge to operate and interpret data. Still many advancements can be made, both in instrumentation hardware and software, as in interpretation of the radiation data. Present systems are mostly used “landborne” or proximal, that is mounted on a vehicle. With the advent of commercially useable drones (UAVs) however, the call for systems that can be used underneath such a small flying frame has been firmly increasing. UAVs provide the possibility to develop relatively low budget and flexible gamma radiation sensing from the air.

Application of gammadetectors underneath drones poses some severe challenges to the sensor hardware. Drones have (very) limited payload and power capacity, imposing serious restrictions on the weight and power consumption of the sensors used. At Medusa we have picked up this challenge by developing a lightweight and low power drone detector system that can be used under commercially available drones. The overall weight of the system has been reduced by more than a factor 2, without compromising data quality. And – maybe more importantly – we have automated and integrated a large part of the processing needed to translate the measurements to useable data into the system itself. The system has an embedded webserver that allows any wifi-enabled device such as a smartphone or tablet to connect and inspect its operational status. It performs on-the-fly full spectral analysis of the radiation data allowing for direct inspection of radionuclide concentrations.

The integration of the system with a commercial drone (DJI S1000+) has been carried out in collaboration with Wageningen Environmental Research (WUR). The paper will present the system and the first results of the tests.

**keywords:** Gammaspectrometer, UAV, soil sensing, gamma radiation, full spectrum analysis, airborne surveying
Comparing UAV airborne and proximal measurements of a gam-
maspectrometer for soil texture mapping

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

Martin Knotters - Wageningen Environmental Research, Netherlands

Ronald Koomans - Medusa Explorations, Netherlands

Dennis Walvoort - Wageningen Environmental Research, Netherlands

Han Limburg - Medusa Sensing, Netherlands

A gammaspectrometer is a proven geophysical tool or soil sensor for mapping soil textural
properties at high resolution at field level. It measures the concentration of radionuclides in the
(top)soil (0-30 cm) which is a proxy for soil texture in areas with similar provenance. However, the
use of this technique depends on the accessibility of fields for the vehicles carrying the sensors. Being
able to apply soil sensors by using a UAV instead of a quad, tractor, airplane or helicopter increases
their applicability. Using an UAV ("drone") is more flexible and better suited to smaller areas than
an airplane. And it can fly when crops or natural vegetation are present or when difficult driving
conditions prohibit the use of a vehicle. To evaluate the usability of a gammaspectrometer for UAV
application we designed a comparison study for the prediction of physical soil properties measured
by a gammaspectrometer under an UAV and on a tractor in a 40 ha (8 fields) area in Flevoland,
the Netherlands. The study area is situated in a 60 year old polder made on top of Pleistocene
sands. The tillage layer and gammaspectrometer depth range of 30 cm has 0 - 20 % clay. Land use
is arable land and pasture. The area is measured with a MS2000 gammaspectrometer on a tractor
and a MS1000 gammaspectrometer on a DJI S1000+ Spreading wings UAV. For calibration, 15
samples are taken and analysed in the lab on % clay, % silt, grain size, organic matter content and
on the concentration of radionuclides. The spectral dataset of the gammaspectrometer on the UAV
is corrected for elevation. Both spectral datasets are analysed using full spectrum analysis and
translated to soil parameters using regression analysis. An independent set of 44 validation samples
was taken and analysed in the lab on the same properties. Sample locations were allocated using
stratified random sampling. Using these data we can compare the two gamma-ray measurements
on their ability to predict physical soil properties, their footprint and accuracy. The ground
borne dataset is used as a reference. The effect of elevation and the difference in spatial and
spectral resolution is assessed. A comparative cost analysis is performed, comparing the cost
of the methodology (euros) to the acquired detail, the accuracy of the result and the benefit of
not disturbing the land. This enables a user to have a well-considered choice of soil mapping
methodology.

keywords: Gammaspectrometer, UAV, Proximal, Soil sensing, Soil texture, Comparison, Validation
# Approaches for commercial Digital Soil Mapping, examples from South Africa

George van Zijl - Digital Soils Africa, South Africa

Digital soil mapping (DSM) has been in the production phase for several years, allowing for
several industries to benefit from high quality soil mapping products created with considerable cost
savings. Unfortunately, the uptake of commercial DSM in South Africa has been slow, and local
industry has not yet harnessed the benefits of the technology. Over the last 13 years local research
to make DSM commercially viable have produced three different approaches, each with unique
strengths and weaknesses, to fulfil the needs of entities requiring affordable, timeous spatial soil
information. In this project, we compare the three approaches, land type disaggregation, expert
knowledge and geostatistical methods, to each other on a site with a simple (Cathedral Peak) and a
site with a complex soil distribution pattern (Ntabelanga). The Cathedral Peak site is overlain with
basalt, has a uniform topography and the soil database comprises 59 soil observations. Ntabelanga
is covered with siltstone, sandstone and dolerite, has a varying topography and the soil database is
larger with 87 observations. The aim was to map soil classes for each of these sites. At Cathedral
Peak, the land type disaggregation approach achieved a Kappa value of 0.66, expert knowledge
0.57 and the best geostatistical method 0.53. It must be noted that the land type disaggregation
method produced a map with only three mapping units, compared to the six of the other two
approaches. Despite the relatively good results of the geostatistical approach, comparison between
the map and the soil surveyor's knowledge of the area showed little resemblance. At Ntabelanga the
picture differs. The kappa values were 0.2 (land type disaggregation), and 0.57 each for the expert
knowledge and geostatistical approaches. Again, the land type disaggregation method had less
map units, but the geostatistical method resembled the soil surveyor's knowledge better than the
expert knowledge approach. The results show that the three different approaches are suitable for
different scenarios. Although the land type disaggregation does not require field work, it only gives
a general idea of soil distribution in landscapes with a simple soil distribution pattern. The expert
knowledge approach gives acceptable results, but requires a relatively small dataset. As the dataset
size increases, the geostatistical approach outperforms the expert knowledge approach. These three
approaches give the soil surveyor the necessary tools to produce soil maps for commercial use, using
digital soil mapping.

keywords: disaggregation, expert knowledge, geostatistics, soil survey, budget
# What is the value of understanding? Comparing mechanistic soil formation and geostatistical modelling.

Tom Vanwalleghem – University of Cordoba, Spain

Andrea Román Sánchez – University of Cordoba, Spain

Juan Vicente Giráldez – University of Cordoba, Spain

There is a need for better understanding the processes influencing soil formation and the result-
ing distribution of soil properties. Soil properties can exhibit strong spatial variation, even at the
small catchment scale. Especially soil carbon pools in semi-arid, mountainous areas are highly un-
certain because bulk density and stoniness are very heterogeneous and rarely measured explicitly.
In this study, we explore the spatial variability in key soil properties (soil carbon stocks, stoniness,
bulk density and soil depth) as a function of processes shaping the critical zone (weathering, ero-
sion, soil water fluxes and vegetation patterns). We also compare the potential of a geostatistical
versus a mechanistic soil formation model (MILESD) for predicting these key soil properties.

Soil core samples were collected from 67 locations at 6 depths. Total soil organic carbon stocks were 4.38 kg m⁻². Solar radiation proved to be the key variable controlling soil carbon distribution. Stone content was mostly controlled by slope, indicating the importance of erosion. Spatial distribution of bulk density was found to be highly random. Finally, total carbon stocks were predicted using a random forest model whose main covariates were solar radiation and NDVI. The model predicts carbon stocks that are double as high on north versus south-facing slopes. However, validation showed that these covariates only explained 25% of the variation in the dataset. Apparently, present-day landscape and vegetation properties are not sufficient to fully explain variability in the soil carbon stocks in this complex terrain under natural vegetation. This is attributed to a high spatial variability in bulk density and stoniness, key variables controlling carbon stocks. Similar results were obtained with the mechanistic soil formation model MILESD, suggesting that more complex models might be needed to further explore this high spatial variability.

keywords: soil carbon, stone content, soil depth, soil formation, mechanistic model, random forest
# Soil class mapping in the Quadrilátero Ferrífero, Brazil: a methodological approach of sampling and selection of covariates

Bruno N.F. Vasconcelos – Federal University of Viçosa, Brazil

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

Eliana de Souza – Federal University of Viçosa, Brazil

César S. Chagas – EMBRAPA, Brazil

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

João C. Ker – Federal University of Viçosa, Brazil

This study aims at evaluating methods of variable selection to predict soil class map from disaggregated soil map unit (SMU). The study was carried out for an pedological diverse area of 1,625.32 km², located on the Quadrilátero Ferrífero, Brazil. A legacy soil map, with 75 SMUs, at scale 1:50.000, was taken as reference to extract soil information and to derive simplified maps according to the categorical level of soil unities and similarity among components of soil associations. Three maps were generated with 9, 24, and 34 SMUs. A sampling density of one point per 5 ha was adopted, generating a total of 32.500 random points, over the area for each four SMU maps. A set of 141 maps associated with the soil formation factors (i.e. relief, climate, organisms and parent material) was used as soil covariates. The maps was derived from SRTM image, climatic data of WorldClim, Landsat-8 satellite imagery and data from Gama-ray spectrometry (thorium (Th), uranium (U) and potassium (K), additionally geomorphological, geological and maps of relieve maps were used, as well as spatial location represented by the geographic coordinates. Random forest classifier was applied for variable selection and to predict soil classes using R packages. Four methods of selection were evaluated, two within the Random Forest algorithm, Ginni index and mean decrease accuracy, and additionally the selection based on the percentile and the linear correlation. To assess the prediction's accuracy, a wall-to-wall comparison between the reference and the predicted maps was performed and analysed by means of the indexes from the confusion matrix. The kappa index ranged from 0.66 to 0.77. A significant decrease in the number of variables, with lower decrease of accuracy, was observed in all method evaluated. After the data mining, the largest number of variables represented 25% of the total variables. The most accurate soil map was predicted using 13% of the variables, selected by the percentile method, with 34 SMUs. The variability of data source on the set of variables selected strengthens the importance of using variable related to more than one soil formation factor. Geophysical variables of gamma ray spectrometry were selected with high frequency by different methods, highlighting its, yet not fully explored potential for digital soil mapping.

**keywords:** Digital soil mapping, Legacy data, Quadrilátero Ferrífero, Random forest, Soil map unit
Digital soil classes map of Minas Gerais State, Brazil

Bruno N.F.V. Vasconcelos – Federal University of Viçosa, Brazil

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

Eliana de Souza – Federal University of Viçosa, Brazil

Bruno A.F. Mendonça – Rural Federal University of Rio de Janeiro, Brazil

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

Sampling is a key issue in soil surveys as it has direct influence on the cost and quality of the mapping. Despite the effectiveness of Conditioned Latin hypercube (cLHS) in sampling design for digital soil mapping, the method is sensitive to disproportions in areas of soil classes. This disproportion generates unbalanced samples which hamper soil class mapping for areas with soils of low occurrence. This study proposes the use of cLHS as a sampling method, which balances the cost of sampling based on the size of the area of soil classes. The sampling method proposed was evaluated the State of Minas Gerais, southeast Brazil. The area is pedologically diverse, comprises three biomes, and has soil of all thirteen orders of the Brazilian System of Soil Classification. A legacy soil map of scale 1: 650,000 was used for extracting information of 19 soil classes classified until the second categorical level, additionally, water body, urban and mining areas was included on the classification. The cLHS-balanced sampling was compared to random sampling and to cLHS without balancing. We used five sampling densities: 1.500, 2.000, 5.000, 10.000, and 23.500 samples. Random Forest classifier was used to predict the soils, using as predictors maps of environmental covariates related to the parent material, climate, relief and spatial positon. The cLHS-balanced mapped all soil classes for all sampling density tested, while cLHS left out one class for sampling density of 1.000 and 2.000 samples, on the other hand, using random sampling resulted in the highest number of soil not mapped, ranging from one to five classes missing when using sampling density 5.000. Assessment through the error matrix for individual classes showed that cLHS-balanced presented lower error for most of classes compared to classification with other sampling schemes. Despite no statistical difference in kappa of the whole map, the cLHS-balanced was more efficient in predicting soil classes for the Minas Gerais State as all classes were mapped with lower uncertainty. We conclude that using sampling balanced by area improves the mapping of soil with low occurrence.

keywords: Random Forest, Sampling, Soil mapping
Modeling urbanization effect on soil functions in the New Moscow,
Russia

Viacheslav Vasenev – RUDN University, Russia

Jetse Stoorvogel – WUR, Netherlands

Sophiya Ibatulina – RUDN University, Russia

Olga Romzaykina – RUDN University, Russia

Ellen Moedt – WUR, Netherlands

Svetlana Kanaeva – RUDN University, Russia

Elvira Dovletyarova – RUDN University, Russia

Urbanization is responsible for substantial environmental consequences worldwide. Urban soils are substantially different from those in natural environment. Urban soils provide important functions and services, including fertility, carbon (C) sequestration, filtering and purifying surface run-off, performing as habitat of microorganisms and supporting biodiversity. Urban soils' formation and functioning are directly and indirectly influenced by human, which shall be considered when analyzing and modeling urban soils' features and functions. This study aimed to analyze the urbanization effect urban soils' functions in the New Moscow.

