# Soil NIR-spectra and high-resolution satellite images to monitor the characteristics of active layer most related to permafrost thermal behaviour, Crater Lake CALM site, Deception Island, Marine Antarctica.

Susana Fernandez - Oviedo University, Spain

Miguel Angel de Pablo - Alcala University, Spain

Miguel Ramos - Alcala University, Spain

Juanjo Peon - Oviedo University, Spain

The test was performed in Crater Lake CALM (Circumpolar Active Layer Monitoring) site. The CALM site is equipped among others with 5 sensors of surface soil temperature, 7 deep boreholes, and 2 air temperature sensors. There are also a CALM net consisting of 16 shallow boreholes (depth of 45 cm) homogenously distributed every 20 meters in the CALM plot. The boreholes are equipped with i-button sensors, which have been recording permafrost active layer temperature data every three hours, every day since 2008. In December 2016, during the last steps of the snow cover melting and the beginning of the permafrost active layer, the surface soil layer of the CALM site was sampled. Soils around the 16 short boreholes were taken. Using a fine borer, the thickness of the active layer was also measured in each short borehole during five consecutive times in alternate days. In laboratory, texture and mineralogy of all samples were analysed. NIR-spectra of the samples were also taken to obtain the spectral signature and quantify the soil colour. The main objective of this test is looking for relationships among the measured soil properties, thermal behaviour, and the evolution of the active layer thickness in the early stages of the snow cover melting. NIR-spectra of soil will be used to build transference functions between the active layer properties most related to the thermal behaviour and satellite images with high spatial resolution, such as QuickBird and WorldView II.

**keywords:** permafrost, active layer monitoring, NIR spectra, satellite images
Large scale modelling of soil organic matter using DTM variables
and geographically weighted regression

Susana Fernandez – Oviedo University, Spain

Celestino Ordoñez – Oviedo University, Spain

Tomas Cotos-Yañez – Vigo University, Spain

Javier Roca-Pardiñas – Vigo University, Spain

Terrain attributes derived from digital elevation model (DEM) have been used widely for map-
ping soil organic matter (SOM). There are significant correlations between terrain variables and
SOM. Terrain variables are exhaustive and spatially extensive and provide potentially volumi-
nous data sets with relevant information at unsampled locations. Nevertheless, at large scales
and depending on the geological and geomorphological setting the correlations between DEM vari-
ables and SOM could have geographical variations. The use of Geographical Weighted Regression
method (GWR) could be very useful to discover spatial patterns of SOM associated to geomor-
phological features. In this paper 350 soil samples distributed over an area of 10.600 km² was
used to analyse the relation between relive variables and SOM. Altitude, slope, distance to the
coast, aspect, seasonal radiation and climate variables as average annual temperature and precip-
itation were calculated at 200 m of pixel size. The calculated DEM variables and concentration
of SOM were analysed using GWR. As result the variables distance to the coast, altitude and
winter radiation explain the most part of the variance (R, 54) in the model. But the coefficients
of the regression equations show geographical dependence. These results have to be interpreted in
relation to the lithology and geomorphological evolution of the studied area.

**keywords: Soil Organic Carbon, Digital Elevation Model, Geographical Weighted Regression,**
**Mapping soil**
Laser scanner technologies to monitoring mountain peatlands re-
covering

Susana Fernández – Oviedo University, Spain

Celestino Ordoñez - Oviedo University, Spain

Jesus Valderrabano – Oviedo University, Spain

Alvaro Bueno – Oviedo University, Spain

The present research is part of a Life Project entitled "Inland Wetlands North of the Iberian Peninsula: Management and restoration of wetlands and hygrophilous environments" TREMEDAL (LIFE 11/ENV/ES/707), in which 25 wetland sites spread all over the north of Spain were selected to be analyzed. In fact, the aim of the project is to protect and restored those sites as well as to improve their conservation status and store seeds of bog plant species.

Comella Polje, a glacio-karstic depression site in Picos de Europa National Park (in Asturias Principality) harboring the most biodiverse peatland plant communities in the Cantabrian Moun-
tain Range is one of the TREMEDAL test sites. The total exclusion of livestock and wild herbivores in three parcels of this area is expected to be followed by changes in top soil morphology and water table regimes with consequences in the evolution of peatland hubs and vegetation.

To quantify the impact of grazing exclusion in the evolution of peatland hubs and rates of vegetation growth, a series of very detailed topographic surveys were made using a terrestrial laser scanner (TLS). The point cloud obtained has a great density (about 0.2 points/cm2), providing a good approximation to the real topography. Both, the terrain inside and outside the parcel are being measured at intervals of one year and compared in order to find differences in the vegetation growth patterns. In 2013, 2014, 2015 and 2016 points clouds were obtained. The comparation between the four DTM obtained show significant differences in terrain elevation between points inside and outside the plot. Also, the recovery of natural vegetation along the analyzed time period provokes irregular distribution of the altitudes inside plot. This result confirms the initial hypothesis that the impact of animals on the vegetation growth could be quiantify using lasser scanner technologies.

keywords: *Recovery vegetation monitoring*, *Laser scanner*, *Digital Terrain Models*
Using a Portable XRF for Classifying Volcanic Paddy Soils of West Sumatra, Indonesia

Dian Fiantis – Andalas University, Indonesia

Gus Gusnidar – Andalas University, Indonesia

Budiman Minasny – Sydney University, Australia

This study investigates geochemical data obtained rapidly from a portable X Ray Fluorescent can be used to classify paddy soils derived from volcanic ash in Western Sumatera, Indonesia. Seventy-nine paddy topsoil samples were collected in along an altitudinal gradient of 44 – 1220 m a.s.l along areas if mountain (Mt) Marapi, Mt. Sago, Mt. Singgalang, Mt. Tandikek and Caldera Maninjau. Geochemical analysis was carried out using a portable x-ray fluorescent spectrometer (XRF).

Linear discriminant analysis was carried out using 10 geochemical concentration of soils as input (SiO2, Al2O3, Fe2O3, CaO, MgO, K2O, SO3, P2O5, Ti and Zr). The results showed that geochemical characteristics of topsoils derived from different volcanoes are distinct. Mahalanobis distance statistic clearly separated soils of Mt. Sago with other four soils. Soils from Mt. Marapi were also dissimilar with other three soils, while soils from Mt. Singgalang-Tandikek and Maninjau were more related. Clear differentiation among weathering indices was also observed, and the following sequence of the degree of weathering can be inferred: Sago > Maninjau > Marapi > Singgalang-Tandikek.

In conclusion, although the soils have been cultivated with paddy for hundreds of years, they still retain distinct geochemical signatures that can be revealed using a portable XRF. Portable XRF offers a rapid analysis and classification of volcanic soils.

keywords: Weathering indices, Discriminant analysis, X-ray fluorescent, Andisols.
# Quantifying the uncertainty in a model reconstruction of a soilscape for archaeological land evaluation

Peter Finke – Universiteit Gent, Belgium

Azam Jafari – Department of Soil Science, Shahid Bahonar University of Kerman, Iran

Ann Zwertvaegher – Universiteit Gent, Belgium

Olivier Thas – Université Gent, Belgium

Process-based soil development models can be applied as temporal interpolators to estimate soil properties at points in time. In geo-archaeological studies, the (pre-)historical state of soil and landscape is reconstructed to better understand the land occupation history and its spatial patterns. We reconstructed a Middle Bronze Age (MBA, 1331 BCE) soilscape for part of Flanders using a simulation model for groundwater flow (MODFLOW) in combination with a soil formation model (SoilGen). The reconstructed groundwater data related to 40x40 m2 cells at full spatial coverage while the reconstructed soil data pertain to the pedon scale at 97 locations. As these data were to be used for a land evaluation for a Bronze Age Land Utilization type, the reconstructed soil data must be interpolated to full extent.

At the simulation locations, values of soil and groundwater properties were known from the mapping (1953) so that simulation errors could be quantified. Under the assumption that model errors were similar in the MBA and 1953, we investigated how errors in the modelling and the spatial interpolation contributed to the uncertainty in the land evaluation. The effect of error of the soil and groundwater models was assessed by generating perturbations of soil and groundwater data using Choleski decomposition of the error-covariance matrix at the 97 locations. The effect of the interpolation of the soil data was assessed by Sequential Gaussian Simulation of the error variogram of regression kriging. The effect of both uncertainty contributions was quantified by inserting the perturbations or realisations in the land evaluation procedure. The error leads to a diversity value connected to the land evaluation outcome at the pixel level. Diversity maps resulting from the model error and the interpolation error were created and compared, and the soil properties that were the main contributors to uncertainty were identified. This approach gives useful information on priorities to increase reliability of such soilscape reconstruction by aiming at either a better model error (for specific properties) or a higher spatial density of point simulations.

**keywords:** soil development model, soilscape reconstruction, uncertainty analysis, archaeology
# Soil hydrological classification mapping in Scotland using DSM and Random Forests

Zisis Gagkas – The James Hutton Institute, United Kingdom

Allan Lilly – The James Hutton Institute, United Kingdom

The nature and spatial distribution of soils and their attributes have a profound influence on catchment hydrology and runoff characteristics. The Hydrology of Soil Types (HOST) classification is a classification scheme based on regional soil maps of the UK which uses expert knowledge to link soil hydraulic indicators (identified during soil surveys) with dominant runoff processes in the form of different conceptual models of subsurface runoff processes and pathways through the soil profile and/or parent material. The HOST decision scheme has been optimised by regressing base flow index (BFI) and standard percentage runoff (SPR) (both used to estimate catchment runoff response) to the proportions of various HOST classes occurring within the watersheds of selected test catchments. HOST class information in Scotland is available as dominant class (based on higher areal coverage) within each polygon of the National Digital Soil Map (1:250 000 scale).

The study presented here is part of an ongoing project which aims to explore the application of Digital Soil Mapping (DSM) techniques for mapping soil hydrological properties and regulatory functions. In particular, this study aims to investigate the performance of Random Forests (RFs) for the spatial prediction and disaggregation of HOST classes at a target 100m grid resolution. RFs were selected as the statistical inference engine due to their ability to utilise both discrete-valued and continuous valued covariates and their successful application in the spatial prediction and disaggregation of soil types. For comparison reasons, two study catchments are selected in NW and NE Scotland that have distinct differences in their soil type composition, topography and hydrological response characteristics. Training sampling points in the two catchments are determined using a random weighted-area spatial sampling scheme and the training dataset is developed using the HOST class values at each point location along with attributes derived from a collated database of environmental covariates (SCORPAN factors). Validation of the predicted HOST class spatial distribution within the study catchments is performed in two ways: a) comparison of predicted to calculated HOST class values at the profile locations of the National Soil Inventory of Scotland (NSIS) and b) comparison of catchment hydrological response based on the proportions of predicted and existing HOST classes. Study results are expected to help improve hydrologic response predictions at small ungauged catchments and provide new insight on the coupling of DSM and hydropedological datasets.

**keywords: HOST, hydropedology, DSM, Random Forests, spatial prediction**
GIS-based multivariate predictive models for gully erosion susceptibility mapping in calcareous soils

Younes Garosi - Soil Science Department, College of Agriculture, Bu-Ali Sina University, Azadegan Street, Hamedan, Iran and, Iran

Mohsen Sheklabadi - Soil Science Department, College of Agriculture, Bu-Ali Sina University, Azadegan Street, Hamedan, Iran, Iran

Christian Conocenti - Department of Earth and Sea Sciences (DISTEM), University of Palermo, Via Archirafi 22, 90123 Palermo, Italy, Italy

Kristof Van Oost - TECLIM, George Lemaitre Center for Earth and Climate, Earth and Life Institute, Université Catholique de Louvain, Louvain-La-Neuve, Belgium and Fonds de la recherche Scientifique, FNRS Rue d'Egmont 5, 1000 Brussels, Belgium, Belgium

Parviz Shekaari - Soil Science and Engineering Department, Faculty of Agriculture, Razi University, Kermanshah, Iran., Iran

Leila Meimivand - Soil Science Department, College of Agriculture, Bu-Ali Sina University, Azadegan Street, Hamedan, Iran, Iran

Gully erosion susceptibility mapping (GESM) is essential to take preventive or control measures, reduce environmental damages and economical costs produced by gully erosion in arid and semi-arid area. The purpose of this study is to produce GESM using different data mining models in calcareous soils at Ekbatan Dam drainage basin. Three modeling technique, Generalized Linear Model (GLM), Random Forest (RF) and Multivariate Adaptive Regression Spline (MARS) are used and their results are compared for GESM. The study area is an 18.5 km² located at Ekbatan Dam drainage basin (Hamadan-Iran), where agriculture activities are limited by intense erosion. By means of extensive field surveys, GPS route of gullies and visual interpretation of satellite images available from Google Earth, we prepared a digital map of the spatial distribution of gullies in the study area. To prepare explanatory variables that affected soil erosion, using the conditioned latin hypercube sampling (cLHS) technique and free survey 130 locations were sampled to create spatial distribution of some soil properties. DEM-derivatives, land use and normalized difference vegetation index (NDVI) maps created by stereo imagery of Spot-6 satellite. Geology map digitized from 1:100,000 scale geological scanned map of Hamadan Province. Road and river networks, derived from 1:25,000 scale topographic maps. The functional relationship between gully erosion occurrence and controlling factor were calculated using the mentioned models (GLM, RF, and MARS). The performance of each models was evaluated using the receiver operating characteristic (ROC), including the area under the curve (AUC), in 10-fold cross-validation. According to the results, RF showed the better performance (with mean AUC≤94.92%) in compared with MARS (with mean AUC≤87.31 %) and GLM (with mean AUC≤82.88%) for calculate the probability to host a gully for each point of study area. Actually these results show that the GLM, RF, and MARS models produced reasonable accuracy in gully erosion susceptibility mapping. The outcome maps can be a useful tool for regional planners, because they assist in identifying areas at risk of gully erosion and improve our understanding of landscape evolution.

keywords: Gully erosion susceptibility, Data mining models, ROC curve, Iran
The I4S approach to site-specific soil fertility management based
on proximal soil sensing

Robin Gebbers – Leibniz-Institute for Agricultural Engineering and Bioeconomy, Germany

Volker Dworak – Leibniz-Institute for Agricultural Engineering and Bioeconomy, Germany

Benjamin Mahns – Leibniz-Institute for Agricultural Engineering and Bioeconomy, Germany

Marcel Oertel – Leibniz-Institute for Agricultural Engineering and Bioeconomy, Germany

Cornelia Weltzien – Leibniz-Institute for Agricultural Engineering and Bioeconomy, Germany

Dominique Büchele – Federal Institute for Materials Research and Testing, Germany

Igor Gornushkin – Federal Institute for Materials Research and Testing, Germany

Michael Maiwald – Federal Institute for Materials Research and Testing, Germany

Markus Ostermann – Federal Institute for Materials Research and Testing, Germany

Madlen Rühlmann – Federal Institute for Materials Research and Testing, Germany

Thomas Schmid – Federal Institute for Materials Research and Testing, Germany

Bernd Sumpf – Ferdinand-Braun-Institute Leibniz-Institute, Germany

Martin Maiwald – Ferdinand-Braun-Institute Leibniz-Institute, Germany

Lara Hoppe – Ferdinand-Braun-Institute Leibniz-Institute, Germany

Jörg Rühlmann – Geophilus Inc., Germany

Mohamed Bourouah – Hahn-Schickart Society for Applied Research, Germany

Herrmann Scheithauer – Hahn-Schickart Society for Applied Research, Germany

Kurt Heil – Technical University of Munich, Germany

Tobias Heggemann – University of Bonn, Germany

Matthias Leenen – University of Bonn, Germany

Stefan Pätzold – University of Bonn, Germany

Gerhard Welp – University of Bonn, Germany

Thomas Chudy – Martin Luther University Halle-Wittenberg, Germany

Alexander Mizgirev – Martin Luther University Halle-Wittenberg, Germany

Peter Wagner – Martin Luther University Halle-Wittenberg, Germany

Thoralf Beitz – University of Potsdam, Germany

Michael Kumke – University of Potsdam, Germany

Daniel Riebe – University of Potsdam, Germany

Christian Kersebaum – Leibniz Centre for Agricultural Landscape Research, Germany

Evelyn Wallor – Leibniz Centre for Agricultural Landscape Research, Germany

In precision agriculture the lack of affordable methods for mapping relevant soil attributes is a fundamental problem. It restricts the development and application of advanced models and algorithms for decision making. The project "I4S - Integrated System for Site-Specific Soil Fertility Management" combines new sensing technologies with dynamic soil-crop models and decision support systems. On the one hand, model based knowledge about processes in soil and crops should dictate the selection of sensors for assessing soil fertility factors. However, quite often a factor cannot be measured directly by a single sensor. Using sensors with different measurement principles improves the estimation of soil fertility factors such as plant available K, P, Mg, humus and soil texture. On the other hand, soil sensors can detect properties that were not regarded in soil-crop models as inputs up to now. This may even lead to switching from mechanistic "white-box" modeling to machine learning "black-box" modeling. To become effective in soil management, sensor system and models have to be user friendly and affordable. Involving many sensors can cause technical difficulties due to interference and is expensive. Some sensor systems may emit harmful radiation such as x-rays or high power laser pulses. Choosing the appropriate set of sensors will become an important aspect of decision making in precision agriculture. So, sensor based
site-specific soil fertility management is only feasible if a decision support system regards natural processes, technical, and socio-economic options and limitations.

**keywords:** Proximal soil sensing, fusion of soil sensors, soil-crop models, decision support systems, site-specific soil management
Effects of Measurement Protocols and Data Mining Techniques
on Soil Proxy Model Extraction: A Czech Case Study

Asa Gholizadeh - Czech University of Life Science, Czech Republic

Nimrod Carmon - Tel-Aviv University, Israel

Ales Klement - Czech University of Life Sciences Prague, Czech Republic

Eyal Ben-Dor - Tel-Aviv University, Israel

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

Soil spectroscopy has shown to be a fast, cost-effective, environmental-friendly, nondestructive, reproducible, and repeatable analytical technique. However, not only the soil components but also types of instruments, protocols, sampling methods, sample preparation, spectral acquisition techniques, and analytical algorithms have an influence on the spectral performance. Therefore it's important to characterize these differences and to establish a simple common standardization procedure in order to minimize the technical factors that alter reflectance spectra which will enable all users to create robust spectral models over large geographical extents. To quantify this alteration and propose a standardization method, a joint project between Czech University of Life Sciences (CULS) and Tel-Aviv University (TAU) was conducted to estimate Cox, pH-H2O, pH-KCl, Fe-d, Fe-ox, Mn-d, and Mn-ox. 78 soil samples from five agricultural lands from different parts of the Czech Republic and two different protocols for soil spectral and chemical measurement and two data mining techniques were analyzed. Spectral measurement at both laboratories was done using ASD spectroradiometer. CULS protocol was based on employing Dark Box (DB) environment, Partial Least Square Regression (PLSR) model and k-fold cross validation, while TAU protocol was carried out using a Contact Probe (CP) and Internal Soil Standard (ISS) procedure. At TAU, spectral modelling was performed using the PARACUDA II, a new machine learning and data mining engine which automatically develops 512 individual models for each soil property and evaluates the model population behavior and the best model selection under the internal calibration-validation 75%-25% technique. The results demonstrated that spectra based on the CULS setup demonstrated significantly higher albedo intensity and reflectance values relative to the TAU setup reflectance. The TAU protocol also improved the analytical results as compare to the CULS (NON-ISS) protocol; however, keeping the spectral measurement stages constant by a strict setup reduces the effectiveness of ISS significantly. The paper also highlights that PARACUDA II engine proved to be a powerful tool to assess higher results than the regular scheme such as PLSR that is traditionally used by many users. Such initiative is not only a way to unlock current limitations of soil spectroscopy, but also helps the soil society for development of an agreed-upon protocol to contribute to the construction of the Soil Spectral Libraries (SSLs).

keywords: Soil spectroscopy, Protocol and standard, Data mining, Internal soil standard, Soil spectral library
Soil organic carbon stocks prediction in Brazil

Lucas C. Gomes – Federal University of Viçosa, Brazil

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

Eliana de Souza – Federal University of Viçosa, Brazil

Raiza M. Faria – Federal University of Viçosa, Brazil

Gustavo V. Veloso – Federal University of Viçosa, Brazil

Carlos E.G.R. Schaefer – UFV, Brazil

This study evaluated an approach for predicting soil organic carbon (SOC) stocks in the Brazilian territory down to 1m depth for all biomes and soil types using environmental and bioclimatic variables. To obtain the highest performance of prediction we tested five machine-learning algorithms: Cubist, Random Forest, Linear Regression, Support Vector Machine, and ExtraTrees. Soil data were obtained from a compilation made by the Brazilian Agricultural Research Agency (Embrapa), based mainly in the RADAMBRASIL country survey. The dataset totalized 8,530 soil profiles with 37,939 samples, in which the main chemical and physical properties are available. From the dataset, 3,278 samples were used to adjust a pedotransfer function for estimating bulk density for missing depths, based on SOC and clay contents, using the linear regression method. To model the vertical distribution of SOC stocks we used mass preserving equal-area quadratic splines to interpolate the distribution of SOC and bulk density at standard depth intervals (0–5, 5–15, 15–30, 30–60, and 60–100 cm). The covariates database was composed of 78 variables including bioclimatic (temperature and precipitation), soil and biome maps, vegetation indexes and morphometric maps derived from a digital elevation model, with a grid of 1 km². To select the most important covariates for each depth we used the Recursive Elimination Feature of the Random Forest, in which the main variables selected were: precipitation, temperature, slope height, soil class, standardized altitude, terrain surface convexity and vegetation indexes (NDVI, EVI). Predictions of all machine-learning algorithms were compared using nested ten-fold cross-validation and independent validation. Independent validation was performed separating the dataset into 10 sets with 90% for training and 10% for validation. Accuracy assessments of predictions were compared by means of statistical indexes: root mean squared error (RMSE) and R-squared (R2). Overall, Random Forest showed the best performance in predicting SOC stocks for all depths, with a highest performance for 15-30 cm soil depth (R2 ≤ 0.30, RMSE ≤ 0.94 kg m⁻²) and was selected for the spatial prediction of SOC stocks. In total, the soils store approximately 87 PgC within the top 1 m and the 0-30 cm layer reaches 48 PgC. Also, 30% of the total carbon stock is present in protected areas (conservation units). The Amazonia biome possesses the highest amount of carbon (44 PgC). Regarding SOC stock among soil classes, more than 53% of the total SOC stock was found in the Oxisols and Ultisols. This study is the first in predicting SOC stocks for the whole of Brazil’s territory using machine-learning algorithms and could be used for future soil carbon inventories and to highlight the importance of nature reserves for protecting soil organic carbon.

keywords: Carbon stock, Machine-learning, Organic carbon, Soil mapping
Variation of soil property depth functions

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

Alfred Hartemink – University of Wisconsin-Madison, USA

Soil property depth functions can be used to identify soil processes that involve translocation and transformation as well as to locate pedological and lithological discontinuities. Within a given soil, every soil property has its own vertical distribution pattern that can be expressed as a depth function. We constructed depth functions using 10 cm interval sampling in 3 soils: a Mollisol, an Alfisol, and an Entisol. We used soil property data to estimate soil property depth function types. We found that the depth function type of a soil property can vary considerably between soils. The Ca concentration depth function types were irregular (Mollisol), disjoint (Alfisol), and exponential (Entisol). We found that, within a soil, the depth function type varies between soil properties. In the Alfisol, the depth function types were exponential for soil organic carbon (SOC) concentration, minimum-maximum for pH, disjoint for Ca and Ti concentration, maximum-minimum for Al concentration and peak for Fe concentration. Some soil property depth functions reflected changes in soil horizon or parent material. The depth functions of Ca concentration in the Alfisol and SOC concentration in the Mollisol matched horizon boundaries. The Ti concentration depth function in the Alfisol reflected the change in parent material. Other depth functions (i.e. Al and Fe concentration) showed continuous changes with depth that did not overlap with horizons boundaries. It is expected that fine resolution sampling of soil profiles will result in an increased use of soil depth functions as a pedological research tool.

keywords: digital soil morphometrics, pedology, soil depth function
Raster sampling of three soil profiles from Wisconsin, USA

Jenna Grauer Gray – University of Wisconsin - Madison, Department of Soil Science, USA

Alfred Hartemink – University of Wisconsin - Madison, Department of Soil Science, USA

Three soil profiles in Wisconsin, USA were sampled using a 10x10 cm raster: a Mollisol (1x1 m), an Alfisol (1x1 m), and an Entisol (1x0.5 m). The profiles were described in the field, and samples were taken from each cell and analyzed for soil color (HSV cylindrical-coordinate representation), soil organic carbon (SOC) concentration, and soil texture. SOC, texture, and color were used to revise delineation of horizons and their boundaries. Raster data verified the boundary of the Mollisol A2/Ab, the Alfisol Bt/2Bw, and the Entisol Ap/Bw1 horizons. Using clay content, an E horizon was identified in the upper part of the field-delineated Bt horizon of the Mollisol. Clay content, color and SOC concentration were also used for the Ap/Bt horizon boundary in the Alfisol. Raster data also showed considerable variation of soil properties within horizons. It is concluded that raster data can detect horizons that were not identified in the field, and can revise or verify field-delineated horizon boundaries.