New Moscow is a recently urbanized 1500 km² areas, included into the boundaries of the Moscow city in 2012 to decentralize the urban development, mitigate the traffic congestion and environmental pollution. New Moscow project will result in substantial urbanization on lands, primarily covered by croplands and forests. Land-use maps for two time periods in 1980 and 2014 were created based on the available historical data, including topographic maps, satellite images and Open Street Maps. Nine different land-use categories, including urban functional zones were distinguished for each time period and land-use change was analyzed. Land cover dynamic (LCD) model was implemented to analyze the patterns in land-use change (with especial focus on urban functional zones) as a function of environmental and socio-economical factors, neighborhood and land-use history. The investigated land-use change patterns were extrapolated to predict future changes. The resulting map of future urbanization was overlaid with the Master plan of New Moscow urban development and the mismatches were analyzed.

The effect of future urbanization on soil functions was analyzed based on the existing soil map (Shishov and Voitovich, 2002) and soil data collected in New Moscow in 2015. Soils located in different functional zones and having different land-use history (e.g. parks located on prior forested areas or residential blocks, occupying prior fallow lands) were sampled to the depth of 0-150 cm. Chemical, physical and biological properties were analyzed in collected samples. Data on SOC stocks, pH, nutrient contents, contaminants' concentrations and microbiological activity were used to analyze such functions of urban soils as fertility, C sequestration and habitat for microorganisms. Land-use modeling and the analyzed functions of urban soils with different land-use history allowed predicting possible effects of urbanization on soil functions in the New Moscow.

**keywords:** digital soil mapping, land dynamics, urban soils, functional zones, carbon stocks, basal respiration, heavy metals
Estimation of Soil Profile Properties Using Field and Laboratory
VNIR Spectroscopy

Kristen Veum - USDA-ARS, USA

Kenneth Sudduth - USDA-ARS, USA

Newell Kitchen - USDA-ARS, USA

Diffuse reflectance spectroscopy (DRS) soil sensors have the potential to provide rapid, high-resolution estimation of multiple soil properties. Although many studies have focused on laboratory-based visible and near-infrared (VNIR) spectroscopy of dried soil samples, previous work has demonstrated that in-situ DRS may be an efficient and viable alternative to laboratory DRS. The objective of this study was to compare estimates of soil properties using VNIR spectra generated in the laboratory with an in-situ profile DRS spectrometer. Soil cores were obtained to approximately 1 m depth from multiple sites in the Midwestern United States, representing a wide range of soil types and agricultural management practices. At the time of core collection, in-situ profile DRS spectrometer scans were obtained at each sampling location. In the laboratory, soil cores were split by horizon, air-dried, sieved (< 2mm), and scanned with the laboratory DRS spectrometer. Laboratory measurement of soil properties including soil organic carbon (SOC), total nitrogen (TN), active C, and texture fractions is underway. Soil properties will be estimated from spectra using partial least squares regression and models will be compared using R2, RMSE, and RPIQ. If in-situ DRS performs well for profile soil property estimation, it may become a cost-effective and practical tool for large-scale soil investigations examining soil health, carbon sequestration, and other important topics related to environmental protection and agronomic sustainability.

keywords: proximal soil sensing, precision agriculture, soil spectroscopy, soil properties, re-
flectance spectra, in-situ sensing
# The cost-efficiency of methods for monitoring soil organic carbon stock

Raphael Viscarra Rossel - CSIRO, Australia

Dick Brus - WUR, Netherlands

Craig Lobsey - CSIRO, Australia

The Australian government has developed and legislated methodologies, based on either direct measurement or modelling, which can be used to recompense landowners for capturing and

accumulating additional C in soil.

To qualify for the direct measurement approach, proponents should measure the soil C stocks present within carbon estimation areas (CEA) by performing a baseline survey at the start of the project and then repeatedly over time to accurately quantify the magnitude of the temporal change in C stocks within the CEA. Currently, this method uses stratified random sampling using

compact geographic strata of equal area and composite sampling across strata. The composite samples are analysed in a laboratory to measure the soil organic C stock.

Alternatively, proximal soil sensing can also be used for baselining and monitoring soil C stocks. Mobile multi-sensor system can help design sampling strategies and map soil C, and visible-near infrared spectroscopy and gamma-ray attenuation can be used to measure soil organic C and bulk density, respectively.

Our aims here were to (i) quantify the soil organic C stock in two CEAs, one used for cropping and one for grazing, using the method based on compositing and laboratory analysis and a proximal soil sensing approach that uses sensor stratification and sensor measurements of the soil organic C stocks and (iii) compare the two schemes in terms of their precision and cost, i.e. their cost-efficiencies.

We found that proximal sensing produced unbiased and more accurate estimates of the mean organic C stock in each CEA compared to those derived with the conventional method. Although

compositing was more affordable than the sensing, it was inaccurate and its estimated variance was the most uncertain and unreliable. Significantly for C accounting and demonstration of a minimum detectable difference in organic C stocks over time, the variance of

the estimated mean of soil organic C stocks and the uncertainty of the variance of the estimated mean from proximal sensing were considerably smaller than those from the compositing method. In terms of the quality of the estimates and the total cost of the sampling designs, sensing was considerably more cost-efficient. The additional advantage of the sensing approach is of course, that one can also derive continuous maps of soil organic C. Finally, sensing provides a viable and cost-efficient approach for monitoring soil C stocks for accounting and as we have shown here, the financial advantage of compositing does not outweigh its disadvantages.

**keywords:** organic carbon, soil sampling, proximal soil sensing, compositing, stratification, visible near infrared
# 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

Borut Vršcaj – Agricultural Institute of Slovenia, Department of Agricultural Ecology and Natural Resources, Centre for Soil and Environmental Research, Slovenia

Laura Poggio – The James Hutton Institute, United Kingdom

Duško Muaketov – UKIM Institute of Agriculture Skopje, Department of Soil Science and Plant Nutrition, Macedonia

Ronald Vargas – FAO, Italy

Soils in the Former Yugoslav Republic of Macedonia (FYROM) are very diverse because of the diversity of its forming factors including parent material, topography, climatic conditions and the land use. Soil survey in FYROM started in the 1950's. The result of a more than four decades long systematic soil sampling, laboratory analyses and delineation of soil mapping units was a substantial collection of more than 4000 geocoded soil profiles and 140 sheets of the 1:50,000 national soil map. Still in paper form this important legacy soil data was largely unused and under-exploited. The need for digital soil information had emerged for evidence based decision making on sustainable agriculture, soil and environment protection, natural resource management and the adaptation to climate change. To provide with the required soil information to a variety of end-users, the Macedonian Soil Information System (MASIS) was developed with the technical and financial support of the FAO.

This contribution presents the complex process to rescue soil legacy data and convert it into digital soil property maps and derived applications using the digital soil mapping methods (DSM). In the first step the soil profile data were verified, harmonized, improved and digitized. The separate soil maps were checked, semantically improved and vectorised. The soil mapping units were edge-matched and merged to create a seamless polygon-based digital soil map of Macedonia in a scale 1:50,000. In the second step, DSM techniques were utilized to create datasets of key soil properties (soil depth, topsoil OM content, silt, sand, clay, and pH). A scorpan-krigin approach was followed and the covariates were derived from digital elevation model and a mosaic of remote sensing images (Landsat 7). Applicability of the soil property maps was demonstrated through a soil suitability assessment, thus completing MASIS data structure. The web-GIS portal (www.maksoil.ukim.mk) was developed to give open access to the improved MASIS soil legacy data as well to the new DSM-based soil information to a wide range of end-users.

**keywords:** legacy soil data, soil information system, modelling, digital soil mapping
# Mapping soil properties using a non-stationary variance geostatistical model

Alexandre M.J-C. Wadoux - Soil Geography and Landscape group, Wageningen University & Research, the Netherlands, Netherlands

Dick J. Brus - Biometris, Wageningen University & Research, the Netherlands, Netherlands

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

Geostatistical mapping of soil properties is decisive for agricultural activities and land use planning. In particular, soil clay content is critical for plant production since it controls soil hydraulic properties and may hinder irrigation. Spatial prediction of soil clay from measured location is therefore highly relevant for farmers. Commonly used spatial prediction methods such as kriging assume intrinsic stationarity of the residual variance. This assumption is implausible for mapping soil clay in a mountainous watershed because the mean and variance are linked to topographic attributes. Over the past years, substantial progress has been made to deal with non-stationary spatial processes in kriging. Various well-documented geostatistical models relax the assumption of stationarity in the mean, while recent studies show the importance of considering non-stationarity in the variance for environmental processes occurring in complex landscapes. In this study, we propose an extension of the Kriging with External Drift (KED) model for prediction of soil clay content. The model incorporates both non-stationarity in the mean and in the variance, which are modelled as linear functions of external covariates such as slope, elevation and geology. Covariates are selected based on Akaike's information criterion whereas model parameters are estimated with Restricted Maximum Likelihood (REML). The proposed approach is compared with the stationary variance model based on the log-likelihood, standardised squared prediction error and accuracy plots of the two fitted models. The methodology was tested using a case study predicting clay content in a small watershed in the centre of China. Results show that the proposed non-stationary variance model outperforms the stationary variance model for all criteria and that the stationary variance model lead to unrealistic prediction error variances of clay content in the watershed.

**keywords:** Geostatistics, Soil clay, Kriging, Non-Stationary Variance, Soil Mapping
Modelling the mid-infrared information content of European soils

Alexandre M.J-C. Wadoux - Soil Geography and Landscape group, Wageningen University & Research, the Netherlands, Netherlands

Leonardo Ramirez-Lopez - NIR Data Analytics, BUCHI Labortechnik, Switzerland, Switzerland

Felix Stumpf - Agroscope, Institute for Sustainable Sciences, Zurich, Switzerland, Switzerland

Thomas Scholten - Institute of Geography, Physical Geography and Soil Science, University of Tübingen, Germany, Germany

Karsten Schmidt - Institute of Geography, Physical Geography and Soil Science, University of Tübingen, Germany, Germany

Assessing soil properties using visible and near infrared (Vis-NIR) spectroscopy have emerged as a powerful tool in digital soil mapping. In particular for the Mid-Infrared (MIR) region (400-4000 cm⁻¹), common calibration methods coupled with laboratory analyses predict soil properties with high accuracy. The reason is that the fundamental molecular vibration occurs in the MIR when only the overtones and combinations occur in the Vis-NIR. Hence, the bending or stretching vibration at a precise wavelength allow qualitative diagnostic on the soil components without any coupled chemical analysis. Recent studies show the importance of the soil information summarised into a few wavelengths of the spectrum. However, very little is known on how to use such information for soil monitoring. In this study, we propose a pragmatic approach to assess the relationships between soil attributes and environmental covariates by using MIR spectroscopy. The method consists in fitting each band of the MIR region as a linear model with spatially correlated errors. Model parameters are estimated by robust (Restricted) Maximum Likelihood whereas the quality of the predictions is assessed using Leave Group Out Cross Validation (LGOCV). This method does not require any prior explicit knowledge of the soil attribute values.

Our method is tested using the GEMAS dataset based on 4000 MIR spectra from soil samples spanning 33 countries of the European continent. The multivariate relationship between spectra and covariates is qualitatively interpreted based on spectrograms derived from robust linear models. The results show that soil mineralogy bands and particularly clay minerals are strongly linked to the climatic conditions as well as to the slope. In contrast, soil organic matter bands are difficult to interpret, showing reasonable correlation with temperature and precipitation only for the amide and humic acid groups. In this work, we show how our method may be used to make pedological interpretations and describe soil spatial distribution without prior knowledge on the soil property spatial variation.

keywords: Soil Spectroscopy, Mid-Infrared Spectra, Robust Linear Model, GEMAS dataset
Prediction of Soil Organic Matter Using VisNIR and PXRF Spec-
troscopy

Changkun Wang – Institute of Soil Science, Chinese Academy of Sciences, China

Xianzhang Pan – Institute of Soil Science, Chinese Academy of Sciences, China

Soil organic matter (SOM) plays an important role in many chemical and physical processes in soils. However, measuring SOM via traditional chemical methods is typically time consuming and expensive. In this study, we combined portable x-ray fluorescence (PXRF) and visible and near-infrared (VisNIR) diffuse reflectance spectrometry to predict SOM. Partial least square regression (PLSR) and random forest (RF) were used to calibrate prediction models for VisNIR spectra only, PXRF measured elements only, and both VisNIR spectra and PXRF elements (VisNIR + PXRF). The general order of predictions can be summarized as VisNIR + PXRF > VisNIR > PXRF regardless of PLSR or RF. In conclusion, the combination of PXRF and VisNIR was shown to be a viable method for SOM prediction.

keywords: soil organic matter, visible and near-infrared, portable x-ray fluorescence
Potential of LUCAS for the development of regional-scale spectral
models for the prediction of soil properties

Kathrin Ward - GFZ, Germany

Sabine Chabrillat - GFZ, Germany

Saskia Foerster - GFZ, Germany

Carsten Neumann - GFZ, Germany

Luis Guanter - GFZ, Germany

Imaging spectroscopy is a powerful, non-destructive and inexpensive tool to predict key soil
properties in local areas provided that ground truth soil data are available for model calibration.
The availability of hyperspectral satellite imagery in the near future has a great potential to
improve the methods for predicting key soil properties which is essential to achieve advances in
global soil mapping and monitoring. Nevertheless, when changing from local to regional or even
broader scales, spectral modelling is challenged by a higher variability of soil characteristics and the
models are dependent on related soil databases. This study aims at investigating the potential of
large soil spectral databases such as the EU-wide LUCAS (Land Use/Cover Area frame Statistical
Survey) database concerning the development of regional soil spectral models for an improved
derivation of more precise and up-to-date spatial information of soil properties. Such methods
will provide new options for a more sustainable soil usage and for monitoring and preventing soil
degradation.