**keywords:** soil carbon, digital soil morphometrics, soil profiles, pedology, Mollisol, Alfisol, Entisol, soil color, soil texture, soil horizons
High resolution modelling of soil organic carbon in West Green-
land

Philipp Gries - Eberhard Karls University Tübingen, Germany

Peter Kühn – Eberhard Karls University Tübingen, Germany

Thomas Scholten – Eberhard Karls University Tübingen, Germany

Karsten Schmidt – Eberhard Karls University Tübingen, Germany

The soil organic carbon (SOC) pool in the first 300 cm of arctic soils includes about 50 % of the estimated global terrestrial belowground organic carbon, which makes about 1024 Pg C and up to 496 Pg within the upper permafrost one meter. The Arctic is sensitive to climate change. Hence, thawing of permafrost-affected soils to greater depth and for longer periods increases the release of CO2 and CH4 to the atmosphere, which queries soils as an important carbon pool. Especially in arctic environments, sparse soil data and limited knowledge of soil processes cause underestimation of SOC stocks. Due to different regional climatic conditions, changing soil-environmental conditions result in varying soil organic carbon contents in Greenland. In West Greenland, coastal oceanic conditions turn into continental climate at the ice margin showing less precipitation, higher insolation and increasing permafrost thickness.

This study comprises a prediction approach of SOC at two different study areas in West Green-
land using digital soil mapping (DSM) and data mining (DM) techniques (e.g. Random Forest,
RF). The objectives are (i) to identify major environmental factors controlling spatial variation of
SOC, (ii) to create high-resolution maps of SOC (iii) and to estimate SOC stocks.

Respecting different climatic conditions, one study area is situated next to the ice margin in the Kangerlussuaq area and the second one is located near Sisimiut at the coast. Both study areas (2 km²) are representative for each region and have similar environmental settings. Soil samples (SOC, bulk density) were taken from depth increments (0-25, 25-50, 50-100, and 100-200 cm) at 40 sampling locations in each study area. Input data of the DSM model includes soil data from fieldwork, a set of terrain attributes and remote sensing data.

First results comprise area-wide calculation of SOC stocks and dominant covariates for both
study areas using RF (R²/RMSE). Within the Kangerlussuaq study area, the average SOC stock
of the upper 25 cm is 7.56 kg/m². There, the most important environmental variables of the
RF model (0.62, 1.22) are aspect and soil moisture. At Sisimiut, using RF (0.65, 6.24) results
in an average SOC stock of 12.12 kg/m² of the upper 25 cm and identifies elevation, aspect and
vegetation as most important environmental variables. We assume for the Sisimiut area that south
facing areas have high SOC stocks due to higher biomass production because of higher insolation.

keywords: digital soil mapping, carbon stocks, machine learning
# Past, present and future of physical, chemical and biological process knowledge in pedometrics

Sabine Grunwald – University of Florida, USA

‘Pedo’ and ‘metrics’ emphasize two perspectives, where the former highlights pedological knowl-
edge and the latter the methodology and computational tools used to understand the past, current,
future conditions and change of soils. This understanding of pedology and physico-, biological- and
chemical transformations has been transposed into sophisticated maps and models. The main focus
in this talk is on the “Why” and “How” soils develop, degrade, improve or are sustained in terms
of their performance to meet needs (e.g., crop yield in context of food security), functions (e.g., soil
carbon sequestration), values and benefits (e.g., water holding capacity), conditions (e.g., various
soil properties) or overall perceptions (soil health, quality, and security). Since the invocation of pe-
dometrics we have seen shifts in how physical, chemical, and biological process knowledge has been
incorporated in soil models. An increased understanding of relationships between environmental
factors and soil properties as well as biogeochemical cycles derived from empirical measurements
(e.g. vadose zone) have afforded to improve our body of knowledge. Trends to couple these soil
models explicitly with the human and life dimensions are underway to move from simpler model
representations (e.g., incorporation of land use) to models that explicitly account for human ac-
tions, values, beliefs, decisions, and other organisms (e.g., microbial processes) allowing improved
back- and forecasting (e.g., global climate change) and evaluation of scenarios (e.g., adaptation in
soil management). Improved soil-environmental data availability through remote and proximal
sensing technologies combined with advancements in computational ability have afforded to build
ever better soil factorial models rooted in empirical science. Specialized process-based soil models
to simulate soil change (quasi)mechanistically have been used in standalone mode or embedded in
holistic Earth System Simulation Models and ecosystem models. Trends and issues related to how
we transpose what we know about soils at a point (site) to spatial and temporal model scales and
associated uncertainties will be addressed. I will also discuss the mainstream paradigms in pedo-
metrics rooted in empiricism and reductionism (i.e., deduct data and process-knowledge at finer
and finer scales and build more detailed and complex models) in light of the needs to synthesize
soil knowledge to resolve the global grand challenges and crises of our time.
Organic carbon in Swiss cropland soils 1985-2014

Andreas Gubler - Swiss Soil Monitoring Network NABO, Switzerland

Peter Schwab - Swiss Soil Monitoring Network NABO, Switzerland

Daniel Wächter - Swiss Soil Monitoring Network NABO, Switzerland

Felix Stumpf - Swiss Soil Monitoring Network NABO, Switzerland

Armin Keller - Swiss Soil Monitoring Network NABO, Switzerland

Soil organic carbon (SOC) is a key factor regarding most soil functions. However, consistent long-term data on SOC contents at national level are scarce. The Swiss Soil Monitoring Network NABO assessed SOC contents of 31 cropland sites sampled six times from 1985 to 2014. The results provide insights into the temporal evolution of SOC in Swiss croplands in the context of the agricultural management. Furthermore, the sensitivity of the used monitoring scheme may be assessed.

keywords: soil monitoring, soil organic carbon, minimum detectable change
# Seeing inside a pedologists head: are machine learning algorithms landscape specific?

Jacqueline Hannam - Cranfield University, United Kingdom

Joanna Zawadzka - Cranfield University, United Kingdom

Ronald Corstanje - Cranfield University, United Kingdom

Thomas Mayr - Cranfield University, United Kingdom

In digital soil mapping (DSM) often only one inference method is applied or a couple are tried and the best performing is chosen based on the model validation statistics. Whilst this is a valid approach from a pedometric point of view it does not tell us if there are any underlying pedological reasons why x method was better than y. This kind of information is useful when operating at the landscape scale when strategic or policy decisions are commonly made within these contexts.

We compare a DSM taxonomic soil map produced at a national scale by Random Forests and a Bayesian network. The final soil associations were selected based on the best performing model after validation using independent field data. It is evident that some areas favour one method over the other and in other areas there is little differentiation between the two models in the landscape. Where there is a fragmented pattern in the optimal model selection this is often due to the efficacy of the training data. In landscapes where one model is obviously more successful over a wider area we discuss why this may be the case. In these areas the way the inference system makes a decision (ensemble decision trees vs conditional probabilities) and the pattern of soils within the landscape may be compatible. It could potentially identify areas where the soil patterns are more stochastic or where there are feedbacks between some of the covariates. This additional information is useful when using the soil map for strategic purposes for example for identifying areas for monitoring soil conditions or to predict a likely outcome to a perturbation such as climate change or land management.

**keywords:** digital soil mapping, soil landscapes, soil monitoring
# The Pedon is at the Core of Digital Soil Morphometrics

Alfred Hartemink – University of Wisconsin, USA

The pedon is at the core of many soil investigations whether that is for digital soil mapping purposes, site characterization, or pedological research. In this talk an overview is provided how the pedon, and its two-dimensional representation (the soil profile), is studied. An overview is presented of the measurement of soil properties and morphometric characteristics, soil depth functions, and soil profile imaging and mapping. The overall purpose is enhanced understanding including more objective ways of identifying and delineating soil horizons and treating the soil as a continuum with depth. Digital soil morphometrics can be viewed as the use of proximal soil sensors and other instruments to study soil profiles. There has been considerable progress in the in-situ measurement of soil properties and soil functions linking a range of instruments with soil inference systems. Some progress has been made in the mapping of the soil profile that includes raster sampling of a soil profile wall, digital image analysis of soil profiles, and monolith scanning. This has resulted in improved soil horizon delineation and assessment of soil horizon purity, and the realization. At last, there may be a recurrent pattern in soil feature at different levels that can be studied using similar pedometrics approaches.
Validation of the 250m Soil Grids in Canada

Juanxia He – Government of Canada, Canada

Xiaoyuan Geng – Government of Canada, Canada

Bert Vandenbygaart – Government of Canada, Canada

Tim Martin – Government of Canada, Canada

Tomislav Hengl – ISRIC, Netherlands

Robert MacMillian – LandMapper Environmental Solutions Inc., Canada

Cindy Shaw – Government of Canada, Canada

The latest version of SoilGrids at 250m resolution has been released. Regional assessment and verification of the released data are still needed in order to guide the use and future improvement of the data. Two sets of independent point soil data were selected to validate the predicted soil total organic carbon (TOC) of the 250m SoilGrids. The first set of point data was acquired from the Canadian Forest Service (CFS) for the non-agriculture areas in Canada. The second set was retrieved from the Canadian National Pedon Database(NPDB).

The LFH and organic horizons information is widely recorded from the pedons from non-
agricultural areas. In contrast, the recorded top horizons of the pedons from agriculture regions
generally of mineral provenance. The two groups of the pedon data were used separately in the
validation method. All of the validation pedon points were preprocessed and modeled to uniform
depths. The zero depth of each pedon was adjusted to the terrain surface, which meant no negative
depth values Soil TOC values at the five standard depths (5, 15, 30, 60, 100 cm) were retained
using the SPLINE tool (Malone et al. 2009). The TOC values of the 250m SoilGrids at similar
depth intervals were extracted at each of the validation points.

With the independent measured and the predicted SoilGrid soil TOC data, the validation in-
cluded examination of scatter plots, calculation of RMSE (Root-Mean-Square-Error), bias (mean
error of prediction), and R2 (the coefficient of determination). Log-transformations were applied
where normality of the distribution was not attained for calculating the R2. The R2 values and
the scatter plots suggested that the SoilGrid predicted TOC in the non-agricultural areas better
than that in the cultivated regions. However, the linear relationship between the measured and
predicted data was weak to moderate, and the R2 values ranged from 0.01 to 0.55. The RMSE
and bias of the TOC values for both the non-agriculture and agriculture land were large espe-
cially at the 5 cm depth. However the RMSE and bias were reduced as the depth increased. In
summary, the TOC prediction of the SoilGrids is better in non-agriculture areas than that in cul-
tivated regions. For the future revision of the 250m SoilGrid, more training point data should be
used from Canada. Organic and Peatland soils should be modeled separately from mineral soils.
Environmental covariates that are unique to Canadian soil development may contribute to the
improvement of future predictions.

keywords: Soil, Soil Carbon, DSM, Canada
# How universally is soil carbon increasing in New Zealand's hill country?

Carolyn Hedley - Landcare Research, New Zealand

Pierre Roudier - Landcare Research, New Zealand

Hill country represents a significant part of New Zealand's agricultural land. These landscapes are characterized by steep slopes, and are prone to erosion. Previous studies indicate the rate of soil carbon accumulation in hill country is highly variable, and processes affecting stock changes differ for contrasting topographic units. In the recent years, resampling of 23 National Soil Database sites at mid-slope positions provided strong evidence of soil carbon increase for these specific sampling sites since the 1980s. However, there is little data available for other topographic units (such as swales, summits, gullies): the spatial pattern of these variations of soil carbon is largely unknown.

This poster will introduce a new project that will establish a soil monitoring framework for New Zealand hill country, leveraging the wealth of environmental data-layers now available. This will guide the selection of new National Soil Database sites for resampling in places previously under-sampled to reduce uncertainty in estimates of changes in soil carbon stock at different slope positions and for different soil orders. The framework will provide a statistically robust method for monitoring ongoing soil carbon changes for New Zealand hill country, and for National Carbon Accounting and Emission Trading Scheme systems.

**keywords: Soil monitoring, Soil carbon, Spatio-temporal variability**
Soil texture estimation via mobile gamma-spectrometry: advanced
evaluation using support vector machines

Tobias Heggemann – University of Bonn, INRES, Soil Science and Soil Ecology, Germany

Gerhard Welp – University of Bonn, INRES, Soil Science and Soil Ecology, Germany

Sylvia Koszinski – Leibniz Centre for Agricultural Landscape Research (Leibniz-ZALF e.V.), Germany

Matthias Leenen – University of Bonn, INRES, Soil Science and Soil Ecology, Germany

Karsten Schmid – University of Tübingen, Department of Geosciences, Char Soil Science and
Geomorphology, Germany

Stefan Pätzold – University of Bonn, INRES, Soil Science and Soil Ecology, Germany

High resolution soil data are an essential prerequisite for the application of precision farming techniques. Sensor-based evaluation of soil properties may replace laborious, time-consuming and expensive soil sampling with subsequent measurements in the lab. Gamma spectrometry generally yields information that can be translated into topsoil texture data after adequate calibration. Yet, preliminary studies show that transferability of local or regional calibration models is restricted because of widely differing mineralogical soil composition and pedogenesis. However, nonlinear models using support vector machines shall be developed in order to be extrapolated to a wide range of different landscape settings. Applying such models would make sampling, analyses and local calibration dispensable. Our recently published results in this regard (Heggemann et al. 2017, Soil Till. Res.) refer to spectra that were recorded directly in the field using a tractor-based spectrometer, but in a stationary stop-and-go mode. In an ongoing project ("I4S - Intelligence for Soil") in the frame of the German Federal BonaRes program we actually evaluate the preciseness and usability of spectra and universal models recorded on-the-go, i.e. from a driving tractor. Predicting sand, silt and clay contents while driving over the field would enable us to directly adapt texture-related agronomic measures such as lime and fertilizer application "on-the-go".

keywords: proximal soil sensing, online sensor, machine learning
# The power of Random Forest for the identification and quantification of technogenic substrates in urban soils on the basis of DRIFT spectra

Jannis Heil – University of Wuppertal, Germany

Xandra Michaelis – Ruhr University Bochum, Germany

Bernd Marschner – Ruhr University Bochum, Germany

Britta Stumpe – University of Wuppertal, Germany

In the past, large amounts of domestic waste and unwanted by-products from industrial processes, such as ashes from municipal solid waste incineration and slags from metallurgy processes were produced and often were deposited unregulated in the urban environment. The incineration of municipal solid wastes has been increasingly adopted around the world to deal with the ever-growing amounts of waste. Slags, on the other hand, are generated as solid by-products during metal production, with annual worldwide production reaching over 50 million tons. Some of these technogenic substrates contain high concentrations of hazardous substances, such as salts, PAHs, or heavy metals, so that an identification of such substrates is necessary.

As traditional methods are slow and costly, we developed a spectroscopic based data mining method to identify different technogenic substrates in soils. Out of 174 soil samples, 13 different samples were chosen, finely ground (<200 µm), and mixed with different finely ground incineration plant ashes and zinc furnace slags at different levels of concentration from 5 – 50%. The spectra of pure substrates and mixtures were determined using diffuse reflectance Fourier transform spectroscopy (DRIFT) in the mid-infrared region (MIR, 4000–400 cm⁻¹) at a resolution of 2 cm⁻¹. The DRIFT spectra were used to develop classification models using the random forest algorithm (RF), one of the latest improvements in ensemble learning. Prior to analysis, different mathematical pretreatments were performed to improve the model performance.

RF was able to separate the pure substrates perfectly, without any pretreatment. Soil/substrate mixtures could be classified at a low uncertainty for technogenic substrate concentrations 10%. Forming the first derivative of the spectra reduced the model error by the factor of two to 4 – 7%. For the detected substrates, RF was also able to determine the level of concentration (low, medium, high), albeit with higher error rates. We showed that RF can be a powerful classification system to identify potential pollutants in soils. With our approach, we present a rapid and cost-effective method to identify and determine technogenic substrates in soils, an approach which could be adapted to a broad range of pollutants, also to other media than soil.

**keywords:** Urban soils, Technogenic substrates, Diffuse Reflectance Spectroscopy, Data Mining, Random Forest
# Mapping the patterns of organic matter decomposition in a high mountain environment

Niels Hellwig – Institute of Geography, University of Osnabrück, Germany

Kerstin Anschlag – Institute of Geography, University of Osnabrück, Germany

Gabriele Broll – Institute of Geography, University of Osnabrück, Germany

High mountain environments are characterized by a high heterogeneity of landscape attributes. Moreover, soil sampling in these areas faces limited terrain accessibility. Spatial modeling of soil properties thus requires techniques that cope with both scarce data and small-scale variations of environmental factors.

In the context of the D.A.CH. project "Effect of climate on coarse woody debris decay dynamics and incorporation into the soils of forested Alpine areas" (DecAlp) soil ecological relationships and processes were investigated in an area in the Northern Italian Alps (Val di Sole / Val di Rabbi, Trentino). Data on humus forms serve as valuable information about the decomposition of organic matter. The aim of this study was to map spatial patterns of decomposition processes inside the forested parts of the study area by implementing models from the local to the landscape scale.

Spatial modeling was based on humus form data and included topography and vegetation as relevant covariates. Different approaches were used according to the model scale: At the local scale, humus forms were associated with the ground cover at the forest floor and the microtopography in a rule-based approach. At the slope scale, random forests were utilized to spatialize patterns of decomposition processes, depending on terrain attributes (from a digital terrain model), forest characteristics (from a vegetation map) and quantitative field estimates of the forest ground cover (e.g. no vegetation, moss, grass). At the landscape scale, modeling was based on decision tree analysis and fuzzy logic. The model results were assessed in terms of the goodness of fit, the validity of the model structure, the uncertainty of the humus form data and the applicability regarding varying landform types.

The model predictions reveal a high spatial variability of decomposition processes. At the local scale, variations of humus forms coincide with small-scale variations of the forest ground cover (e.g. no vegetation, moss, grass). At the slope and landscape scale, forest humus forms show a trend according to factors such as elevation and slope exposure. At high elevations and north-exposed slopes, environmental conditions for the decomposition of dead organic matter are generally less favorable in comparison to low elevations and south-exposed slopes. The model assessment highlights the suitability of different model approaches depending on the scale, but also reveals uncertainties in some places of the study area due to the scarcity of humus form data.

**keywords:** Upscaling, Tree-based modeling, Fuzzy logic, Humus forms, Italian Alps
# End of kriging? Or how tree-based Machine Learning Algorithms can be used to generate more accurate spatial predictions with combined geographical and feature space covariates

Tomislav Hengl – ISRIC, Netherlands

Kriging (the best linear unbiased prediction for spatial data) has been for over 40 years among the most used technique for spatial prediction of soil variables. This talk will demonstrate, using real-life data sets, that Machine Learning Algorithms (MLA) that utilize buffer distances between sampling points as covariates can be used to produce equally good, frequently more accurate predictions than kriging; if auxiliary or feature space covariates (e.g. RS images) are added to the model then MLAs generate more accurate predictions than regression-kriging. The advantages of MLAs (the focus of this work is on tree-based MLAs such as Random Forest, Gradient Boosting, Cubist and/or Bayesian Additive Regression Trees) vs linear geostatistical models will be demonstrated using several standard soil mapping data sets (Ebergotzen, Meuse, Edgeroi). It will be further shown that advantages of MLAs over kriging methods are especially distinct in cases where relationship between soil variables and geographical and feature space covariates is non-linear and complex, and when models are based on millions of measurements. In addition, it will be shown that MLAs show also numerous other advantages over kriging due to increasing possibilities for interpretation of modelling results (sorting of the predictor variables based on importance, visualization of complex non-linear relationships, detection of possible outliers and blunders in the input data). These discoveries imply that, in principle, MLAs could be used to partially or completely replace kriging in soil mapping. There are still many challenges, however, in front of MLA users: how to improve computational efficiency? how to derive spatial uncertainty for MLA-based predictions? how to generate spatial simulations? how to include variable uncertainty in soil measurements in model fitting? and similar.

**keywords:** machine learning, random forest, regression-kriging, soil predictive modelling, automation, big data
Hyperspectral Imaging of Soil Cores

Eleanor Hobley – Technical University of Munich, Germany

Markus Steffens – Technical University of Munich, Germany

Stefanie Kriegs – Technical University of Munich, Germany

Obtaining reliable and accurate data regarding the spatial distribution of different soil com-
ponents is difficult due to issues related with sampling scale and resolution on the one hand and
laboratory analysis on the other. When investigating the chemical composition of soil, studies
frequently limit themselves to two dimensional characterisations, e.g. spatial variability near the
surface or depth distribution down the profile, but rarely combine both approaches due to limi-
tations to sampling and analytical capacities. Furthermore, when assessing depth distributions,
samples are taken according to horizon or depth increments, resulting in a mixed sample across the
sampling depth. Whilst this facilitates mean content estimation per depth increment and therefore
reduces analytical costs, the sample information content with regards to heterogeneity within the
profile is lost.

Hyperspectral imaging can overcome these sampling limitations, yielding high resolution spec-
tral data of down the soil profile, greatly enhancing the information content of the samples. This
can then be used to augment horizontal spatial characterisation of a site, yielding three dimensional
information into the distribution of spectral characteristics across a site and down the profile.

Soil spectral characteristics are associated with specific chemical components of soil, such as
soil organic matter or iron contents. By correlating the content of these soil components with their
spectral response, high resolution multi-dimensional analysis of soil chemical composition can be
obtained. Here we present a hyperspectral approach to the characterisation of soil organic matter
down cores obtained from agricultural soil profiles in Germany, outlining advantages and issues
associated with the methodology.

keywords: Vis-NIR, Deep soil, Multivariate analyses
High resolution estimation of peat depth using electromagnetic
induction in a Scottish peatland

Jonathan Holland - James Hutton Institute, United Kingdom

Laura Poggio - James Hutton Institute, United Kingdom

Richard Hewison - James Hutton Institute, United Kingdom

Jingyi Huang - UNSW, Australia

James Taylor - Newcastle University, United Kingdom

John Triantafilis - UNSW, Australia

There is widespread recognition of the significant value of peatlands and their ability to store large amounts of soil organic carbon. Recent surveys have been undertaken to assess the condition of peatlands and this includes calculating the amount of carbon stored. An important factor in this calculation is the accurate determination of peat depth, i.e. the depth of highly organic material which is above mineral soil or parent material. Several different methods are used to measure peat depth in the field. For instance, there have been some attempts to use ground penetrating radar. Manual measurement using augers are still commonly used, but this is time-consuming and only provides a depth reading at a single point. Some previous studies have reported on the use of electromagnetic induction (EMI) to non-invasively estimate the depth of peat. These studies have been limited in scope and there have not been any published EMI studies on peatlands in the UK. In this study, a DualEM 21S was used to record the electromagnetic signal along a transect of the Moidach More peatland in north-east Scotland, UK. The Moidach More has been the site of numerous intensive studies since the 1950s and thus, there is a large amount of legacy peat depth data. The peat depth varied significantly along the transect and the maximum depth surveyed was 3 m. The EMI transect was undertaken in the summer (1 September) when the antecedent moisture levels were moderate. The EMI data were processed and inverted using EM4Soil software. The inverted EMI data were modelled to see if the depth of the peat could be predicted. A Generalized Additive Model was used to capture non-linear relationships. The initial model was between peat depth and the mean of the conductivity data in a moving window of 10m around the measured depth locations (~20 points). The initial results showed a 38% of explained deviance and an R2 of 0.4. Further modelling will include environmental covariates, such as morphological features (elevation, slope) and remote sensing indices (vegetation, moisture). Given the small number of peat depth measurements available particular attention will be paid to model selection to avoid overfitting. These results show some promising potential for EMI data to be used for peat depth surveying. It is proposed that future research is undertaken on a larger dataset (>100) which should reduce uncertainty and provide a more powerful predictive relationship.

keywords: peat depth, soil organic matter, EM inversion, EM4Soil, Generalized Additive Model
3-D geostatistical interpolation of soil organic matter in the Nether-
lands

Tom Hoogland - Wageningen University and Research centre, Netherlands

Dick Brus - Wageningen University and Research centre, Netherlands

Dennis Walvoort - Wageningen University and Research centre, Netherlands

Detailed and accurate information on soil organic matter (SOM) content is important for many applications, like the assessment of greenhouse gas emissions, carbon sequestration and the fixation and leaching of pesticides. In the Netherlands the fate of pesticides in the soil-plant system is modelled with GeoPEARL, a dynamic, multilayer model. Previous research showed that SOM content was the most important source of uncertainty in the prediction of the leaching concentration with this model. The aim of this research was to use all information in the Dutch Soil Information System, both data on point support and soil maps, to construct a three-dimensional (3-D) map of SOM. A large part of the point data on SOM were field determinations. These field determinations of SOM were calibrated against laboratory measurements, to eliminate bias and to obtain an estimate of the precision of the field determinations. SOM predictions at multiple depths were obtained by 3-D simple kriging with varying local means. The local means were modelled as a stepwise trend in three dimensions, the spatial structure of which was derived from typical soil profile descriptions of the units of the Soil Map of the Netherlands 1:50 000. For the topsoil (15 cm) the stepwise trend was further differentiated according to three land use categories. The local means, averages of log-transformed SOM-values, were estimated from a stratified random sample of 1210 locations. Legacy data at points selected by non-probability sampling were not used for estimating the local means to guarantee unbiasedness. Residuals of the local means at 766,000 locations were interpolated by 3-D simple kriging based on the sum metric 3-D variography in both horizontal space and depth. Leave one out cross-validation on the 1210 locations showed that the mean prediction error was 0,029 and the root means squared error was 0,034. The mean squared deviation ratio is 0.79, indicating that the estimated prediction accuracy is an overestimation and actual errors at the validation locations are smaller.