We are focusing on the link between geo-chemical soil properties and spectral variabilities
within the soil database LUCAS. LUCAS constitutes to date the most complete and consistent
soil spectral library at continental scale, with 20,000 top-soil samples collected following the same
sampling protocol. It includes thirteen chemical and physical properties, including Vis-NIR re-
flectance, which were analysed following standard procedures. Our intention is to group the large
number of samples within the database into smaller, more homogeneous clusters to improve overall
prediction accuracies at larger scales. We focus on agricultural areas because they can be mapped
by remote sensing when they are temporarily free of vegetation. Different clustering approaches
(e.g. a random sampling machine-learning approach) based on spectrally pre-processed data, are
tested to find the best method. Afterwards, multivariate modelling approaches (e.g. PLSR) are
used for each cluster to predict soil organic carbon (SOC) and other soil properties based on the
spectral information. Preliminary results show that clustering can improve the prediction results
in comparison to non-clustered LUCAS data.

A further goal is to validate this approach with new spectra and transfer it to hyperspectral
images from test sites which will be assigned to the clusters. This includes the application of the
established models to simulated EnMAP image data. This contribution will focus on presenting the
results achieved so far in terms of LUCAS database clustering and development of cluster-specific
soil spectroscopic models to derive SOC and other parameters.

keywords: hyperspectral database, LUCAS soil spectral library, Vis-NIR reflectance spectroscopy, soil organic carbon, multivariate modelling, clustering, EnMAP
Time sequence division of high standard farmland construction
based on cultivated land quality uniformity in administrative vil-
lage and obstacle factors

Song Wen – China University of Geosciences (Beijing), China

Wu Kening – China University of Geosciences (Beijing), China

As an important constitute of the land consolidation, high standard farmland construction is
the important means to protect the quantity , quality and ecological environment of cultivated
land.Through this paper we tried to explore the method of cultivated land evaluation and con-
struction zoning to facilitate the process of decision and implementation for high standard farmland
construction. Taking land comprehensive improvement project area in Quzhou county as a case
study, the whole process of the study was composed of three steps: 1) establishing the evaluation
model of cultivated land quality uniformity based on regional optimum cultivated land quality
and constructing the uniformity evaluation index system of cultivated land quality which was con-
structed from the aspects of soil quality, engineering quality, space quality, ecological environment
quality and aesthetic quality that based on the new concept of cultivated land quality; 2 calculating
the cultivated land quality uniformity through grading indicators, assigning score and weighting
sum; 3) according to the principle of priority to the easy in order to carry out high standard farm-
land construction timing partition through analyzing regional cultivated land quality difference
and obstacle factor index restriction.The results show that the evaluation model and index system
can satisfy the evaluation of the farmland quality and the diagnosis of obstacle factors to facilitate
the subsequent construction decision; The value of farmland quality uniformity is different between
various administrative villages. Through the natural breaks method, the project area is divided
into three sections of quality uniformity level of cultivated land, including the high-level area,the
middle-level area and the poor level area, and the quality of cultivated land plots are from high
to low. The project area is divided into four time sequence partitions including the major con-
struction area, the secondary construction area, the general construction area and the conditional
construction area , and the construction difficulty of these four areas is from easy to difficult; The
most restrictive factors in the project area are medium and low transformation difficulty indica-
tors,including soil organic matter content, forest density, field regularity and scale of field, and
there are high difficulty indicators in some areas, such as sectional configuration. The study can
be a reference for the practice of high standard farmland construction, and provide new ideas and
methods for related research.

**keywords:** high standard farmland, cultivated land quality uniformity, obstacle factor, time sequence division
Is a national Vis-NIR library useful for filed scale predictions of
soil type?

Johanna Wetterlind – Swedish University of Agricultural Sciences, Sweden

Bo Stenberg – Swedish University of Agricultural Sciences, Sweden

Although cost-efficient, large-scale Vis-NIR calibration models have often proven to lack in precision at the farm or field scale. And often the large models are not validated on this local scale. Small local calibrations, on the other hand, have previously been shown to be successful, but then quite a large number of samples need to be analyzed the traditional way and much of the advantage with spectroscopy is lost. So called spiking, amending a large scale library with a few local samples, and memory based learning (MBL) techniques, has been suggested to improve precision and reduce errors. Here we compare these methods with plain large scale PLS calibrations as well as small local PLS calibrations. We used 11425 topsoil samples distributed over Swedish agricultural soils for calibrations and 11 farms between 42 and 165 ha for field scale validation. The Swedish library was divided into a 7619 sample calibration set and a 3806 sample validation set was used for validation at the national scale. The validation samples were never involved in any calibration. Soil from 4 of the farms were analyzed with the same Vis-NIR range instrument as the Swedish library. The 7 other farms were analyzed with an instrument operating in the NIR-range only. For predictions of these farms, Swedish library spectra were transformed for a better match. Spiking and local calibrations were performed with 10, 20 and 40 samples. Field scale validation samples were always the same, independent of calibration set. Results showed that local calibrations with 40 samples were the best for most farms, but spiking with 10 or 20 samples was almost as good or better than local calibrations of the same size. These findings were more pronounced for farms analyzed with the Vis-NIR instrument. General MBL and especially plain large scale PLS calibrations performed substantially worse with some farms suffering from large bias. We concluded that general calibrations from large libraries are not reliable at the farm scale and that local calibrations of sufficient size perform best in most cases. But to gain from the advantages with the simplicity of Vis-NIR spectroscopy spiking with 10-20 samples is an interesting alternative that make use of the stability from the large library and the special features of the local soil type. Spiking also seemed to overcome some of the issues still remaining after the transformation of the national Vis-NIR spectra to match the NIR instrument.

keywords: Near infrared spectroscopy, Spectral library, Memory based learning techniques, Spiking, Field scale, Clay content, Soil organic matter
# Hydro-geomorphic Spatial Modelling for Multi-scale Coastal Acid Sulfate Soil Mapping

Wirastuti Widyatmanti - Faculty of Geography, Universitas Gadjah Mada, Indonesia

Ibnu Rosyadi - Faculty of Geography, Universitas Gadjah Mada, Indonesia

Kurniawan Nugroho - Faculty of Geography, Universitas Gadjah Mada, Indonesia

Coastal acid sulfate soils (ASS) occurrences in tropical region are largely controlled by the seasonal energy of marine and upper catchment. However, the ASS occurrence prediction and mapping using soil-landscape modelling that consider the multi-temporal and multi-scale hydrology and geomorphology spatial data variables that represent that seasonal energy has been limited. As a matter of fact, inadequate number of variables calculated in this model will under- or over-estimate the ASS occurrences and their properties. This paper aims to develop a hydro-geomorphic spatial model for predicting the presence and absence of ASS and their properties, and to classify the mapping variables that needed in different scale of ASS mapping.

The multi-temporal and multi-resolution remotely sensed data (SRTM 1 Arc-second Global, Landsat 8, QuickBird/Ikonos satellite imageries) and geographic information system (GIS) approaches were utilized to spatialize the variables that control the ASS formation. Multi-level data aggregation approach was applied to combine the spatial data of the sources of iron, organic matter, sulfate, the energy level of estuary and coastal environment, the information of regional climate, sea level rise, and alternately fresh and brackish water dominance and waterlogged environments during rainy and dry seasons. Field survey included the soil sampling for field and laboratory analysis, water salinity and elevation measurement, and ebb and tide observation. The result shows that for the regional scale, the hydrology and geomorphology data of the catchment level become the main variable that control the ASS occurrences, reaching 85% accuracy test, using matrix correlations. On the local scale, the seasonal energies during the dry and rainy seasons predominantly influence the properties of the ASS.

**keywords:** acid sulfate soil, hydrogeomorphic spatial modelling, multi-temporal, multi-scale, remote sensing and GIS
A VNIR penetrometer for soil profile sensing

Nuwan Wijewardane – University of Nebraska-Lincoln, USA

Yufeng Ge – University of Nebraska-Lincoln, USA

Cristine Morgan – Texas A&M University, USA

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

Visible and near-infrared (VNIR) reflectance spectroscopy has been proven as an effective method in estimating important soil properties such as textures, organic and inorganic carbons, and moisture. Due to its ability to derive multiple soil properties from a single scan and its rapid, nondestructive, and low-cost nature, researchers have made efforts to develop proximal sensors using this technology to enable horizontal soil sensing. Similarly, this technology can be used to design and develop vertical soil sensors which can aid in precision agricultural and environmental focused decisions. The objective of this research is to design and develop a penetrometer system to enable the acquisition of soil spectra and penetration resistance along the depth. To achieve this objective, a penetrometer with an optical module was designed and developed to obtain re-flectance spectra along the soil profile. A load cell and a distance sensor were integrated with the penetrometer to measure the penetration resistance and insertion depth. The developed system was attached to a Giddings probe and tested for its field functionality in several fields located in Lincoln, NE, USA. All the sensor readings were logged in real-time using a LabVIEW program. Field test of the system demonstrated its ability to read and log soil spectra, penetration resistance with depth in real-time and the acquired data were used to predict various soil properties along the soil profile. Results will discuss the contribution of penetration resistance to analysis of soil properties.

**keywords:** Visible and near infrared reflectance spectroscopy, soil penetrometer, vertical soil sensing, soil carbon, soil textures, penetration resistance, soil moisture
# The Rapid Carbon Assessement Project: a modern soil carbon stock baseline for the conterminous United States

Skye Wills - USDA-NRCS, USA

Zamir Libohova - United States Department of Agriculture, Natural Resources conservation Services, USA

Stephen Roecker - USDA-NRCS, USA

Terry Loecke - University of Kansas, USA

The Rapid Carbon Assessment Project (RaCA) was undertaken to quantify the soil organic carbon (SOC) stocks across the conterminous US at one point in time. Sample locations were chosen using a multi-stage stratified-random sampling frame and cover all areas in CONUS with SSURGO certified maps as of Dec 2010. Sampling was stratified into 17 regions for logistical reasons. Within each region, soils groups and land use/cover classes were used to further stratify sampling. Each region had 375 - 400 sites, for a total of approximately 6,400 sites. At each site, basic information about land use, vegetation and management were collected as appropriate and available. Samples were collected from 5 pedons (a central and 4 satellites) per site to a depth of 1m, at 0 - 5cm and by genetic horizon. A volumetric sample was collected for horizons above 50 cm to determine bulk density. For horizons below 50cm (or when a volumetric sample could not be obtained) bulk density was modeled from morphological information. All samples were air dried and crushed to <2mm. The central pedon was analyzed for total and organic carbon at the Kellogg Soil Science Laboratory in Lincoln, NE. A visible near-infrared (VNIR) spectrophotometer was used to predict organic and inorganic carbon contents for all satellites samples. A Hierarchical Bayesian statistical approach was used to estimate C stocks, concentrations, and uncertainty for each sampling level (i.e., CONUS, region, soil group, land use/cover and site). Carbon stocks were summarized by fixed depth increments for sites, soil groups, and land use/groups and mapped by linking the values to a raster of SSURGO (Jan 2012) that includes map unit and land use/cover classification. The CONUS SOC stock to 1m of soil depth was estimated at 84.6 Pg +/- 3 Pg.

The mean area weighted SOC stock to 1m of soil depth for CONUS was 110.4 Mg C ha⁻¹ with a 95% creditable interval ranging from 106.8 to 114.3 Mg C ha⁻¹. Soil groups explained the greatest portion (45.5%) of CONUS SOC stock variance (1m) followed by the inter-site level (32%), the regional scale (12.8%), and land use/cover explained the least (9.7%). Due to large variability land use/cover classes are not statistically different, but wetlands have the largest SOC stocks and account for 30% of the CONUS total. This SOC stock baseline data set will be useful for modeling, monitoring and tracking SOC changes for many disciplines.

**keywords:** soil organic carbon, soil sampling design, continental scale
# 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

Melanie Wisgott – Bergische Universität Wuppertal, Germany

Jannis Heil – Bergische Universität Wuppertal, Germany

Britta Stumpe – Bergische Universität Wuppertal, Germany

Persistent organic pollutants (POPs) are known to have adverse effects on human health and the environment. Thus, an effective risk assessment with concern on POP behavior in the soil compartment is of high interest. Therefore, the objective of our study was to develop a reliable, rapid, and low-cost method, which can predict the adsorption and desorption behavior of two model POPs, 4-n-nonylphenol (NP) and perfluorooctanoic acid (PFOA), in soils. NP and PFOA were selected as model pollutants since they are highly resistant against microbial degradation. As consequence, their environmental fate will be mainly controlled by adsorption and desorption processes in soils. As FTIR spectra reflect the total soil composition, we used FTIR spectroscopy in combination with supervised statistical classification procedures to develop a prediction tool for the fate of NP and PFOA.

For the model development 72 soil samples were analyzed for particle size distribution, manganese and iron oxide concentration, and organic carbon content to characterize the chemical soil composition. FTIR spectra of all soil samples were measured in the mid-infrared region. The adsorption and desorption of the model POPs to the soil samples were analyzed in batch experiments.