keywords: Three-dimensional Interpolation (3-D), Geostatistics, Kriging, Digital soil mapping, Organic matter, Carbon, pesticides, Soil information system, Soil map
# High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: a Comparison of Machine Learning and Multiple Linear Regression Models

Ozias Hounkpatin – University of Bonn, Germany

Gerald Forkuor – West African Science Service Centre on Climate Change and Adapted Land Use—WASCAL, Germany

Gerhard Welp – University of Bonn, Germany

Michael Thiel – University of Wuerzburg, Germany

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties – sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen – in a 580 km² agricultural watershed in south-western Burkina Faso. Four statistical prediction models – multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB) – were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

**keywords:** Soil property mapping, Remote Sensing, RapidEye, Landsat, Multiple linear regression, Random forest regression, Support vector machine, Stochastic gradient boosting
Soil water nowcasting in 2-d and 3-d using electromagnetic con-
ductivity imaging and the ensemble Kalman filter

Jingyi Huang - School of Biological, Earth and Environmental Sciences, UNSW Australia, Kensington, NSW, 2052, Australia, Australia

Alex McBratney - Department of Environmental Sciences, Faculty of Agriculture and Environment, The University of Sydney, Biomedical Building C81, 1 Central Avenue, Australian Technology Park, Eveleigh, NSW, 2015, Australia, Australia

Budiman Minasny – Department of Environmental Sciences, Faculty of Agriculture and Environ-
ment, The University of Sydney, Biomedical Building C81, 1 Central Avenue, Australian Technology
Park, Eveleigh, NSW, 2015, Australia, Australia

John Triantafilis – School of Biological, Earth and Environmental Sciences, Faculty of Science, UNSW Australia, Kensington, NSW, 2052, Australia, Australia

Mapping and immediate forecasting of soil water content (theta) and its movement can be challenging. Although apparent electrical conductivity (ECa) measured by electromagnetic induction has been used, it is difficult to map the depth-specific theta along a transect or across a field. Across a 3.95-ha field with varying soil texture, an ensemble Kalman filter (EnKF) was used to monitor and nowcast theta dynamics in 2-d and 3-d over 16 days. The EnKF combined a physical soil-water tipping bucket model fitted with theta measured by soil moisture sensors and an artificial neural network model established using estimates of true electrical conductivity (sigma) generated by inversion of DUALEM-421S ECa data. Results showed that the spatio-temporal variation in depth-specific theta along the transect and across the field can be successfully modelled using the EnKF approach (Lin's concordance $\le 0.89$, RMSE $\le 0.03$ cm3/cm3), which was superior to the physical and empirical models. Soil water dried fast at the beginning of the irrigation and decreased with time and soil depth, which were consistent with the classical soil drying theory and experiments. It was also found that the soil dried fast in the loamy and duplex soils across the field, which was attributable to deep drainage and preferential flows. It was concluded that the EnKF approach can be used to better the irrigation practice so that variation in irrigation is minimised and irrigation efficiency is improved by applying variable rates of irrigation across the field. In addition, soil water status can be potentially nowcasted using this method with weather forecast information, which can provide guidance to farmers for real-time irrigation management.

keywords: DUALEM-421S, data assimilation, deep drainage, irrigation efficiency, real-time irri-
gation management
Sequential creation of a generic soil classification system using
taxa from existing systems

Philip Hughes – University of Sydney, Australia

Alex McBratney – University of Sydney, Australia

Budiman Minasny – University of Sydney, Australia

We can create a classification based on a common set of principles. In order to do this, there
is the requirement for a universal database. This database needs to be comprehensive, non-
duplicated, have the capacity for other suitable taxon to be added to it, and at the same time
to be small and simple. As an initial step, soil profile description data and taxa from two differ-
ing taxonomies have been successfully combined into a single database, referred to as the Total
Reference taxonomic database, or TRex. This has the data range to suit most soil properties in
both Australia and the United States, but there is extensive duplication and issues of equivalence
(whether a taxonomic tier such as Order, Suborder or Great Group of one taxonomic system is
equivalent to the same tier of another taxonomic system). We propose an additive model by
which the impediments of redundancy and equivalence can be countered and demonstrate how a
combined data set can be created that is just as effective for the purpose of creation of a world
taxonomy, yet has less unnecessary overlap. These ideas are incorporated, creating a hybridized
data set that covers that same ranges as TRex in principal component space, yet is significantly
smaller, making distance calculations and optimizations of various kinds much faster. This system
is tested by the addition of taxa from an independent data set in New Zealand. The possibility
of merging and simplifying of such different systems and the addition of independent third party
data demonstrates the possibility of similar mergers with taxonomies of other nations, creating a
global data pool for a world taxonomic system.

keywords: Soil classification, Soil Taxonomy, Soil profile description, Soil taxonomic description
Developing desktop, on-line, and mobile applications to support
soil experts and soil data end-users

Gábor Illés – National Agricultural Research and Innovation Centre, Hungary

Tamás Fonyó – National Agricultural Research and Innovation Centre, Hungary

László Pásztor – Institute for Soil Sciences and Agricultural Chemistry, CAR-HAS, Hungary

We develop modularly built software group for desktop, on-line, and mobile platforms in order to support and promote soil data collecting, -data sharing, and -data utilization. Software modules are available independently, and freely. Data visualization and distribution for end-users is served by SiteViewer module. In this module soil maps or tabular soil data are available for the desired locations. The module serves the tasks of landowners and forest managers. The SiteProfessional module is suitable to create and manage personal or shared soil databases among the users. It serves the needs of soil experts in surveying or mapping tasks to optimize sampling density and to check data availability of the given area. It is also suitable to build quality checked, national soil profile database. Our initiative supports the best utilization and archiving of existing data came from either former or future surveys. We also want to provide a virtual place and data store for co-operating expert groups. Last but not least we want to support land managers to access high quality soil data.

keywords: sharing soil data, personal and joint soil databases, software development, building soil database
# Mapping the Suitable Sites for Rice Production Using Analytical Hierarchy Process and Geographical Information System

Md Monjurul Islam – University of Tsukuba, Japan

Tofael Ahamed – University of Tsukuba, Japan

Ryozo Noguchi – University of Tsukuba, Japan

Bangladesh is the smallest country in the South-Asia with high density of population. Food security is not just an economic issue but also a political and societal problem in Bangladesh faces today. To feed the huge population there is no more alternatives without increase the agricultural production; maximum utilization of land resources. But the production is not so upright due to appropriate knowledge and scientific application of Soil.

Land is called the unique source of all material wealth. Soil is a vital part of the natural environment. The importance of land is therefore too much as it is influencing the standard of living. Usually Rangpur division is vulnerable than any other part of Bangladesh. To rise production, it is essential to secure lands for achieving optimum application of resources. Land Suitability Assessment (LSA) is one of the most important tools to increase rice production in the poverty prone areas. Therefore, the aim of the study was to identify the suitable sites for rice farming in the northern part of Bangladesh. To satisfy the research, a multi-criteria analysis was done and produced a suitable map for rice production with the integration of Geographical Information System (GIS) and Analytical Hierarchy Process (AHP). Data were collected from ground truth information and regional offices, Landsat and MODIS satellite databases of vegetation indices provided by USGS. The recent raster image from Google Earth Pro was used to confirm the consistency of suitability analysis. The suitability levels were ranked based on FAO land suitability classification as: Highly Suitable (S1), Moderately Suitable (S2), Marginally Suitable (S3), Currently Not Suitable (N2), and Permanently Not Suitable (N1).

To create the suitability map for rice weighted sum overlay tool in ArcGIS 10.3 was used. Then, the suitability map for rice production in the study area was designed. The weighted overly was done and found that 32.67% (192087 ha) was permanently not suitable and 1.19% (6997 ha) was currently not suitable for rice farming which occupied with settlement, water bodies, river and forest. Instead, 22.74 % (133702 ha) of study area was highly suitable, while 28.54% (167804 ha) was moderately suitable, 14.86% (87371 ha) was marginally suitable. This research provided info at local level that could be used by rice producer to select suitable fields and then it can be apply in other crops. It will also helpful for the extension workers and policy makers who serves in the agricultural sector.

**keywords:** Land Suitability Assessment (LSA), Geographical Information System (GIS), Rice Farming, Bangladesh, Analytical Hierarchy Process (AHP)
Soil salinity assessment through novel application of satellite ther-
mography

Konstantin Ivushkin - Wageningen University & Research, Netherlands

Harm Bartholomeus - Wageningen University & Research, Netherlands

Arnold K. Bregt - Wageningen University & Research, Netherlands

Alim Pulatov – Tashkent Institute of Irrigation and Melioration, Uzbekistan

Elisabeth N. Bui - CSIRO Land and Water, Australia

John Wilford - Geoscience Australia, Australia

An increased soil salinity is one of the severe land degradation factors that inhibits growth and development of crops. Therefore, up to date soil salinity information at increasing spatial and temporal resolution is vital for appropriate management practices and reclamation strategies. But conventional soil sampling are slow, expensive, and often cannot deliver the temporal and spatial resolution required.

The canopy temperature change is one of the stress indicators in plants. Its behaviour in response to salt stress on individual plant level is well studied in controlled experiments, but its potential for landscape scale studies is not investigated yet. In our study, possibilities of satellite thermography for landscape scale soil salinity assessment of cropped areas were studied. The performance of satellite thermography is compared with other approaches that have been used before, like Normalised Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). The study areas were Syrdarya province of Uzbekistan and four study areas in Australia. The diversity of the study areas allowed us to analyse behaviour of canopy temperature of different crops (wheat, cotton, barley) and different agriculture practices (rain fed and irrigated). MODIS and Landsat TM multiannual satellite images were used to measure canopy temperature. As ground truth for Uzbekistan study area we used a provincial soil salinity map. For the Australian study areas we used the EC map for the whole country. ANOVA was used to analyse relations between the soil salinity maps and canopy temperature, NDVI, EVI. Time series graphs were created to analyse the dynamics of the indicators during the growing season.

The results showed significant relations between the soil salinity maps and canopy temperature. The amplitude of canopy temperature difference between salinity classes varies for different crops, but the trend of temperature increase under increased salinity is present in all cases. The calculated F-values were higher for canopy temperature than for all other compared indicators. Also the visual comparison of the soil salinity map and the canopy temperature map show similar spatial patterns. The NDVI and EVI maps look more random and noisy and patterns are less pronounced than for the canopy temperature map. The strongest relation between the soil salinity map and canopy temperature was usually observed at the end of a dry season and in the period of maximum crop development.

Satellite thermography appeared to be a valuable approach to detect soil salinity under agri-
cultural crops at landscape scale.

keywords: soil salinity, satellite thermography, landsat, modis, vegetation indices
# The German National Soil Inventory - Soil sampling for climate change abatement

Anna Jacobs - Thünen Institute of Climate-Smart Agriculture, Germany

Arne Heidkamp - Thünen Institute of Climate-Smart Agriculture, Germany

Roland Prietz - Thünen Institute of Climate-Smart Agriculture, Germany

Due to several international agreements on climate change abatement (UN Framework Convention on Climate Change, Decision 529/2013/EU of European Parliament and of the Council) Germany is in duty to report greenhouse gas emissions from agricultural soils. Moreover, enhanced carbon storage in soils can be accounted for as greenhouse gas mitigation strategies. For such reports and policy decision making, a harmonized, representative, and reliable database of soil carbon stocks was missing so far. Thus, the German National Soil Inventory started over in 2011 assessing soil carbon contents and stocks in a nation-wide harmonized sampling system. A 8*8 km grid defined the 3100 sampling points under arable land, grassland and plantation crops. Landscape and soil type were described for each point and disturbed and undisturbed soil samples were taken from a 1 m3 pit. Sampling depths were 0-10, 10-30, 30-50, 50-70, and 70-100 cm. Further, from soils rich of carbon, an additional sampling of the subsoil was conducted at 100-150 and 150-200 cm. In order to evaluate the spatial heterogeneity on the field scale, eight additional soil cores were taken in a distance of 10 m around the pit. All samples were analyzed in the same lab following standardized protocols. In order to evaluate effects of arable management on soil carbon contents, farmers managing the field of a sampling point filled in a questionnaire covering the last ten years before sampling. Thus, we are able to link land use and arable management (tillage, fertilization...) information with the carbon stock measured.

This enormous database of harmonized soil carbon data is a valid basis of several ongoing and future subprojects. Currently, the team is working on (i) site specific and anthropogenic drivers of arable soil's carbon stocks via machine learning, (ii) vulnerability of carbon in organic and mineral soils via density fractionation and incubation, (iii) predictability of soil carbon fractions via near infrared spectroscopy, (iv) models which predict an arable site as a sink or a source for soil carbon, (v) stratification of the sampling area (Germany) in order to enhance the predictability of soil carbon stocks, and (vi) regionalization of soil carbon data for the generation of maps via machine learning. The poster will present the design of the project and selected results.

**keywords:** arable soil, soil carbon, soil management
# Prediction of soil organic carbon fractions using near infrared reflectance spectroscopy

Angélica Jaconi – Thünen, Germany

Leonardo Ramirez-Lopez – BUCHI Labortechnik AG, Switzerland

Axel Don – Thünen, Germany

Soil carbon fractions (C-fractions) are important to understand terrestrial carbon fluxes and ecosystem processes that affect soil carbon (C) stocks. Soil samples can be divided into fractions that are labile and easily decomposable (LA) and fractions with more stabilised resistant C (RE), where access by microorganisms is restricted. Various methods have been proposed to separate the soil samples into C fractions, however all of them are very time-consuming. Near-infrared (NIR) reflectance spectroscopy has been used as fast method to evaluate various soil properties with reasonable accuracy and low costs. The aim of the present study is to test a more rapid alternative to quantify LA and RE soil C-fractions based on the NIR analysis of bulk soils. The C-fractions of 140 topsoil samples (0-10 cm) from the German Agricultural Soil Inventory (GASI) were obtained by conventional methods. These samples were also scanned in the NIR region to obtain their spectra. We developed quantitative NIR models to predict the C-fractions in the GASI samples. In this presentation, we analyse what degree of accuracy can be expected from NIR spectroscopy for predicting different levels of Fractionation of soil organic carbon.

**keywords:** soil organic carbon fractions, near infrared reflectance spectroscopy, memory-based algorithms
Developing Combined Soil-climate Indices for Crop Suitability Recommendations

Ebrahim Jahanshiri - Crops For the Future, The University of Nottingham Malaysia Campus,
Malaysia

Ayman Salama - Crops For The Future, Malaysia

NA Nur Marahaini Mohd. Nizar - Crops For the Future, The University of Nottingham Malaysia Campus, Malaysia

Hilda Hussin - Crops For the Future, The University of Nottingham Malaysia Campus, Malaysia

Assessment of land capability for a crop requires the bringing together of data on soils, climates and crop requirements. In Malaysia, the main method to identify suitable species for agriculture is soil-crop suitability classification based on a range of soil properties that can impose limits on crop growth. This method has been used widely by governmental agencies to propose crop diversification projects since the 1970s. As information on genotypic and agronomic characteristics of crops and environmental factors such as climate become available, new methods of suitability assessment can be designed and tested. For instance, climate suitability indices that consider suitability as a function of temperature and rainfall at any location can be combined with the soil data to improve the accuracy of crop recommendations. In this work we combined a soil-based method with a climate suitability index to develop a recommendation tool primarily for currently underutilised crops. The assessment tool was then tested with data from globally available soil and climate datasets to validate the accuracy of the augmented suitability criteria. Crop soil and climate data were acquired from the FAO EcoCrop database. This data includes optimal and absolute minimum and maximum temperatures and rainfall for each cultivated species, together with the length of the growing season. Other information such as optimal soil pH level, depth, texture, fertility level, salinity resistance and drainage requirements were also incorporated into the tool. Soil data was extracted from the ISRIC global soil grids dataset; this database provides maps that are the result of interpolations of soil profiles across the globe. Climate information was extracted from the WorldClim dataset which provides gridded maps of historical averages at the global scale. A script for data processing and suitability calculation was developed using R statistical language. The combined soil-climate-crop suitability assessment tool was tested in the Bera district of Pahang, Malaysia. We will demonstrate the advantages of the new analytical tool in comparison with the traditional method of land capability assessment and its use by farmers to determine how their cropping systems might be diversified. We will also discuss the implications of using globally available datasets for the purpose of local crop suitability assessments.

keywords: crop suitability, soil, climate, classification
Comparison of multinomial logistic regression and random forest
classifiers in digital mapping of soil in western Haiti

Wesly Jeune – UNIQ, Haiti

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

Marcio R. Francelino – Federal University of Viçosa, Brazil

Genelicio C. Rocha – Federal University of Viçosa, Brazil

Eliana Souza – Federal University of Viçosa, Brazil

Due to the low availability of data on the soils of Haiti, digital soil mapping is suitable for providing quick and accurate spatial information to support decision makers in agricultural and environmental planning programs. This study aims to map soils in western Haiti by comparing Multinomial Logistic Regression (MLR) and Random Forest (RF) classifiers. Digital soil mapping (DSM) techniques were evaluated to delimit soil classes in western Haiti (~4300 km²). A soil survey was carried out through a mixed sampling strategy, describing 140 soil profiles, for which samples were taken for physical and chemical analyses. The algorithms MLR and RF were applied in the classification process based on soil-landscape relationship, involving climate, lithology and landscape attributes as predictor variables. Classifier performances were assessed using Kappa index derived from a confusion matrix and map validation was carried-out using an independent sample of 118 points. A comparison between the classifiers was carried out through Z statistic. Kappa index determined in this process 0.47 for MLR and 0.33 for RF indicate that the classification performance can be characterized as good for MLR and moderate for RF. A statistically significant difference between Kappa values was encountered with a Z statistic of 1.98 at a 5% significance level. Kappa index in validation of the final map was 0.49; while the overall accuracy value was 0.59. Map units with the largest percentage in the study area were: Entisols (32.4%), rock outcrops (22.1%) and Mollisols (13.2%). The approach proved to be promising as a method to map the soils of other parts of the country.

keywords: Soil survey, Soil Mapping, Auxiliary data, Data mining
Pre-processing of on-the-go mapping data

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

Viacheslav Adamchuk - Department of Bioresource Engineering, McGill University, Canada

Sophie Lauzon - Department of Bioresource Engineering, McGill University, Canada

Yue Su - Department of Bioresource Engineering, McGill University, Canada

Md Saifuzzaman - Department of Bioresource Engineering, McGill University, Canada

Hsin-Hui Huang - Department of Bioresource Engineering, McGill University, Canada

On-the-go proximal soil sensors offer promising benefits to the mapping of soil spatial variability, as they provide an increased density of measurements at a relatively low cost. Numerous on-the-go sensors have been developed based on a variety of design concepts. Among them, mapping apparent soil electrical conductivity (ECa), along with field elevation, has become a standard practice in mapping field heterogeneity. In this study, a DUALEM-21S sensor and a real-time kinematic (RTK) Trimble AgGPS 542 global navigation satellite system (GNSS) receiver were fused and the data from both sensors were logged using the dedicated DUALEM_DAQ software. The generated ECa and elevation maps reflect systematic and random sources of soil variation as well as numerous errors caused by the mapping procedures. Taking the ECa data as an example, a general framework for processing the data was developed. Pre-processing of the data was mainly based on the time intervals, the distance between every two point measurements (i.e. speed) and the change of the variables within a small window of neighbor measurements. Other parameters were also considered, for example, pitch and roll. The detailed steps include (i) raw data screening, (ii) median filtering each ECa value, (iii) removing the starting (warm up) uncertain values, (iv) removing the start-and end-pass delays, and the stop-and-go segments within the field, (v) removing the points with over speed limits, (vi) removing ECa values outside the user-defined minimum and maximum ECa limits, (vii) removing small patches or narrow strips with extremely low or high ECa that are not closely related to the immediate neighbors, and (viii) removing the values when pitch or roll changes are outside the acceptable limit. The pre-processing procedures for the logged RTK elevation data, as well as other on-the-go sensors, are similar. In this study, several fields from Quebec and Ontario, Canada, were selected as examples to demonstrate the detailed pre-processing procedures. About 5% to 20% of the data were regarded as erroneous data and were thus removed from the raw data for different fields. This technique allows more accurate representation of soil electrical conductivity and field variability, which provide important data layers in algorithms for prescribing variable rates of production inputs.

keywords: on-the-go proximal soil sensing, pre-processing, apparent electrical conductivity, elevation
# The use of proximal soil sensor data fusion and digital soil mapping for precision agriculture

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

Viacheslav Adamchuk - Department of Bioresource Engineering, McGill University, Canada

Songchao Chen - INRA InfoSol Unit & UMR SAS, INRA, AGROCAMPUS OUEST, France

Asim Biswas - School of environmental sciences, University of Guelph, Canada

Maxime Leclerc - Department of Bioresource Engineering, McGill University, Canada

Raphael Viscarra Rossel - Land and Water Flagship, CSIRO, Canada

Proximal soil sensing (PSS) is a promising approach when it comes to detailed characterization of spatial soil heterogeneity. Since none of existing PSS systems can measure all soil information needed for implementation precision agriculture, sensor data fusion can provide a reasonable alternative to characterize the complexity of soils. In this study, we fused the data measured using a gamma-ray sensor, an apparent electrical conductivity (ECa) sensor, and a commercial Veris MSP3 platform including a optical sensor measuring soil reflectance at 660 nm and 940 nm, a soil ECa sensor and a pH sensor, with the addition of topography for the prediction of several soil properties, i.e. soil organic matter, pH, buffer pH, phosphorus, potassium, calcium, magnesium, aluminum. A total of 65 sampling locations were selected from a 38.5 ha field in Ontario, Canada. Among them, 35 locations were selected by a random stratified sampling strategy. The stratification grid was 1 ha. Sampling was prohibited in areas near the field boundaries and within a safety margin from the drainage system. 20 locations were selected using a neighbourhood search approach, a spatial data integration strategy. These two sample datasets were used as the calibration dataset to build the model between soil properties and readings from different proximal soil sensors. The remaining 10 sensing locations were used as an independent validation dataset. Partial least square regression (PLSR) was performed on the data from each individual sensor and different sensor combinations (sensor data fusion). For most soil properties, predictions based on sensor data fusion were better than those based on the output of individual sensors. By fusing the data from all of the proximal soil sensors, more properties can be predicted simultaneously (R2>0.5, and RPD>1.50). After choosing the optimal sensor combination for each soil property, different digital soil mapping methods, including support vector machines (SVM), random forest (RF), multivariate adaptive regression splines (MARS), regression trees (RT) and back-propagation artificial neural network (BP-ANN) were used to estimate variograms and pursue regression kriging. High resolution maps were thus interpolated with the most successful methods. The performance of the two different sampling strategies was compared by the prediction accuracy from the validation samples. We thus conclude that proximal soil sensor fusion paired with the digital soil mapping method is a promising way to offer the essential soil information needed for precision agriculture.

**keywords:** vis-NIR spectroscopy, Gamma ray, electrical conductivity, topography, sensor fusion, data fusion, digital soil mapping
Effect of different soil compaction levels on prediction of soil prop-
erties using MIR spectra in situ

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

Eko Leksono - Department of Bioresource Engineering, McGill University, Canada

Asim Biswas - School of environmental sciences, University of Guelph, Canada

Viacheslav Adamchuk - Department of Bioresource Engineering, McGill University, Canada

Nandkishor Dhawale - Department of Bioresource Engineering, McGill University, Canada

Zhou Shi - Institute of Agricultural Remote Sensing and Applied Information Technology, Zhejiang
University, Canada

Bo Stenberg - Department of Soil and Environment, Swedish University of Agricultural Sciences, Sweden

Mid-infrared (MIR) diffuse reflectance spectroscopy can provide rapid and relatively accurate predictions for a number of soil properties. Along with air-dried ground spectral measurements in the laboratory, much attention has been given to determining soil properties in situ. The soil moisture effect has received much attention while research on other effects is scarcely reported. The differences in soil compaction has a significant influence on structure and porosity and thus, on the reflected spectra. The objective of this study was to create different soil compactions on natural wet soil samples in the laboratory and examine their effects on the MIR spectra, and compare them with in situ collected spectra. A total of 120 (56+64) locations were selected from two agricultural fields from Macdonald farm of McGill University: one organic field and one mineral field. At each location, the MIR spectra were recorded in situ using a portable MIR spectrometer (898-1811cm⁻¹). The level of compaction was measured using a portable penetrometer at the same time. After which, bulk samples were collected and packed into plastic bags, sealed to avoid the loss of soil moisture and sent to the laboratory. Spectroscopic measurements using the same equipment were carried out in the laboratory on those 120 samples at three different compactions (initial compaction, medium compaction and high compaction). After the spectral measurements, a range of properties were measured. Partial least square regression models were developed between these properties and the spectra measured at four different compactions (in situ, c1, c2 and c3). By comparing the predictions, we concluded that the influence of different compaction levels on mineral soil and organic soil is different. For both fields, with some compaction level, prediction with spectra on compacted soil in the laboratory is always more accurate than for in situ conditions. Predictions with compacted soils are closer to those with air-dry non-ground samples.

keywords: mid-infrared spectroscopy, in situ, soil compaction, organic soil, mineral soil, soil organic matter, soil texture
Can organic carbon in soil cores be predicted by VNIR and MIR
techniques in alpine landscape?