Generally, the adsorption of NP and PFOA was linear in all soil samples. However, significant differences between the KD-values of PFOA (KD ≤ 2 to 40 ml g⁻¹) and NP (KD ≤ 25 to 1000ml g⁻¹) were observed. Via multiple regression analysis we demonstrated that the PFOA adsorption is mainly controlled by the soil organic carbon as well as by the iron oxide content. The adsorption of NP was controlled by the soil organic carbon content only.

Since different adsorption mechanisms of NP and PFOA have been observed we decided to develop different prediction models for both pollutants. Because FTIR spectra are reflecting all soil components in different spectral intervals, initially, the spectral intervals representing organic carbon and iron oxides were statistically identified for further model developments. The relevant spectral intervals were used to create prediction models based on the random forest algorithm, whereas different intervals were used for NP and PFOA due to their different adsorption mechanism. The prediction of NP and PFOA adsorption reveal model error rates of 3 and 7%, respectively. Likely, the higher error rate of the PFOA model could be explained by coated iron oxides in the batch experiments masking the oxide adsorption potential.

**keywords:** sorption, FTIR spectra, random forest algorithm, 4-n-nonylphenol, perfluorooctanoic acid, persistent organic pollutants, multivariate statistics
Fine-resolution mapping of soil carbon stock in Japanese forest
based on machine-learning regression kriging

Naoyuki Yamashita – Forestry and Forest Products Research Institute, Japan

Shoji Hashimoto – Forestry and Forest Products Research Institute, Japan

Kazuki Nanko – Forestry and Forest Products Research Institute, Japan

Shigehiro Ishizuka – Forestry and Forest Products Research Institute, Japan

Yuko Osone – Forestry and Forest Products Research Institute, Japan

Shin Ugawa – Kagoshima University, Japan

Nagaharu Tanaka – Forestry and Forest Products Research Institute, Japan

Akihiro Imaya – Japan International Research Center for Agricultural Sciences, Japan

Shinji Kaneko – Forestry and Forest Products Research Institute, Japan

Satoru Miura – Forestry and Forest Products Research Institute, Japan

Spatial information of soil organic carbon (SOC) is important in global carbon cycle and climate
change research. The information is still limited at fine spatial-scale in Japanese forest that is
mainly distributed in mountainous regions with complex topography. Digital Soil Mapping (DSM)
approach may contribute to the spatial prediction of SOC stock in the regions at both fine (local)
and regional spatial-scale using the dataset of National Forest Soil Carbon Inventory (NFSCI),
various terrain attributes and other environmental factors. Our objective is to evaluate DSM
approach for predicting SOC stocks in 0-30 cm depth in the whole area of Japanese forest (ca.
250,000 km²), with grid-resolution of 50 × 50 m. We compared the accuracy of 5 methods; random
forest (RF), support vector machine (SVM), ordinarily kriging (OK), and their combinations known
as machine-learning regression kriging (RFRK and SVMRK).

The training dataset of SOC was obtained from NFSCI that is conducted in FY 2006–2010.
The survey plots were installed in some of 4 × 4 km grid points over the forest sector of Japan (N
≤ 2462). As for explanatory variables, we estimated environmental factors from traditional map
(e.g. soil, geology, vegetation, and tephra) and climatic database, and various terrain attributes
from high resolution DEM. We determined optimal parameters of SVM and RF by a grid search
method. As for spatial parameters of RFRK, SVMRK and OK, a distinct two spatial-structures
with the ranges of ca. 10 km and 60 km were observed in the experimental semi-variograms and
well fitted to the spherical models. Finally, the accuracy of the maps was evaluated by RMSE and
correlation coefficient using the cross-validation approach. We also tested the sensitivity of the
accuracy to the number of the training data for each method.

As a result, this study confirmed the high accuracy and reproducibility of the SOC map by
SVMRK in comparison with the map by OK. Particularly the accuracy of SVMRK and RFRK
(machine-learning regression kriging) was less sensitive to a decrease in the number of training
data points whereas the accuracy of OK reduced with a decrease in the number of the data points.
These results suggested that SVMRK performed better and is more flexible to the number of the
sampling plots than traditional kriging for fine-resolution mapping of SOC stock in Japanese forest.
Additionally, we highlighted the contribution of the explanatory variables to the spatial variation
in SOC stock.

**keywords: Soil organic carbon, Regression Kriging, Machine-learning, National-scale soil map-**
**ping, Japanese forest, Mountainous region**
# A MGWRK technique for mapping soil electrical conductivity in the Heihe River Basin, northwest China

Shunhua Yang - Institute of Soil Science, Chinese Academy of Sciences, China

Yuanyuan Lu - Institute of Soil Science, Chinese Academy of Sciences, China

Ganlin Zhang - Institute of Soil Science, Chinese Academy of Sciences, China

Spatial information of soil electrical conductivity (EC), which is a good proxy for soil salinity, has attracted increasing attention in arid-semiarid region as it largely determines plant growth. The relationships between EC and environmental variables are usually used to map its spatial variation. However, digital soil mapping EC in large and complex environmental settings remains challenging due to its spatial non-stationarity and spatial dependence. For the spatial non-stationarity, the relationships between soil properties and environmental covariates may be invariant or varying over spaces. For the spatial dependency, soil property observations and their environmental covariates are not independent each other. The current regression models have been constructed either to address the former issue or to solve the latter one. Yet, few researches paid attention to explore the spatial dependence of environmental covariates and to investigate the fixed or varying relationships between soil properties and environmental covariates.

As the second largest inland river basin in northwest China, the Heihe River Basin ranges from 900 m a.s.l. to 5,500 m a.s.l by altitude. The upstream, midstream and downstream correspond to alpine, oasis and desert environments respectively. The ECs in these contrasting settings are highly heterogeneous and the relationships between EC and environmental variables are spatially non-stationary.

In this study, a mixed geographically weighted regression kriging (MGWRK) is introduced to examine the correlations between environmental covariates and EC to better estimate the spatial distribution of EC in the Heihe River Basin. Results from OK, MLR, RK, GWR, GWRK and mixed geographically weighted regression were used for comparison with those from MGWRK. The key environmental variables were refined from spatial information on topography, climate, vegetation, land use/cover, soil parent material, and soil type. Two hundred and forty-four georeferenced top soil samples were divided into two subsets, i.e. the calibration dataset (80%) and the validation dataset (20%). MAE and RMSE were used for evaluating the performances of the aforementioned approaches. EC ranged from 31 to 182100 s·cm⁻¹ for the all soil observations. The mean value is 3335.1 s·cm⁻¹ and the coefficient of variation is 44.8%. Geostatistical analysis showed that the isotropic variogram model was best fitted by Gaussian model, and the C0/(C0 + C) ratios of fitted variogram model fell between 0 and 25%, which means that EC exhibited extensive spatial autocorrelation. These preliminary results suggest that MGWRK has the potential to address the spatial dependency and spatial non-stationarity issues for EC.

**keywords:** soil salinity, MGWRK model, Heihe River Basin
# Accounting for fieldwork costs in validation of soil maps: a comparison of design-based sampling strategies

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

Dick Brus – WUR, Biometris, Netherlands

A-Xing Zhu – Department of Geography, University of Wisconsin-Madison, USA

Xinming Li – Institute of Geographic Sciences and Natural Resources Research, Chinese Academy Sciences, China

The quality of soil maps can best be estimated by collecting additional data at locations selected by probability sampling. These 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 fieldwork costs related to differences in accessibility it can be attractive to account for these differences in selecting validation locations. Sampling at lower densities in remote areas may increase the sampling efficiency. We compared two types of sampling design that take fieldwork costs into account: sampling with probabilities proportional to size (pps) and stratified simple random sampling (STSI). In pps the inverse of the square root of the point-specific costs was used as a size variable. Two estimators were applied, the Hansen-Hurwitz and Hajek estimator. In STSI optimal strata were constructed for optimal allocation of the total sample size to the strata. Simple random sampling (SI) was taken as a reference design. The sampling strategies were compared on the basis of: 1) the variance of the estimated MSE; 2) the variance of the total costs. The comparison was done at equal expected total costs. The sampling strategies were compared in a simulation study and a real-world case study in Anhui, 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 car traveling costs, hiking costs and costs spend on the spot were considered in computing total sampling costs per point. Hiking and travel costs were based on the topographic condition, surface friction and road data. The simulation results show that pps(Hajek) and STSI were equally precise. The gain in precision compared to SI depends on the frequency distribution of the point-specific costs. The larger the variance of the costs, the larger the gain. Also, the larger the fixed cost constants, the smaller the gain in precision. The variance of total sampling costs was the smallest for STSI, showing that control of total sampling costs with STSI was the best. In Anhui the gain in precision of pps(Hajek) and STSI was about 30% accounting for hiking costs only, and about 5% accounting for all three cost components.

**keywords:** validation, probability sampling, accessibility, sampling with probabilities-proportional-to-size, stratified random sampling
Identifying soil landscape units at the district scale by numerically
clustering remote and proximal sensed data

Ehsan Zare – UNSW Australia, Australia

Jingyi Huang – UNSW Australia, Australia

John Triantafilis – UNSW Australia, Australia

Identifying soil landscape units at a district scale is important as it allows for sustainable land-
use management. However, given the large number of soil properties that need to be understood
and mapped, cost-effective methods are required. In this study, we used a digital soil mapping
(DSM) approach where remote and proximal sensed ancillary data collected across a farming
district near Bourke, were numerical clustered (fuzzy k-means: FKM) to identify soil landscape
units. The remote data was obtained from an air-borne gamma-ray spectrometer survey (i.e.
potassium-K, uranium-U, thorium-Th and total counts-TC). Proximal sensed data was collected
using an EM38 in the horizontal (EM38h) and vertical (EM38v) mode of operation. The FKM
analysis (using Mahalanobis metric) of the kriged ancillary (i.e. common 100 m grid) data revealed
a fuzziness exponent (phi) of 1.4 was suitable for further analysis and that k ≤ 4 classes was
smallest for the fuzziness performance index (FPI) and normalised classification entropy (NCE).
Using laboratory measured physical (i.e. clay) and chemical (i.e. CEC, ECe and pH) properties
we found k ≤ 4 was minimized in terms of mean squared prediction error (i.e. sigma2p,C) when
considering topsoil (0–0.3 m) clay (159.76), CEC (21.943), ECe (13.56) and pH (0.2296) and
subsoil (0.9–1.2 m) clay (80.81), CEC (31.251) and ECe (16.66). These sigma2p,C were smaller
than those calculated using the mapped soil landscape units identified using a traditional approach.
Nevertheless, class 4A represents the Aeolian soil landscape (i.e. Nb4), while 4D, represents deep
grey (CC19) self-mulching clays, and 4B and 4C yellow-grey (II1) self-mulching clays adjacent to
the river and clay alluvial plain, respectively. The differences in clay and CEC reveal why 4B, 4C
and 4D have been extensively developed for irrigated cotton production and also why the slightly
less reactive 4B might be a source of deep drainage; evidenced by smaller topsoil (2.13 dS/m) and
subsoil (3.76 dS/m) ECe. The research has implications for providing meaningful DSM of soil
landscape units for farmers at districts scales where traditional methods were restrictive in terms
of time and cost.

keywords: EM induction, fuzzy k-means, gamma-ray spectrometry, REML, digital soil mapping,
Linear mixed model
# A prototype methodology for assessing within-field soil variation using digital soil mapping, legacy soil datasets and satellite imagery to aid precision farming

Joanna Zawadzka - Cranfield University, United Kingdom

Stephen Hallett - Cranfield University, United Kingdom

Ronald Corstanje - Cranfield University, United Kingdom

Caroline Keay - Cranfield University, United Kingdom

Addressing matters of food security in the light of rapid population growth is an increasingly important challenge. Sustainable intensification of agricultural food production will be required to meet the future demands for food, and this can be facilitated by smart management of land resources offered by advanced precision agriculture technologies. The precision farming concept has been developed on the grounds of evidence that applying seed and fertiliser variably, based on localised geographical soil property patterns, helps maximise yields whilst saving resources and inputs. For precision farming to be effective, determining within-field soil variation is essential. Legacy soil data, however, rarely provide soil information at appropriate spatial scales to match precision farming requirements. Acquiring more detailed soil information using traditional soil survey methods is costly and time consuming, especially where data is required across entire regions or countries. Digital soil mapping, coupled with remotely-sensed, fine resolution digital datasets and advanced machine learning techniques, offers promising opportunities for mapping soil information at spatial scales appropriate for precision farming, but without the necessity for conducting extensive soil surveys.

We present a study exploring the capacity of such techniques to represent reliably field-scale soil variation, using an exemplar of fifty fields distributed across England. The work is undertaken within a recently-commenced two-year project, funded by the UK Natural Environment Research Council (NERC), InnovateUK and AgSpace Ltd., a leading precision farming company. This communication sets out a provisional methodology, with preliminary results, focusing on the application of the DSMART algorithm for spatial disaggregation of the national soil map for England and Wales (NATMAP), locating soil series within soil association mapping units at 1:250,000 scale. Results are verified with existing auger-bore observations and detailed soil series maps at 1:25,000 and 1:50,000 scales. The disaggregated maps are also compared to soil characteristic zone delineations, based on soil brightness mapping, developed by AgSpace from high-resolution multispectral remote sensing data.