Xiaolin Jia - Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, China

Songchao Chen - Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University & INRA InfoSol Unit & UMR SAS, INRA, AGROCAMPUS OUEST, France

Yuanyuan Yang - Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, China

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

Wu Yu - Department of Resources and Environment, Tibet University, China

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

1. Introduction

Carbon is the essential component of all organic matter. The Qinghai–Tibet Plateau is an important part of the global terrestrial ecosystem because it is a large and concentrated distribution area of frozen soil at low latitudes. It is not only sensitive to global climate change, but also plays an important role in the Asian climate and global climate change. To our knowledge, no studies have reported the potential to predict SOC in individual soil cores (0–100 cm) in an alpine region. The aim of this research was to evaluate the potential of predicting the SOC concentration in individual soil cores using VNIR and MIR DRS.

2. Materials and methods

The study area is located on the Sygera Mountains in the southeast of the Qinghai–Tibet Plateau, China. Elevation of forest land ranges from 3300 to 4300 m above sea level, and alpine shrub and meadows are found above 4300 m. We examined 24 soil cores (0–100 cm) from the Sygera Mountains on the Qinghai–Tibet Plateau, considering field-moist intact VNIR, air-dried ground VNIR and air-dried ground MIR spectra at 5-cm intervals. According to the elevation, two thirds of soil cores in each class, shrub meadow, forest and total dataset, were divided into a calibration set and the remaining cores were divided into a validation set. Preprocessed spectra were used to predict SOC in the soil cores by partial least square regression (PLSR) and support vector machine (SVM).

3. Results and conclusions

The SVM models performed better in three predictors with the ratio of performance to inter-
quartile distance (RPIQ) and R2 values typically exceeding 2.00 and 0.80, respectively. The SVM
using the DRS technique indicated accurate predictive results of SOC in each core. The RPIQ
values of the shrub meadow, forest and total dataset predition by air-dried ground VNIR were
respectively 1.97, 2.68 and 1.99, compared to the values by field-moist intact VNIR (1.95, 2.07,
1.76) and air-dried ground MIR (1.78, 1.96, 1.74). Generally, the results were more accurate at
0–60 cm soil depth than at 60–100 cm, indicating suitability of spectroscopic techniques for the
prediction of SOC stock density. We conclude that the DRS technique is an efficient and rapid
method of SOC prediction and has potential in dynamic monitoring of SOC stock density on the
Qinghai–Tibet Plateau.

keywords: soil profile, VNIR, MIR, organic carbon
Application of portable XRF and VNIR sensors for rapid assess-
ment of soil heavy metal pollution

Xiaolin Jia - Institute of Agricultural Remote Sensing and Information Technology Application,
Zhejiang University, China

Bifeng Hu - Institute of Agricultural Remote Sensing and Information Technology Application,
Zhejiang University, 310058 Hangzhou, China, China

Songchao Chen - INRA Orléans, InfoSol Unit, US 1106, CS 40001, Ardon, 45075, Orléans cedex
2, France; UMR SAS, INRA, AGROCAMPUS OUEST, 35000 Rennes, France, China

Jie Hu - Institute of Agricultural Remote Sensing and Information Technology Application, Zhe-
jiang University, 310058 Hangzhou, China, China

Fang Xia - Institute of Agricultural Remote Sensing and Information Technology Application, Zhe-
jiang University, 310058 Hangzhou, China, China

Junfeng Xu - College of Science, Hangzhou Normal University, Hangzhou 310036, China., China

Yan Li - Institute of Land Science and Property Management, School of Public Affairs, Zhejiang
University, Hangzhou, China

Zhou Shi - Institute of Agricultural Remote Sensing and Information Technology Application,
Zhejiang University, 310058 Hangzhou, China, China

Rapid heavy metal soil surveys at large scale with high sampling density could not be con-
ducted with traditional laboratory physical and chemical analyses because of the high cost, low
efficiency and heavy workload involved. This study explored a rapid approach to assess heavy
metals contamination in 301 farmland soils from Fuyang in Zhejiang Province, in the southern
Yangtze River Delta, China, using portable proximal soil sensors. Portable X-ray fluorescence
spectroscopy (PXRF) was used to determine soil heavy metals total concentrations while soil pH
was predicted by portable visible-near infrared spectroscopy (PVNIR). Zn, Cu and Pb were suc-
cessfully predicted by PXRF (R2 >0.90 and RPD >2.50) while As and Ni were predicted with less
accuracy (R2 <0.75 and RPD <1.40). The pH values were well predicted by PVNIR. Classification
of heavy metals contamination grades in farmland soils was conducted based on previous results;
the Kappa coefficient was 0.87, which showed that the combination of PXRF and PVNIR was
an effective and rapid method to determine the degree of pollution with soil heavy metals. This
study provides a new approach to assess soil heavy metals pollution; this method will facilitate
large-scale surveys of soil heavy metal pollution.

keywords: portable X-ray fluorescence spectroscopy, portable visible-near infrared spectroscopy, soil heavy metals contamination
Validation of the coarse-scale remotely sensed soil moisture data
by using ground measurements with a hybrid geostatistical down-
scaling method

Yan Jin - State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, University of Chinese Academy of Sciences, China

Yong Ge - State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, China

Jianghao Wang - State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, China

Monitoring the surface soil moisture (SM) acts a critical role in applications such as hydrology, meteorology and agriculture. The passive microwave remote sensing as a monitoring tool for large regions has been widely applied. However, these sensors always own a coarse spatial resolution of about 25 to 50 km. To validate these satellite SM data, addressing the scale mismatch between its coarse-scale estimates and local scale measurements is one of the key issues. Downscaling is considered to be an implement method to predict SM at fine resolution for matching the scales. Following the available data sources, the downscaling approaches of passive microwave remotely sensed SM data fall into two basic categories. One is to combine the passive and active microwave estimates, and the alternative is based on optical/thermal remotely sensed data. The latter is preferred because of the accessibility and high temporal-spatial resolution of optical/thermal remotely sensed data. This paper aims to examine the potential of a hybrid geostatistical downscaling method named area-to-area geographically weighted regression kriging (GWATARK) for validating passive microwave remotely sensed SM product.

The GWATARK method is applied to improve the spatial resolution of Advanced Microwave Scanning Radiometer 2 (AMSR-2) and Soil Moisture and Ocean Salinity (SMOS) SM products. This downscaling model involves a trend component and a residual component, employing optical/thermal remotely sensed data as explanatory variables (e.g., land surface temperature, vegetation index, temperature-vegetation index, apparent thermal inertia and soil evaporative efficiency). The downscaled SM predictions of 1 km resolution during one-year period are obtained over the upstream area of the Heihe River Basin in northwestern China where includes a ground-based monitoring network. The downscaled predictions are compared with the area-averaged SM of 1 km resolution from in situ measurements within the study area. Furthermore, the SM data are validated for different temporal and spatial by using statistical parameters, such as bias (BIAS), root mean square error (RMSE), correlation coefficient (R) and the slope of the linear regression (S). It shows that GWATARK downscaling method improves the statistics of SM data for both AMSR-2 and SMOS products, indicating that the downscaled predications can explicitly represent the variability of SM at fine resolution. The GWATARK has strong potential to be as a tool to evaluate coarse-scale remotely sensed soil moisture data.

keywords: soil moisture, downscaling, validation
# Monitoring soil heavy metal distribution over three different time periods using trivariate linear mixed models

Liana Johnson - The University of Sydney, Australia

Thomas Bishop - The University of Sydney, Australia

Gavin Birch - The University of Sydney, Australia

Remediated sites that have been previously contaminated may still have the potential to cause harm to humans and the surrounding environment. It is for this reason that many remediated sites require monitoring to ensure managed soil contaminants do not become exposed or leach into groundwater. If contaminants enter waterways or groundwater, the area of impact can extend beyond the site of the contaminant source.

Bicentennial Park - a recreational area within Sydney Olympic Park (Sydney, Australia), was a wetland system treated as a landfill for industrial and municipal waste. The area was remediated in 1988 by covering the landfill with ~50cm of soil and transforming it into parkland. In contrast, more recent remediation methods use clay capping and geotextile fabric to prevent leaching and re-emergence. The method used for remediation of Bicentennial Park in combination with soil movement and erosion over time elevates the risk of human and environmental exposure of contaminants.

Relatively few studies involve monitoring of previously contaminated sites and those that do generally compare samples pre- and post-remediation; even fewer compare between multiple sampling events. A significant amount of time has passed since Bicentennial Park's opening in 1988 and over the years it has been surrounded by remediation work and major road networks with increasing traffic density. During this time it is possible that in addition to original contaminants being exposed, new sources of contamination have emerged.

The objectives of this study are to determine whether there has been any change in soil heavy metal concentrations over time, and whether or not subsequent recontamination has occurred. We will compare heavy metal concentrations in soil samples obtained from Homebush Bay in 1990 not long after creation of the parklands, samples taken in 2002 following remediation of Sydney Olympic Park, and samples taken in 2015-25 years since the initial survey.

To compare the three datasets the study will use trivariate linear mixed models to build a space-time model for the three datasets. This will be used to predict at three spatial supports - over a 20 metre grid, block support of different previous land uses, and across the entire site. For each spatial support we will examine contrasts for years 2002-1990, years 2015-2002 to determine whether there has been any change in concentrations and whether there are re-emerging sources.

**keywords:** Monitoring, Linear mixed modelling, Remediation, Environmental change, Sydney, Soil contamination
Estimating soil profile attributes with proximal sensors and a spectral inference system

Edward Jones – University of Sydney, Australia

Budiman Minasny – University of Sydney, Australia

Alex. McBratney – University of Sydney, Australia

Proximal soil sensors are moving the domain of quantitative soil science from the laboratory into the field. To utilise these sensors effectively platforms must be developed to access sensor information and predict soil properties in near real-time. We present a framework to predict soil attributes in-situ from the conjoint use of visible and near-infrared (VisNIR) diffuse reflectance spectroscopy and portable x-ray fluorescence (pXRF) spectroscopy. We further demonstrate how information from these sensors can be combined with a spectral inference system (SPEC-SINFERS) to greatly amplify the number of properties predicted. The effectiveness of the platform was assessed on fifteen soils across the state of New South Wales, Australia. At each location a soil pit was dug and three 1 m vertical transect surfaces were prepared at 0.5 m lateral spacing. Each transect was scanned using VisNIR and pXRF at 2.5 cm intervals to give a total of 123 (3 x 41) scans per device at each soil pit. Scanning took place under field moist conditions and bulk samples were taken at 10 cm intervals for laboratory analysis. We explore if these devices can provide effective characterisation in terms of a range of attributes including: mineral composition; texture; organic carbon; pH; and cation exchange capacity. This information may be utilised in the field for improved decision making, including multi-phase mapping techniques, management zone delineation and pedogenesis investigations.

keywords: proximal soil sensing, digital soil morphometrics, soil inference system
Spatial modelling of soil carbon in Sri Lanka using sparse datasets
with samples collected with different depth supports

Senani Karunaratne – The University of Sydney, Australia

Tom Orton – The University of Queensland, Australia

Tom Bishop – The University of Sydney, Australia

Jason Lessels – The University of Sydney, Australia

Renuka Ratnayake – National Institute of Fundamental Studies, Sri Lanka

In many developing countries the availability of soil data is limited and often collected through different small geographic soil surveys each with varying depth supports. In this study we used two soil survey datasets of soil carbon collected using non-probabilistic sampling schemes namely; (a) country-wide soil survey (n ≤ 387) with data available based on soil horizons and (b) regional soil survey (n ≤ 166) with data available based on fixed depth support. Rather than pre-processing the data with equal-area quadratic splines or other depth functions to harmonise the depths of observations, we used the approach by Orton et al. (2016) based on area-to-point kriging to fit one model for all depths to the data in its original vertical support. We split the individual datasets separately as model calibration (85 %) and validation (15 %) using a conditional latin hypercube sampling algorithm using a variety of environmental covariates that coincided with the individual sampling locations. A linear mixed model (LMM), with spatially- and vertically-correlated random effects modelled through a product-sum covariance model, was used for analysis. Thereby all data could be modelled in a single step without need for prior harmonization. Fixed effects were modelled based on a linear function of covariates (terrain, climate, remote-sensing data), and models with and without interaction terms with depth were compared. We present validation results and finally maps of predictions across Sri Lanka for three depth intervals (0-0.3 m, 0.3-0.6 m and 0.6-1.0 m) at a spatial support of 100 m. This constitutes the first ever country-wide soil attribute mapping carried out in Sri Lanka using modern digital soil mapping techniques. The outcomes of this work will provide valuable information on the soil carbon distribution across the tropical island. Additionally, information generated will act as a base towards development of a country-specific national carbon accounting model. This study also demonstrates spatial modelling of soil attributes with limited data collected over varying vertical supports, a common problem in most developing countries.

keywords: Digital soil mapping, sparse datasets, 3 D modelling
# The Effect of Topography on Spatial Variation in Soil Health

Arthur Khozin – Ben-Gurion University of the Negev, Israel

Tal Svoray – Ben-Gurion University of the Negev, Israel

Peter Atkinson – Lancaster University, United Kingdom

Profound changes in soil characteristics and soil health are observed due to land degradation processes. This affects human nutrition and health through impacts on food production. There is, therefore, a growing need and an interest in soil health mapping to assist decision-making regarding land use policies and soil management. Soil health tests developed recently are provided as quasi-point data in specific plots and there is, therefore, a crucial need to develop techniques to provide soil health mapping over wide areas, for example, to increase farming efficiency via precision agriculture.