**keywords:** Precision soil mapping, Spatial disaggregation, DSMART
Teaching digital soil mapping as an example of contemporary en-
vironmental survey methods

Joanna Zawadzka - Cranfield University, United Kingdom

Ron Corstanje - Cranfield, United Kingdom

Jacqueline Hannam - Cranfield University, United Kingdom

Pedometrics, and in particular, digital soil mapping is taught at Cranfield University at the post-graduate level. Due to the modular structure of all Master courses at Cranfield the class is directed at students with varied backgrounds and as a result no assumptions of the prior knowledge of soil science nor advanced geostatistical or machine learning methods can be made. The class provides an example of handling and processing of spatially-explicit environmental data as part of the Environmental Resource Survey (ERS) module that aims at familiarising the students with the state-of-the-art methods in environmental survey design and data analysis. The class is therefore an opportunity to introduce machine learning methods for applications in environmental sciences and by doing so illustrates the concepts of data fusion and model data fusion explained to students earlier in the module. In particular, the class utilises an example of the Irish Soil Information System (ISIS) project where digital soil mapping was used to compliment the traditional soil survey and existing legacy data to generate a harmonised map of soil associations for the Republic of Ireland. The subject is taught over 4.5 contact hours, with the first hour dedicated to an introductory lecture covering the basic concepts of soil-landscape modelling, exemplified by the case study of the ISIS project. The remaining time is allocated to a step-by-step tutorial where students are acquainted with the process of the creation of a predictive soil map with the use of Random Forests models, followed by verification of the mapping accuracy. The structure of the class contributes to the following intended learning outcomes of the ERS module: ‘to evaluate existing information and models which complement the survey method’, ‘to select and carry out appropriate modelling and statistical analyses’, and ‘to assess the accuracy of the results’. Both the lecture and practical are designed to engage students exhibiting various learning styles as well as level of proficiency in the presented methods, and are characterised with clearly defined learning objectives for each section of the teaching process.

keywords: digital soil mapping, master-level education, environmental survey methods exemplar
Data mining of soil color database

Rong Zeng – Institue of Soil Science, Chinese academy of Sciences, China

David Rossiter – Section of Soil and Crop Sciences, Cornell University, USA

Ganlin Zhang – Institue of Soil Science, Chinese academy of Sciences, China

Soil color is a very important morphological property which is determined by soil physio-
chemical and mineral composition. It is an important indicator used in many current soil classifi-
cation systems. Potentially soil color map also serves to determine soil reflectance features which
are important for regional energy radiation-transfer models.

The determination of soil Munsell color (Cm) is influenced by many factors such as illuminance conditions, soil moisture status and the perceptual ability of the observers, which will lead to large differences between different replicate measurements or different observers. This study tries to compare and evaluate the consistency of Cm determined in different soil laboratories using reflectance spectroscopy, and to explore how soil color measurements influence the determination of diagnostic horizons with regard to soil color. A Soil color database, covering the geographic extent of both western and eastern China, with all typical soil types and a wide range of soil color, was constructed.

The average difference between predicted and observed Cm is 0.19, -3.28 and -0.70 units respec-
tively for Hue, Value and Chroma. Spectrally predicted hue is generally more yellow and less red
for soil hue in the range of 10R~7.5YR; while for hue ranged from 10YR to 2.5Y, predicted hue is
less yellow and more red. As for Value and Chroma, spectra generally gave lower values. Since soil
spectra can give more objective and consistent prediction of soil color, thresholds for determination
of soil color related diagnostic horizons (e.g., mollic epipedon, albic horizon) were proposed based
on data mining of spectrally predicted soil colors. In addition, a comparison was made between soil
color maps generated from observed and spectrally predicated data using geostatistical techniques.

keywords: Munsell color, reflectance spectroscopy, data mining, mapping
Soil big data: Requirement and Potential in China

Weili Zhang – Chinese Academy of Agricultural Sciences (CAAS), China

Aiguo Xu – Chinese Academy of Agricultural Sciences (CAAS), China

Renlian Zhang – Chinese Academy of Agricultural Sciences (CAAS), China

Hongjie Ji – Chinese Academy of Agricultural Sciences (CAAS), China

Huaizhi Zhang – Chinese Academy of Agricultural Sciences (CAAS), China

Huaiyu Long – Chinese Academy of Agricultural Sciences (CAAS), China

Soil fertility management challenge: To increase food production, the agro-chemical inputs have been raised by 3.6 times since beginning of 1980s, however, little improvement for land fertility management techniques of farmers has been achieved. Blind and excessive application of fertilizers, manures from large livestock and poultry farms, pesticides and irrigation have been common in crop production to compensate technical shortages. In recent years, fertilizer nutrients input has reached to 450 kg per hectare arable land and showed upward trend. It is causing serious soil quality degradation, over expending of ground water irrigation, heavy pollutions of soil, water, air as well as farm produce.

Little benefits to farmers from state soil investigation: Aimed to improve land fertility management techniques of farmers in China, a series nation-wide soil investigations, which involved 40 billion $, have been carried out and numerous soil fertility observations have been gained in recent years. However, the data obtained, which should be helpful to improve farmer's techniques, are storied in raw data format in different organizations or institutions mainly. The valuable data are either not understandable for farmers or not achievable to farmers.

To deliver farmers soil info: By using big data approach, raw soil observations from different investigations, time periods and regions can be extracted, integrated and interpreted to the info or recommendation which are easy for farmers to understand and to improve their land fertility management practices. The soil data processing, interpreting and knowledge delivering can be carried out by specialized state soil research institutes. Through GPS inserted in smart phone, each farmers can get the in-situ soil info and recommendation of his farms and field.

Potentials: In 2014 the users of mobile phone in China reached 91% of the whole population and smart phone reached 36%. Processing and interpreting of soil big data from different resources and delivering farmers with useful soil fertility management info and knowledge will be not only feasible but also very important for the country to realize a sustainable agricultural development.

keywords: soil big data, China, soil fertility
Development of China Digital Soil Map CDSM) at 1:50,000 Scale

Weili Zhang – Chinese Academy of Agricultural Sciences (CAAS), China

Aiguo Xu – Chinese Academy of Agricultural Sciences (CAAS), China

Renlian Zhang – Chinese Academy of Agricultural Sciences (CAAS), China

Huaizhi Zhang – Chinese Academy of Agricultural Sciences (CAAS), China

Hongjie Ji – Chinese Academy of Agricultural Sciences (CAAS), China

Huaiyu Long – Chinese Academy of Agricultural Sciences (CAAS), China

Objective: Temporal and spatial soil information with high resolution, as a new and indispensable tool for researchers and managers, can be applied to soil and environmental quality assessment, arable land conversation, flood and draught mitigation, erosion control, non-point source pollution control, soil and agricultural product pollution control and many other areas. Purpose of the study is to develop China Digital Soil Maps (CDSM) through extracting soil information from legacy data collected from the whole country.

Data resources of CDSM: In last decades, several nation-wide soil surveys have been conducted in China. The valuable and numerous soil data, in paper or digital raw data format from these investigation provided important basic for CDSM development

CDSM construction: To extract, integrate and interpret heterogeneous and massive soil raw data originated from different resources, periods, provinces and counties, a human-computer interactive data treatment working flow was developed. A computer software program IMAT (Intelligent Mapping Tools) have been developed.

CDSM content: CDSM was composed of two kind of soil geo info. One was map data with vector and raster format that describe soil types, soil organic matter, total nitrogen, available phosphorus, potassium and pH-value. The second kind was point data of about 100,000 soil profiles and 80,000 plough layer samplings, which describe soil parent material, mechanical composition, texture and other morphological features and the fast-changing soil properties as soil carbon and nutrient concentrations. By time series, space sequence or thematic order, required soil info with a map resolution of 100 m×100 m can be inquired and provided by CDSM. The information has been applied by a number of agricultural, environmental and land resource organizations, divisions and research institutes for knowing current soil quality and changing of soil quality.

Future works: With CDSM completion and continuing supplement, further works should be paid on: (1) data share to different scientific disciplines and part of data share through internet. (2) Further processing and interpreting, to deliver farmer soil info and recommendation, to improve land fertility management techniques.

keywords: Digital Soil Map, China, large scale
Intelligent Mapping Tools (IMAT) for big soil data processing

Weili Zhang – Chinese Academy of Agricultural Sciences (CAAS), China

Aiguo Xu – Chinese Academy of Agricultural Sciences (CAAS), China

Renlian Zhang – Chinese Academy of Agricultural Sciences (CAAS), China

Huaizhi Zhang – Chinese Academy of Agricultural Sciences (CAAS), China

Hongjie Ji – Chinese Academy of Agricultural Sciences (CAAS), China

Huaiyu Long – Chinese Academy of Agricultural Sciences (CAAS), China

**Objective:** To extract, integrate and make cartographic interpretation with soil data from different data resources and with heterogeneous format and feature has been very time and manpower consuming by using mainstream software package such as ArcGIS, MapInfo and Map GIS, especially for making thematic maps with high resolution of vast continents. Purpose of the study is to develop a professional tool to process heterogeneous big soil geo-info from different data resource. The software package should finish data processing and mapping human-computer interactively or automatically.

**Software design and development:** Procedures of soil data abstracting, classifying, integrating and cartographic representing were analyzed. These procedures are normally carried out by data analyzers with help of mainstream database and GIS software packages. Each segmental procedure were identified by their features of whether it can be done automatically or by human-computer interactive way. Based on requirement analysis, the software design were finished. The whole design consisted of system architecture, system data supporting platform, system modules and models. For IMAT software development, C# was used as the programming language, NET Framework 4 Extended was applied as development environment, functions and components from software packages of ArcGIS, Access and DotNet Bar were called.

**IMAP Functions:** IMAP has 38 independent modules and can provides the main functions for treatment big soil spatial data and drafting thematic maps. The advantages of system is its ability to process big soil data with heterogeneous format and structure. When making cartographic representations with IMAT, rules and parameters for the whole map that is composed of a large amount of map sheets can be set, through which differentiated data extracting and mapping for map sheets can be carried out. The data analysis and mapping procedures can be operated by intelligent and automatic way as well as human-computer interactive approach.

By using IMAP, processing of raw soil data collected from different regions of China and drafting soil map of the whole country, which composed by 24,000 map sheets, have been successfully finished just with a couple of data analyzers in acceptable time period, The maps are in good visual quality for official press as well as for internet browsing.

**Conclusion:** IMAT can be applied to analyze and express thematic elements such as soil types, texture, and soil nutrient and contaminant concentration In addition, it can be used mapping with different map scales or large amount map sheets based on big soil data.

**keywords:** Intelligent Mapping Tools (IMAT), China, big soil data
Mapping soil properties for ecological and hydrological modeling
in the typical inland river basin of northwest China

Ganlin Zhang - Institute of Soil Science, Chinese Academy of Sciences, China

Feng Liu - Institute of Soil Science, Chinese Academy of Sciences, China

Xiaodong Song - Institute of Soil Science, Chinese Academy of Sciences, China

Renmin Yang - Institute of Soil Science, Chinese Academy of Sciences, China

Jinling Yang - Institute of Soil Science, Chinese Academy of Sciences, China

Yuguo Zhao - Institute of Soil Science, Chinese Academy of Sciences, China

Decheng Li - Institute of Soil Science, Chinese Academy of Sciences, China

Soil plays a crucial role in regulating ecological and hydrological processes. To better understand ecosystem evolution and ecosystem services and the impacts of human activities and climate change, precise modeling of the ecosystem processes and change scenarios is essential. However, lack of sufficient soil data often limits a reliable modeling that can help make decisions for ecosystem management.

The Heihe River located in the northwest China is the second largest inland river where socio-economic development and ecological degradation have led to increasing demand of water use and caused conflicts in policy making for sustainable development. The recent grand efforts made by scientific community and policy makers for reliable prediction and ultimately efficient use of valuable water resource require sophisticated hydrological modeling which looks upon detailed spatial soil information.

In order to map the fairly large area with contrasting environmental conditions, digital soil mapping technique was adopted. Based on analysis of environmental covariates with a focus on environmental similarity, soil sampling was conducted to cover all representative sites as much as possible. In total about 300 sites were visited and soil profiles were sampled.

Important soil attributes especially those closely related to soil water movement were analyzed and adopted as target mapping objects, which include effective soil thickness, soil organic matter content, soil particle size distribution, soil bulk density, soil water holding capacity and saturated water conductivity. Due to limitation in obtaining some directly measured attributes such as water infiltration rate, pedotransfer functions were built for estimation of those soil parameters.

Digital soil mapping models were developed for various target soil attributes. In general, multiple linear regression models, fuzzy C mean cluster (FCM) based models, various kriging models, Random Forest models were tested and compared. Especially three dimensional predictions of soil organic matter content, soil particle size distribution as well as some soil hydraulic properties were made, which provided possible optimization of soil horizon parameters for hydrological modeling.

keywords: digital soil mapping, inland river basin, hydrological modeling
Digital mapping of soil carbon in a soil profile using image analysis

Yakun Zhang – University of Wisconsin-Madison, USA

Alfred Hartemink – University of Wisconsin-Madison, USA

Jenna Grauer-Gray – University of Wisconsin-Madison, USA

In this study, we explored the CIEL*a*b* color coordinates extracted from a digital image as covariates for soil profile mapping of SOC in an Alfisol. The soil profile wall (1.0x0.9m) was divided into a 10x10 cm raster, and samples were collected at the center of each grid for SOC analysis. Digital images with a horizontal and vertical resolution of 300 dpi were taken. The digital image was cropped and georeferenced to the profile wall, and scaled to 1-cm resolution. The 90 soil samples were separated into a calibration (45) and validation dataset (45) by odd-even transect numbers. Regression kriging was used to build a SOC-color model. SOC concentrations ranged from 16.7 g/kg in the A horizon to 0.9 g/kg in the 2Bw horizon. Overall, SOC was correlated with b* (-0.75), and a* (-0.50), but not correlated with L* (0.08). Three vertical color layers were determined from the SOC map which were corresponded with the Ap (0-12cm), Bt (12-58cm), and 2Bw (58-90cm) horizons. Within the layers, there was considerable variation. Validation for the SOC map had a R2 of 0.80 and a RMSE of 1.64 g C/kg. The b* coordinate is useful for SOC mapping, the L* coordinate is sensitive to shadows caused by animal holes and other features, and the a* coordinate can be used to distinguish the 2Bw horizon. We conclude that CIEL*a*b* coordinates are suitable covariates for soil profile C mapping and may improve soil horizon delineation, as well as assessment of variation within soil horizons.

keywords: soil profile, soil carbon, digital mapping, CIE L*a*b*, image analysis
Review on the research of cultivated land quality monitoring in
China

Rui Zhao – School of Land Science and Technology, China University of Geoscience, Beijing, China
Kening Wu – School of Land Science and Technology, China University of Geoscience, Beijing, China

The monitoring of cultivated land quality is an important technical support for the manage-
ment of quantity, quality and ecology of cultivated land in China. The cultivated land is the
fundamental guarantee of national food security, the foundation and the lifeblood of agricultural
development and agricultural modernization, and the basic national conditions of many people
and less land determine the special importance and strategic nature of China's cultivated land
resources. Carrying out the monitoring of cultivated land quality is of great significance to im-
plement the strictest cultivated land protection system, guarantee the national food security and
realize the sustainable utilization of land resources.

keywords: Cultivated land, soil, quality monitoring
Methods comparison on prediction accuracy of soil properties de-
rived from soil-landscape principles in a middle size watershed of
Qinghai-Tibetan Plateau, China

Xia Zhao - Qinghai Normal University, China

Decheng Li - Institute of Soil Science, Academia Sinica, Nanjing, China

Feng Liu - Institute of Soil Science, Academia Sinica, Nanjing, China

Pingchao Shi - Qinghai Normal University, China

Guangchao Cao - Qinghai Normal University, China

Kelong Chen - Qinghai Normal University, China

Huangshui river watershed is one of most important agricultural region in Qinghai-Tibetan Plateau, which supports more than 2/3 of population and food provision of Qinghai Province, and thus burgeoning an urgent demand on digital soil mapping that can tells not only the spatial distribution s of soil properties, but also the spatial variability and temporal dynamic of them. Given the facts of lacking expert knowledges on local soils, this paper adopted a purposive sampling strategy to collect comparative data on typical soil properties over yeas of 1980s to nowadays. The sample positions were firstly decided according to the location of typical soil profiles recorded in the Second Soil Census Data of China in 1980s, and then combined with some other factors such as area and accessibility etc. From which, a total amount of 61soil profiles with 215 layers of stratified soil samples was collected, which covers 15 major soil sub-types in this region.

Based on laboratory data of organic matter, total nitrogen, soil texture, pH, bulk density and content of CaCO3 of each sample, integrated with environmental variables of elevation, slope, aspect, slope surface curvature that derived from digital elevation map, and temperature, precipitation, land use/cover, geomorphology and geology that extracted from corresponding thematic maps, this paper conducted three soil-landscape reasoning works at different spatial scales, which including predictive mapping for soil sub-types in Minhe County within an area of 1900 km² that covers 14 sub-types, spatial distribution characteristics and its impact factors of soil organic matter in Beichuan River Watershed within an area of 3900 km² that covers 14 sub-types, and the spatial distribution and influencing factors analysis of cropland on soil total nitrogen in Huangshui River Watershed within an area of 162000 km² that covers 18 sub-types.

From which, we learned some specific rules of soil-landscapes at different scale and their signifi-
cant difference on interpolation results for different attributes, which is very helpful in understand-
ing the scale-dependent relationship between soil attributes and spatial interpolation accuracy. In
addition, given the significant impacts of interpolation methods on producing a digital soil map,
this paper also provided a set of interpolation strategy for non-uniform discrete point by comparing
prediction accuracies of different methods, which including ordinary kriging interpolation, fuzzy
reasoning and random forests etc. Given the fact of lacking such researches in this region, this
work can provide an initial reference on producing a more accuracy digital soil map in future.

**keywords: digital soil mapping, soil-landscape modeling, fuzzy reasoning, purposive sampling,**
**Huangshui river watershed**
Quantitative relationships between soil properties and hyperspectral characteristics and their possible application in soil classification in Qinghai Province, China

Xia Zhao - Qinghai Normal University, China

Decheng Li - Institute of Soil Science, Academia Sinica, Nanjing, China

Feng Liu - Institute of Soil Science, Academia Sinica, Nanjing, China

Hyperspectral data, especially the visible near-infrared spectra, has been evidenced a powerful tool in interpreting relationships between soil properties and their spatial-temporal heterogeneity, which has been used widely in building inversion models between them and furtherly making predictive maps for many physical and chemical soil properties, such as organic matter, soil moisture, mineral composition and soil texture. However, limited by the complexity of soil property-spectral responses and the difficulties in defining soil groups by using a few of soil properties, the soil identification and classification via spectra data remains ascendant in many regions all over the world.

Facilitated by the fundamental project of Soil Series Survey in Western China, specifically in Qinghai Province, this paper first collected 161 soil profiles that covering mostly sub-soil types in this region, conducted both laboratory tests on major soil properties that tightly related to soil classification, including organic matter, total nitrogen, soil texture, pH, bulk density, content of CaCO3, effective phosphorus, rapidly-available potassium, total phosphorus, total potassium, and cation exchange capacity (CEC) etc., and reflectance spectra data for each stratified layers samples within all profiles. Then conducted the parameters inversion process by setting the cross-examination variances as the objective function, from which two sets of inversion models were produced, namely acceptable and unacceptable respectively. Thirdly, those acceptable inversion models were used furtherly into identifying and discriminating of different soil types, which is mainly extracted from the available soil map at 1: 4 000 000 scales that provided by Institute of Soil Science, Academia Sinica, Nanjing, China.

The result remains in processing right now, but one conclusion we can draw currently is that some valuable findings could be expected from this work, since it is the first time to do a systemic examination on the relationships between soil properties and their hyperspectral response characteristics in this region, which covers almost all soil types (163 soil species) of this region that recorded in the Second Soil Census Data of China, which is conducted in 1980s and used as the guidance principle of soil sampling in this work. Besides, an additional output of this work is an initial hyperspectral database for typical soil profiles at the stratified layers as well.

**keywords:** Hyperspectral data, Reflectance spectra, Soil properties, Soil classification, Qinghai Province
Rainfall magnitudes and digital soil mapping using land surface
dynamic feedback patterns

A-Xing Zhu - Department of Geography, University of Wisconsin-Madison, USA

Canying Zeng - School of Geography, Nanjing Normal University, China

Previous studies demonstrated that the pattern of land surface dynamic feedback patterns captured by remote sensing images after a rainfall event can be used to derive environmental covariates for predicting soil spatial variation over low relief areas. The impact of the magnitude of rainfall on the performance of the derived environmental covariates for soil property prediction is still unknown. This paper shows our findings from our recent investigation of this impact in an effort to shed some lights on guidance for choosing suitable observation periods based on rainfall magnitude. A study area of over 2,000 square kilometers in Anhui Province of China was used for this investigation. An individual predictive soil mapping method (iPSM) was used to predict soil texture over space based on the environmental covariates derived from MODIS image based on land surface dynamics. Ten observation periods were selected after rainfall events with varying rainfall magnitudes. The results showed strong positively correlation between rainfall magnitudes and prediction accuracies. When the rainfall reaches a certain amount, the prediction accuracy became stable. The threshold of rainfall for the land surface dynamic feedback method in this study area is about 20 mm for both sand and clay content. The predicted maps based on different observed periods with similar rainfall magnitudes show only slight differences. It can be concluded that rainfall magnitudes have obvious effect on the prediction accuracy of soil texture mapping. Larger rainfall amount will improve the prediction accuracy when using the LSDF. It was also observed through this study that strong winds, high evaporation and low relative humidity during the observed periods also improve the prediction accuracy.

**keywords:** Digital soil mapping, remote sensing, MODIS, SoLIM, Land surface feedback patterns, soil texture
# Predictive modelling of soil properties using hyperspectral images and different multivariate regression techniques

Daniel Žížala – Research Institute for Soil and Water Conservation, Czech Republic

Tereza Zádorová – Czech University of life Science, Czech Republic

Image spectral data, particularly airborne hyperspectral data, has been proven to offer efficient input data to map spatial variability of important soil properties like organic carbon or soil texture. However, there are still many limitations in the use of the image hyperspectral data for soil properties quantification. On one hand, there are restraints connected to data collecting and surface characteristics (soil covered with vegetation, litter, dust or soil crust). On the other hand, the spectral information is considerably influenced by soil properties (soil moisture, soil roughness, soil texture or size of soil aggregates) and heterogeneity of environmental settings.

Our study aimed to assess the influence of soil conditions, environmental settings and use of different multivariate regression techniques on the prediction accuracy of models assessing soil organic carbon and texture classes. The study was performed at six study sites about 1 km² large representing the most extensive soil units of the agricultural land in the Czech Republic (Chernozems and Luvisols on loess and Cambisols and Stagnosols on crystalline rocks). The adopted methods included extensive field sampling, laboratory analysis and predictive modelling of selected soil surface properties using aerial hyperspectral data. The influence of site-specific conditions on prediction of soil properties was assessed. Different multivariate regression techniques (Partial Least Square, Random forest, Support Vector Machine and Artificial neural network) were applied in the predictive modelling of soil properties.

The prediction accuracy (R2) of the best performing models predicting the soil properties varies in range 0.8–0.91 for soil organic carbon content, 0.21–0.67 for sand content, 0.4–0.92 for silt content and 0.38–0.89 for clay content. Among the applied methods, the machine learning Support Vector Machine and Artificial neural network methods showed the best prediction accuracy. The prediction accuracy was influenced by the spatial variability of soil units and parent material. Better model performance was in most cases achieved in more homogeneous Chernozem and Luvisol loess regions while the models applied in heterogeneous Cambisols and Stagnosols regions showed lower accuracy. The results showed that the sites with heterogeneous structure of the soil cover and parent material will require more precise local-fitted models and use of further auxiliary information such as terrain or geological data.

**keywords:** soil properties, imaging spectroscopy, hyperspectral image, multivariate techniques
Index

Abbaszadeh Afshar
*Farideh*, 13

Abreu
*Valdemir S.*, 14, 69

Acharige
*Niranjan*, 15, 38

Ackerson
*Jason*, 166, 263
*Jason P.*, 16

Adamchuk
*Viacheslav*, 17, 18, 113–115

Adhikari
*Kabindra*, 137, 203

Aguado
*Pedro L.*, 165

Ahamed
*Tofael*, 107

Akbari Moghaddam
*Ali Reza*, 184

Alamar
*Carmen M.*, 71

Albrecht
*Alain*, 19

Allo
*Myriam*, 19

Alonso
*Carmelo*, 20

An
*Yi-Ming*, 198
*Yiming*, 21

Andina
*Diego*, 212

Angelini
*Marcos Esteban*, 22

Anjos
*Lúcia Helena Cunha Dos*, 60

Anschlag
*Kerstin*, 98

Arrouays
*Dominique*, 125, 167, 173, 207, 213

Artemyeva
*Zinaida*, 123

Atkinson
*Peter*, 122

Attinger
*Sabine*, 26

Ayoubi
*Shamsollah*, 13

Baggaley
*Nikki*, 23

Bahls Fogaca
*Geisy*, 234

Bakacsi
*Zsófia*, 187

Bakkenes
*Michel*, 241

Baltensweiler
*Andri*, 24

Barca
*Emanuele*, 25

Bardy
*Marion*, 126

Baroni
*Gabriele*, 26

Barthes
*Bernard*, 126, 173

Bartholomeus
*Harm*, 27, 68, 108

Bashar
*Ibrahim*, 28

Batjes
*Niels*, 29, 154

Bautista
*Francisco*, 30, 31

Beaudette
*Dylan*, 32, 137, 211

Bedard-Haughn
*Angela*, 33

Bedin
*Luis Gustavo*, 217

Beitz
*Thoralf*, 83
*Toralf*, 204
Bellete
Tegbaru, 34

Ben-Dor
Eyal, 85, 238

Benito
Rosa M., 20

Bentham
Murray, 33

Bernoux
Martial, 199

Beucher
Amélie, 35, 37, 42, 161, 162

Bierkens
Marc F.P., 241

Birch
Gavin, 119

Bishop
Thomas, 15, 38, 119, 135, 230
Tom, 121

Biswas
Asim, 17, 18, 39, 114, 115

Black
Helaina, 50

Blasch
Gerald, 40

Bockheim
James, 203

Bogner
Christina, 41

Boman
Anton, 35, 42

Boruvka
Lubos, 44, 85

Bosmans
Joyce H.C., 241

Bouma
Johan, 209

Bourennane
Hocine, 125, 207

Bourgeois
Jean, 176

Bourouah
Mohamed, 83

Bouwman
Arno, 241

Breger
Pascale, 227

Bregt
Arnold K., 108

Broll
Gabriele, 98

Brossard
Michel, 199

Brown
David J., 195

Brungard
Colby, 45, 146, 183, 184

Brus
Dick, 46, 102, 228, 254, 268
Dick J., 256

Bueno
Alvaro, 78

Bui
Elisabeth N., 108

Buitrago Escobar
Jeiner, 47

Burgos
Stéphane, 48

Butler
Benjamin, 49

Buttafuoco
Gabriele, 25, 52

Bódis
Judit, 225

Büchele
Dominique, 83

Camacho-Tamayo
Jesús Hernán, 180

Campbell
Grant, 50

Cao
Guangchao, 279

Carmon
Nimrod, 85

Carnieletto Dotto
Andre, 234

Carrick
Samuel, 152

Cassman
Kenneth G., 130

Castaldi
Fabio, 51

Castellanos
Maria Teresa, 165

Castrignano
Annamaria, 13

Castrignanò
Annamaria, 25, 52

Cavanagh
Jo-Anne, 53

Cerkowniak
Darrel, 33

Chabala
Lydia, 54

Chabrillat
Sabine, 51, 259

Chagas
César S., 250

Chao
Li, 55

Chapoto
Antony, 54

Chartin
Caroline, 51

Chen
Kelong, 279
Songchao, 56, 114, 116, 117, 134, 136
Zueng-Sang, 139, 236, 237

Chudy
Thomas, 83

Cid-Diaz
Felix, 20

Claessens
Lieven, 130

Clairotte
Michael, 173

Clingensmith
Christopher M., 57, 58, 160

Conocenti
Christian, 82

Cooper
Hannah, 59

Cornu
Sophie, 126

Correa
Diana Lucia, 47

Corstanje
Ron, 271
Ronald, 50, 92, 270

Costa
Adriana Monteiro Da, 179
Elias Mendes, 60

Cotos-Yañez
Tomas, 77

Coulon
Frederic, 71

Cousin
Isabelle, 207

Creamer
Rachel, 157, 175
Rachel E., 224

Cropper

Wendell P., 159, 160

Crucil
Giacomo, 61

Csorba
Adam, 157
Ádám, 168

Cudennec
Christophe, 227

Dahlhaus
Peter, 155

Dahmardeh
Khodadad, 184

Dai
Yongjiu, 220

Dalal
Ram, 62

Dalmolin
Ricardo, 215
Ricardo S. D., 70

Dang
Yash, 62, 181

Darghan Contreras
Aquiles Enrique, 180

de Gruijter
Jaap, 151

de Gruitjer
Jaap, 213

de Miguel
Angel, 205

de Pablo
Miguel Angel, 76

De Smedt
Philippe, 63

de Souza
Eliana, 14, 86, 153, 233, 250, 251

Debeljak
Marko, 175

Declercq
Ynse, 63

Del Monte
Juan Pablo, 165

Delle Grazie
Fabio, 64

Dematte
Jose, 217

Deng
Xunfei, 65, 201

Dennerley
Claire, 66

Denoroy
Pascal, 132

Deters

Ralph, 33

Dhawale

Nandkishor, 115

Doan

Thuy, 67

Dobos

Endre, 157, 168

Doff Sotta

Eleneide, 199

Dokuchaev

Pavel, 156

Domingues Franceschini

Marston Héracles, 68

Domke

Grant, 191

Don

Axel, 110

Dos Santos

Eliana Elizabet, 69

Dotto

Andre C., 70

Douglas

Reward, 71

Dovletyarova

Elvira, 208, 252

Drufin

Sebastien, 157

Du

Changwen, 144

Dubois

Remy, 227

Dworak

Volker, 83

Dzeroski

Saso, 175

Eftekhari

Kamran, 183

Ellenkamp

Reinier, 72

Ellili

Yosra, 73

English

Luc, 18

Evans

Brad, 15

Eveillard

Philippe, 132

Everett

Mark, 16

Fajardo

Mario, 15, 74, 75, 193

Faria

Raiza M., 86

Faucherre

Samuel, 223

Fengrong

Zhang, 55

Fernandes Filho

Elpidio I., 112

Elpídio I., 14, 69, 86, 153, 233, 250, 251

Fernandez

Susana, 76–78

Fernandez-Ugalde

Oihane, 131

Fiantis

Dian, 79, 170

Fick

Johanna, 148

Field

Damien, 75

Filippi

Patrick, 15

Finke

Peter, 67, 80

Fletcher

Dale, 235

Foerster

Saskia, 259

Fongaro

Caio Troula, 68

Fonyó

Tamás, 106

Forkuor

Gerald, 103

Fouad

Youssef, 227

Fraefel

Marielle, 177

Francelino

Marcio R., 112

Márcio R., 14, 69, 153, 233

Franke

Jappe, 246

Friedlingstein

Pierre, 167

Gagkas

Zisis, 81

Gale
Andrew, 28

Gallegos
Angeles, 30, 31

Gan
Qianjun, 18

Garosi
Younes, 82

Garrido
Alberto, 240

Gasch
Caley K., 195

Ge
Yong, 118
Yufeng, 263

Gebbers
Robin, 83, 204

Geng
Xiaoyuan, 94

Gheyle
Wouter, 176

Gholizadeh
Asa, 44, 85

Gibson
Andy, 28

Gill
Laurence, 64

Gimona
Alessandro, 194

Giráldez
Juan Vicente, 249

Goge
Fabien, 19

Gomes
Lucas C., 86

Gomez
Cécile, 125

Gornushkin
Igor, 83

Gouny
Laetitia, 132

Grauer Gray
Jenna, 88

Grauer-Gray
Jenna, 87, 277

Gregory
Linda, 155

Greve
Mogens, 35, 37, 196
Mogens Humlekrog, 42

Gries
Philipp, 89

Gris
Diego J., 70

Grob
Urs, 177

Grunwald
Sabine, 57, 58, 90, 159, 160

Guanter
Luis, 259

Gubler
Andreas, 91

Guilpart
Nicolas, 130

Guo
Horng-Yuh, 139, 237

Gusnidar
Gus, 79

Gómez-Barbara
L., 212

Hadizadeh
Mojtaba, 183

Hainsworth
Sharn, 152

Hallett
Stephen, 270

Hannam
Jacqueline, 50, 92, 271

Harris
Willie G., 159

Hartemink
Alfred, 87, 88, 93, 218, 277

Hashimoto
Shoji, 266

He
Juanxia, 94

Hedley
Carolyn, 95, 152

Hegde
Rajendra, 230

Heggemann
Tobias, 83, 96, 124, 131

Heidkamp
Arne, 109

Heil
Jannis, 97, 171, 219, 265
Kurt, 83

Heim
Birgit, 223

Heitkötter
Julian, 219

Hellwig
Niels, 98

Hengl
Tom, 130
Tomislav, 94, 99, 220

Heung
Brandon, 33

Heuvelink
Gerard, 216, 220, 228
Gerard B. M., 22
Gerard B.M., 130, 256

Hewison
Richard, 101

Hierold
Wilfried, 245

Hillier
Stephen, 49

Hobley
Eleanor, 100

Hol
Gera, 27

Holland
Jonathan, 101

Hoogland
Tom, 102

Hoppe
Lara, 83

Hounkpatin
Ozias, 103

Houska
Jakub, 44

Hu
Bifeng, 117
Jie, 117

Huang
Hsin-Hui, 113
Jingyi, 66, 101, 104, 269

Hugelius
Gustaf, 223

Hughes
Philip, 105

Humlekrog Greve
Mogens, 161, 162

Hussin
Hilda, 111

Ibatulina
Sophiya, 252

Ibs-Von Seht
Malte, 124

Illés
Gábor, 106, 187

Imaya
Akihiro, 266

Isee Network
NA, 218

Ishizuka
Shigehiro, 266

Islam
Md Monjurul, 107

Ismail
Ashraf, 18

Iversen
Bo Vangsø, 196

Ivushkin
Konstantin, 108

Jacobs
Anna, 109

Jaconi
Angélica, 110

Jafari
Azam, 80

Jahanshiri
Ebrahim, 111

Jameux
Magali, 19

Jamsrandorj
Tom, 33

Jetten
Victor G., 164

Jeune
Wesly, 112

Ji
Hongjie, 273–275
Wenjun, 17, 18, 56, 113–115

Jia
Xiaolin, 116, 117

Jien
Shih-Hao, 237

Jin
Yan, 118

Johnson
Liana, 119

Jones
Edward, 15, 120

Kanaeva
Svetlana, 252

Kaneko
Shinji, 266

Karltun
Erik, 34

Karunaratne
Senani, 121

Kassai
Piroska, 225

Keay
Caroline, 270

Keller
Armin, 91, 177

Kemp
David, 123

Kempen
Bas, 22, 154

Kening
Wu, 260

Ker
João C., 250

Kersebaum
Christian, 83

Khozin
Arthur, 122

Kidanu
Selamyihun, 34

Kidd
Darren, 145

Kikas
Tambet, 197

Killaars
Lars, 241

Kirillova
Nataliya, 123

Kitchen
Newell, 253

Klement
Ales, 85

Knadel
Maria, 37

Knotters
Martin, 247

Kooistra
Lammert, 68, 174

Koomans
Ronald, 72, 247

Koor
Nick, 28

Koszinski
Sylvia, 96, 124

Kramer
Henk, 246

Kriegs
Stefanie, 100

Krotov
Dmitry, 214

Krug
Paul, 33

Kumar
Rohini, 26

Kumke
Michael, 83

Kühn
Peter, 89, 202

Kühnel
Anna, 41

Laborczi
Annamária, 187, 232

Lagacherie
Philippe, 125

Landré
Amelia, 126

Lang
Vince, 127

Lark
Murray, 59, 128, 129

Lauzon
Sophie, 113

Lavelle
Patrick, 149

Le Bas
Christine, 207

Lebdi
Fethi, 150

Leclerc
Maxime, 17, 114

Lee
Wonsuk, 159, 160

Leenaars
Johan, 29
Johan G.B., 130

Leenen
Matthias, 83, 96, 131

Legier
Patrick, 19

Leksono
Eko, 115

Lemercier
Blandine, 73, 132

Lessels
Jason, 121

Li
De-Cheng, 140
Decheng, 276, 279, 280
Hongyi, 133
Shuo, 56, 134, 135
Xinming, 268

Yan, 117

Liang

Zongzheng, 136

Libohova

Zamir, 137, 264

Ließ

Mareike, 138

Lilburne

Linda, 152

Lilly

Allan, 23, 50, 81, 194

Limburg

Han, 246, 247

Lindbo

David, 137

Liu

Feng, 140, 276, 279, 280

Xiao-Nan, 139, 236

Lizarazo Salcedo

Ivan Alberto, 141

Lobsey

Craig, 134, 142, 195, 221, 254

Loecke

Terry, 264

Long

Huaiyu, 273–275

Lopez-Brody

Nathan, 45

Losada

Juan Carlos, 147

Lu

Xiaonan, 65, 201

Yuanyuan, 267

Lungu

Olipa, 54

Löhmannsröben

Hans-Gerd, 204

Ma

Fei, 144

Wanzhu, 56, 65

Yuxin, 143

MacLeod

Andrew, 155

MacMillian

Robert, 94

Mahns

Benjamin, 83

Main

Angela, 190

Maiwald

Martin, 83

Michael, 83

Mallawaarachchi

Thilak, 181

Malone

Brendan, 15, 143, 145, 146, 162, 172, 173, 185

Brendan P, 213

Brendan Philip, 229

Mamo

Tekalign, 34

Mantica Kreimeier

Luiza, 234

Marchant

Ben, 200

Benjamin P., 133

Marcondes

Robson, 60

Marques

Greissz E., 69

Marschner

Bernd, 97, 171, 219

Martin

Manuel, 125, 207

Tim, 94

Martin-Sotoca

Juan José, 147

Martinsson

Johan, 192

Marx

Kirstin, 148

Mathieu

Jerome, 149

Mathlouthi

Majid, 150

Mattbäck

Stefan, 35, 42

Mayorga Lozano

Diana, 180

Mayr

Thomas, 92

McBratney

Alex, 74, 75, 104, 105, 143, 145, 146, 151, 182, 185, 189, 193, 213

Alex., 120

Alex. B., 229

McInnes

Kevin J, 16

McNeill

Stephen, 53, 152
Medeiros Neto
Luiz Gonzaga, 217

Medyckyj-Scott
David, 155

Meier
Martin, 153

Meimivand
Leila, 82

Melo Demattê
José Alexandre, 68

Mendes de Jesus
Jorge, 154, 155

Mendonça
Bruno A.F., 251

Menzies
Neal, 181

Meshalkina
Joulia, 156, 214

Meunier
Jean-Dominique, 126

Michaelis
Xandra, 97

Micheli
Erika, 157

Michot
Didier, 73, 227

Michéli
Erika, 168

Mikheeva
Irina, 158

Minasny
Budiman, 74, 75, 79, 104, 105, 120, 126, 143, 145, 146, 162, 170, 172, 173, 182, 185, 189, 213, 229

Miura
Satoru, 266

Mizgirev
Alexander, 83

Mizuta
Katsutoshi, 159, 160

Moedt
Ellen, 252

Molin
José Paulo, 68

Mooney
Sacha, 59

Moonjun
Ruamporn, 164

Morató
Maria Carmen, 165

Morgan

Cristine, 166, 263
Cristine L, 16

Mouazen
Abdul M., 71, 169

Moura Bueno
Jean Michel, 234

Muaketov
Duško, 255

Mudge
Paul, 152

Mulder
Vera Leatitia, 167

Mutuma
Evans, 168

Myers
Brenton D., 159, 160

Møller
Anders Bjørn, 161, 162

Nanko
Kazuki, 266

Nanni
Marcos Rafael, 217

Nault
Jacques, 18

Nave
Luke, 191

Nawar
Said, 71, 169

Nelson
Malik, 170

Neumann
Carsten, 259

Neuser
Anne, 171

Ng
Wartini, 172, 173

Ni
Zhihua, 65

Nielson
Rod, 66

Nijp
Jelmer, 174

Nikoloski
Stevanche, 175

Noguchi
Ryozo, 107

Nol
Linda, 178

Noll
Dorothea, 48
Note

Nicolas, 176

Nugroho

Kurniawan, 262

Nur Marahaini Mohd. Nizar

NA, 111

Nussbaum

Madlene, 177

Nysten

Sylvan, 178

Nørgaard

Henrik, 35, 42, 196

O'Neale

Dion, 211

Odgers

Nathan, 162

Oertel

Marcel, 83

Ojeda-Magañas

B., 212

Oliveira

Amanda Ribeiro De, 179

Ordoñez

Celestino, 77, 78

Orjuela-Osorio

Iván, 180

Orton

Thomas, 38, 62, 181

Tom, 121

Osone

Yuko, 266

Ostermann

Markus, 83

Owens

Phillip, 137

O'huallachain

Daire, 224

P. Wall

David, 175

Padarian

José, 182

Pahlavan-Rad

Mohammad Reza, 183, 184

Pallegedara Dewage

Sanjeewani Nimalka Somarathna, 185

Pan

Xianzhang, 186, 258

Papritz

Andreas, 177

Paterson

Stacey, 189

Peaslee

Steve, 137

Peng

Yi, 37

Penizek

Vit, 44

Penu

Priit, 197

Peon

Juanjo, 76

Perry

Charles, 191

Petersen

Hauke, 124

Rasmus Jes, 196

Phillips

Michelle A, 159

Michelle A., 160

Pichelin

Pascal, 73, 227

Piikki

Kristin, 192

Pinheiro

Helena Saraiva Koenow, 60

Pino

Vanessa, 193

Poggio

Laura, 101, 194, 206, 255

Matteo, 195

Power

Catherine, 135

Prietz

Roland, 109

Prinds

Christian, 196

Pringle

Matt, 181

Matthew, 62

Pulatov

Alim, 108

Putku

Elsa, 197

Pásztor

László, 106, 187, 232

Pätzold

Stefan, 83, 96, 124, 131

Pérez-Fernández

Estefania, 23, 206

Estefanía, 190

Qi
Long, 17

Qin
Cheng-Zhi, 21, 198

Quarto
Francesco, 52
Ruggiero, 52

Quintanilla-Domínguez
J., 212

Ramifehiarivo
Nandrianina, 199

Ramirez-Lopez
Leonardo, 110, 257

Ramos
Miguel, 76

Ratnayake
Renuka, 121

Ravalontsalama
Mamy, 199

Rawlins
Barry, 200

Razafimbelo
Tantely, 199

Razakamanarivo
Herintsitohaina, 199

Regnier
Pierre, 167

Ren
Zhouqiao, 65, 201

Rentschler
Tobias, 202

Reyes-Rojas
Luis, 203

Ribeiro
Eloi, 29, 154, 155

Richard
Antoine, 126

Richter
Andrea, 224

Riebe
Daniel, 83, 204

Ritchie
Alistair, 155

Rivas
David, 205

Rizzo
Rodnei, 68, 217

Robertson
Jean, 23, 190, 206

Roca-Pardiñas
Javier, 77

Rocha
Genelicio C., 112

Roecker
Stephen, 264

Roman Dobarco
Mercedes, 207

Romzaikina
Olga, 208

Romzaykina
Olga, 252

Román Sánchez
Andrea, 249

Rosendahl
Rainer, 42

Rossiter
David, 210, 272
David G., 209

Rosyadi
Ibnu, 262

Roudier
Pierre, 32, 95, 211

Rubiano Sanabria
Yolanda, 47, 180

Ruelas
R., 212

Ruggieri
Sergio, 25

Ruipérez Gonzalez
Maria, 130

Rühlmann
Jörg, 83
Madlen, 83, 204

Saa-Requejo
Antonio, 240

Saby
Nicolas, 125, 126, 132, 173
Nicolas P.A., 207
Nicolas Pa, 213

Saey
Timothy, 72, 176

Saifuzzaman
Md, 113

Salama
Ayman, 111

Samaniego
Luis, 26

Samson
Roeland, 63

Samsonova
Vera, 214
Samuel-Rosa
Alessandro, 215

Santana
Felipe C., 69

Santos
Eliana E.S., 14
Nerilson T., 233

Sarjant
Sam, 235

Sawicka
Kasia, 216

Sayão
Veridiana Maria, 217

Schaefer
Carlos E.G.R, 69
Carlos E.G.R., 86, 250, 251

Scheithauer
Herrmann, 83

Schmid
Karsten, 96
Thomas, 83, 204

Schmidt
Karsten, 89, 202, 257

Scholten
Thomas, 89, 202, 257

Schulte
Rogier P.O, 224

Schulze
Darrell, 218

Schwab
Peter, 91

Schwarz
Katharina, 219

Sefton
Michael, 66

Shahriari
Ali, 183

Shangguan
Wei, 220

Shaw
Cindy, 94

Shekaari
Parviz, 82

Sheklabadi
Mohsen, 82

Shi
Pingchao, 279
Zhou, 56, 115–117, 133, 134, 136, 221

Shrestha
Dhruba Pikha, 164

Sidorova

Valeriia, 222

Siewert
Matthias, 223

Sileova
Tatiyana, 123

Simao Diniz Dalmolin
Ricardo, 234

Simo
Iolanda, 224

Simons
Bruce, 155

Singh
S.K, 230

Sisák
István, 225

Sjogersten
Sofie, 59

Skála
Jan, 226

Soltani
Ines, 227

Sommer
Michael, 124, 245

Song
Xiao-Dong, 140
Xiaodong, 276
Yuanhong, 195

Soons
Merel, 174

Souza
Eliana, 112

Stadig
Henrik, 192

Steffens
Markus, 100

Steinbuch
Luc, 228

Stenberg
Bo, 115, 261

Steuer
Annika, 124

Stevenson
Bryan, 152
Sean, 200

Stichelbaut
Birger, 176

Stockmann
Uta, 15, 185, 229

Stoof
Cathelijne, 244

Stoorvogel
Jetse, 241, 252

Stumpe
*Britta*, 97, 171, 219, 265

Stumpf
*Felix*, 91, 257

Su
Yue, 113

Subramanian
*Dharumarajan*, 230

Sudarsan
*Bharath*, 17

Sudduth
Kenneth, 253

Sumpf
*Bernd*, 83

Sun
*Xiaolin*, 231

Sutanudjaja
*Edwin H.*, 241

Svoray
*Tal*, 122

Szabó
*József*, 187

Szatmári
*Gábor*, 187, 232

Söderström
*Mats*, 192

Tack
*Filip*, 63

Takács
*Katalin*, 187, 232

Tanaka
*Nagaharu*, 266

Taques
*Renato C.*, 233

Tarquis
*Ana*, 240
*Ana M.*, 147
*Ana Maria*, 20, 165, 205, 212

Taylor
*James*, 101
*James A.*, 40

Temme
*Arnaud*, 174, 244, 245

ten Brink
*Ben*, 241

ten Caten
*Alexandre*, 70, 234

Terhoeven-Urselmans
*Thomas*, 235

Teuling
*Ryan*, 174

Thas
*Olivier*, 80

Theocharis
*John*, 238

Thiel
*Michael*, 103

Thompson
*James*, 218
*Jim*, 137

Thuries
*Laurent*, 19

Tiansheng
*Wen*, 55

Tijs
*Marco*, 246

Todoroff
*Pierre*, 19

Torre
*Ivan G.*, 147

Torres
*Gemma*, 224

Toth
*Gergely*, 131

Trajanov
*Aneta*, 175

Tran Minh
*Tien*, 67

Triantafilis
*John*, 66, 101, 104, 269

Troldborg
*Mads*, 23

Tsai
*Chen-Chi*, 236

Tsakiridis
*Nikolaos*, 238

Tsui
*Chun-Chih*, 139, 236, 237

Tziolas
*Nikolaos*, 238

Ugawa
*Shin*, 266

Vacek
*Oldrich*, 44

Vadnai
*Peter*, 157

Valderrabano
*Jesus*, 78

Valtera
Martin, 239

Valverde
Omar, 240

van Beek
Rens L.P.H., 241

van Bussel
Lenny G.J., 130

Van De Vijver
Ellen, 243

Van den Berghe
Hanne, 176

van der Ent
Ruud J., 241

van der Esch
Stefan, 241

van der Kroef
Ilona, 244

van der Meij
Marijn, 245

Van der Veeke
Steven, 246

van Eck
Christel Melissa, 167

Van Eetvelde
Veerle, 176

van Egmond
Fenny, 72, 246, 247

van Erp
Peter, 235

Van Meirvenne
Marc, 176, 243

van Meirvenne
Marc, 72

Van Oost
Kristof, 82

van Oostrum
Ad, 29

Van Rees
Ken, 33

van Ruitenbeek
Frank J.A., 164

van Voorn
George, 174

van Wesemael
Bas, 51

van Zijl
George, 248

Vandenbygaart
Bert, 94

Vangso Iversen
Bo, 161, 162

Vanwalleghem
Tom, 249

Vargas
Ronald, 255

Vasat
Radim, 44

Vasconcelos
Bruno N.F., 250
Bruno N.F.V., 251

Vasenev
Viacheslav, 252
Vyacheslav, 208

Vasques
Gustavo M, 160
Gustavo M., 159

Vedder
Herman, 235

Veloso
Gustavo V., 86

Ventura
Stephen, 203

Veum
Kristen, 253

Viana
João Herbert Moreira, 179

Viscarra Rossel
Raphael, 114, 134–136, 142, 254

Viscarra-Rossel
Raphael, 195, 221

Voronin
Victor, 214

Vršcaj
Borut, 255

Wadoux
Alexandre M.J-C., 256, 257

Wagner
Peter, 83

Wall
David, 224

Wallinga
Jakob, 174, 245

Wallor
Evelyn, 83

Walter
Christian, 73

Walters
Kent, 33

Walthert
Lorenz, 24, 177

Walvoort
Dennis, 102, 247

Wanders
Niko, 241

Wang
Changkun, 186, 258
Jianghao, 118

Wani
Suhas, 57, 58

Ward
Kathrin, 259

Wawire
Amos, 168

Webster
Richard, 133

Welivitiya
Welivitiyage Don Dimuth Prasad, 143

Welp
Gerhard, 83, 96, 103, 124, 131

Weltzien
Cornelia, 83

Wen
Song, 260

Westerdijk
Kees, 178

Wetterlind
Johanna, 261

Widyatmanti
Wirastuti, 262

Wijewardane
Nuwan, 263

Wilford
John, 108

Willaart
Barbara, 205

Willgoose
Garry, 143

Wills
Skye, 137, 264

Wilmer
Wendy, 200

Wilson
Neil, 193
Peter, 155

Wispott
Melanie, 265

Wu
Kening, 278
Shiwen, 186

Wächter
Daniel, 91

Xia

Fang, 117

Xiong
Xiong, 159, 160

Xu
Aiguo, 273–275
Dongyun, 56
Junfeng, 117

Yamashita
Naoyuki, 266

Yang
Haishun, 130
Jinling, 276
Lin, 21, 46, 268
Renmin, 276
Shunhua, 267
Yuanyuan, 116, 136

Yimam
Yohannes, 166

Yu
Wu, 116

Zalidis
George, 238

Zare
Ehsan, 269

Zawadzka
Joanna, 92, 270, 271

Zeng
Canying, 281
Rong, 272

Zhang
Gan-Lin, 140
Ganlin, 267, 272, 276
Huaizhi, 273–275
Renlian, 273–275
Shupeng, 220
Weili, 273–275
Yakun, 277

Zhao
Rui, 278
Ruiying, 136
Xia, 279, 280
Yu-Guo, 140
Yuguo, 276

Zhen
Xie, 55

Zhou
Jianming, 144
Lianqing, 116

Zhu
A-Xing, 21, 46, 198, 268, 281
Zink

Matthias, 26

Zwertvaegher

Ann, 80

Zádorová

Tereza, 282

Šamonil

Pavel, 239

Žížala

Daniel, 282