Here, we address the landscape scale at which spatial variability is affected mainly by three factors: soil type; surface topography; and water distribution. Topography and water distribution, as soil functions that affect soil quality, include the ability: i) to accept, hold, and release nutrients and other chemical constituents; and ii) to accept, hold and release water to plants and surface and groundwater recharge. It is, therefore, hypothesized that soil health can be predicted efficiently in space by evaluating soil attributes simultaneously using regression kriging with topography and/or its derivatives as covariates.

The aim of this study was, therefore, to explore the potential to map soil health over an agricultural catchment using topographic terrain indices as covariates. Secondary aims were assessment of model residuals and validation of the use of Landsat 7 data as further auxiliary variables.

Based on the stratified random sampling method, 130 point measurements of the soil were distributed spatially in the Harod valley, Israel, and 15 soil properties were measured at each, during 20-23/03/2011. Based on these data, the Cornell Soil Health Test (CSHT) was evaluated using scoring functions. The CSHT was then interpolated in the Harod valley basin using Regression Kriging. Terrain indices, wetness index, Stream power index, Sediment transport index, Profile curvature, Planar curvature, and Topographic slope were used as covariates to predict hydromorphological processes as they were reported as correlating with erosion, deposition, compaction and saturation.

The results show that there is a spatial pattern in the model residual's which is correlated with remotely sensed vegetation indices and landform attributes. The use of NDVI, NDIMDIR as auxiliary variables together with Terrain indices, reduced the RMSE in the interpolated map. However, the reduction in RMSE could lead to biased representation of soils, due to foliage at planted fields in the catchment. Reclassification of Terrain indices into more discrete groups reduced this variability in the interpolation procedure.

**keywords:** Soil Health, Geostatistics, Regression Kriging, Spatial interpolation
Application of colorimetric analysis of soils using flatbed scanners

Nataliya Kirillova - Soil Science Faculty, Moscow State University, Leniskie gory, Moscow,
119991 Russia, Russia

David Kemp - School of Geosciences, University of Aberdeen, Old Aberdeen, AB24 3UE, UK,
United Kingdom

Zinaida Artemyeva - V.V. Dokuchaev Soil Science Institute, Pyzhevskii 7, bld. 2, Moscow, 119017
Russia, Russia

Tatiyana Sileova - Soil Science Faculty, Moscow State University, Leniskie gory, Moscow, 119991
Russia, Russia

The physical characterization of soil horizons based on colour is a key diagnostic method in the description of soil profiles, and has been integrated in to diagnostic keys such as the World Reference Base for Soil Resources [2]. The Munsell colour system has been the primary qualitative or semi-quantitative means to describe soil colour.

At the same time, the main quantitative way to describe colour in soil science is through the CIE L*a*b* system. In this colour space system, the colour coordinates (a*, b*) are separated from the lightness (L*) coordinate [3].

Flatbed scanners have been used previously for colorimetric characterization of rocks and sediments [1].

In this study, we explore the feasibility of using flatbed scanners to derive colorimetrically accurate images and data of soil samples, and hence assess the suitability of the method as a diagnostic tool for soil characterization. The basic principle underlying our approach is to characterise and calibrate scanners using a variety of colour charts/targets. We tested the accuracy of these calibrations through analysis of a soil sample set containing 161 spectrophotometrically analysed soils.

The efficacy of our approach was tested with 2 low-cost scanners, and included analysis of 2 commercial colour charts, 6 printed colour charts, and 3 editions of the Munsell Soil Colour chart to assess optimum colorimetric calibration methods. For both scanners tested, we found that accurate colour characterization could be achieved for >95% of the studied soil samples (ΔEab* colour errors of <3, equating to a colour difference barely perceptible by the human eye).

Of all the studied printed charts, the Munsell soil colour charts gave the best result to characterize scanners colorimetrically. This is an encouraging result given their popular use amongst soil scientists. We find that using a Munsell chart for scanner calibration can provide mean ΔEab* of <2, with ΔEab* <3 for more than 90% of tested samples.

These results demonstrate the merit and efficacy of this rapid and low cost approach for soil colour evaluation.

1. Kemp, D.B. 2014. Colorimetric characterisation of flatbed scanners for rock/sediment imaging. Computers & Geosciences, 67, 69–74.

2. WRB 2014. World reference base for soil resources. International soil classification system for naming soils and creating legends for soil maps. IUSS Working Group. World Soil Resources Reports No. 106. FAO, Rome.

3. Wyszecki, G. & Stiles, W.S., 2001. Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd ed. Wiley, New York

keywords: soil colour, digital soil morphometrics, flatbed scanner, the Munsell charts
Gamma radiometric mapping of soil texture at field and regional
scale

Sylvia Koszinski - Leibniz Centre for Agricultural Landscape Research (Leibniz-ZALF e.V.), Germany

Tobias Heggemann - INRES, Soil Science and Soil Ecology, University of Bonn, Germany

Malte Ibs-Von Seht - Federal Institute for Geosciences and Natural Resources Hannover, Germany

Stefan Pätzold - INRES, Soil Science and Soil Ecology, University of Bonn, Germany

Hauke Petersen - Federal Institute for Geosciences and Natural Resources Hannover, Germany

Annika Steuer - Federal Institute for Geosciences and Natural Resources Hannover, Germany

Gerhard Welp - INRES, Soil Science and Soil Ecology, University of Bonn, Germany

Michael Sommer - Leibniz Centre for Agricultural Landscape Research (Leibniz-ZALF e.V.), Germany

Within-field variation of soil properties is increasingly imaged by geophysical soil sensing (ground penetrating radar, electrical conductivity (ECa), resistivity (ER) and, more recently, gamma ( ) radiometry) and on-the-go measurements at field scale are progressing well. Continuous ground and airborne -spectrometry, respectively, are known to be a good choice for topsoil investigation at field scale, but still need to be tested for consistency and transferability in case of regional approaches. For quite some time, airborne -ray spectrometry is used to explore and map geological units across large areas. Here, we question the ability of airborne information to reflect areas of soil textural properties within a hummocky ground moraine landscape with comparable parent material.

Two regions of the Uckermark, Kraatz (10 km²) and Dedelow (12 km²), were investigated by a helicopter mission in autumn 2014 equipped with a -device (RS-500, Radiation Solutions Inc., CAN). Two fields (25 ha each) in both regions were mapped at about the same time by a tractor-driven -device (RS-700, Radiation Solutions Inc., CAN) and accompanied by ground truth soil sampling (120 positions). At field scale, ground- and airborne based -spectrometric maps of K, U, Th and total counts were used to be related to site (point) specific soil information. For regional aspects, existing soil maps of textural units were compared with the airborne maps.

Gamma derived maps of different raster cell sizes were analysed for their spatial association
with soil texture at reference points, their spatial validity and transferability to other fields. At field
scale the ground-based information was more accurate in reflecting point information compared to
the airborne information. We attribute this to the very large footprints of airborne measurements.
Nevertheless, for the regional purpose the airborne information seems an unavoidable means to
support digital soil mapping for larger environments and its spatial patterns.

keywords: Gamma spectrometry, proximal soil sensing, field scale, regional scale, soil texture
Assessment of Uncertainty on a Digital Soil Map: a sensitivity
analysis on the uncertainty indicators

Philippe Lagacherie - INRA, LISAH, Montpellier, France

Dominique Arrouays - INRA Infosol Orléans, France

Hocine Bourennane - INRA UR sols Orléans, France

Cécile Gomez - IRD, LISAH, Montpellier, France

Manuel Martin - INRA, Infosol, Orléans, France

Nicolas Saby - INRA, Infosol, Orléans, France

Digital Soil Map uncertainty is usually evaluated from a set of independent soil observations – i.e. that are not used for calibrating the DSM model –. As any statistical parameters derived from a set of individuals, the uncertainty indicators – e.g., R2, ME, PICP,...– could be sensitive to the number and the locations of these soil observations. To our knowledge, this issue has not be considered yet in the literature since it would require performing a sensitivity study from a base spatial sampling that had to be dense and extended enough for picturing the real underlying soil pattern and allowing the test of multiple sampling schemes, which is not feasible in practice.

In this paper, such sensitivity analysis is performed from the virtual pattern of topsoil clay
content of bare soil surfaces at 5 meter resolution over 300 km² in the Cap Bon region (Tunisia).
This pattern, derived from a hyperspectral image, was accurate enough (R2val ≤ 0.75), free of
visible artefacts and pedologically plausible for being considered as a fair representation of reality.

We estimated the uncertainty of a DSM model obtained by calibrating from virtual values of
clay content a Quantile Random Forest using relief soil covariates and geographical location (the r
and n of "scorpan"). Different sampling methods and numbers of validation sites were considered,
each time with 100 repetitions. The result showed that i) the range of variation on the uncertainty
indicators raised a lot below a given number/density of validation sites, whatever the sampling
method; ii) a non-negligible uncertainty range may remain for large/dense validation datasets.

We will discuss these results in the perspective of better assessing the quality of the Digital
Maps of soil properties that are currently being produced across the world.

keywords: Digital Soil Mapping, Uncertainty assessment, Spatial sampling, Imaging spectroscopy
Predicting and Mapping Total Si over the main territory of France

Amelia Landré – inra, France

Nicolas Saby – inra, France

Sophie Cornu – inra, France

Budiman Minasny – University of Sydney, Australia

Marion Bardy – inra, France

Bernard Barthes – IRD, France

Antoine Richard – INRA, France

Jean-Dominique Meunier – CEREGE, France

The biogeochemical cycle of Si has become a subject of increasing interest in recent years because of its interconnection with the C cycle and global change. Recent studies have suggested that land use and particularly agriculture can modify the Si cycle. However, the consequences of such a modification for the global Si cycle and for the growth of crops which are Si accumulators (most of the cereals) are still not well constrained. The bioavailable Si fraction is governed by several factors, among which the amount in total Si (Si-tot) that represent the long term weatherable pool. This amount has thus to be quantified at the territory scale. However, its determination is time consuming and costly. It is performed by ICP-AES after mineralization by sodium peroxide and sodium carbonate fusion. We thus propose an alternative estimation of the Si-tot at the French territory scale, within the project BIOSISOL (ANR-14-CE01-002), based on the samples of the French soil monitoring network (RMQS). This network consist in 2130 sites sampled in a regular 16 × 16-km grid. We measured topsoil Si-tot at 630 sites of the RMQS network consisting in one point out of four from this network (that is 450 points) and a set of 150 points selected randomly among the remaining sites. Mid-infrared reflectance and a large set of soil properties were measured at all sites of the RMQS. These soil properties are particle-size distribution, pH water, organic carbon, Total N, soluble and total P contents, calcareous, CEC and exchangeable cations (K, Ca, Mg, Na), Bo and 8 trace elements (Cd, Cr, Cu, Co, Ni, Pb, Tl, Zn). The dataset was then divided in 3 sets: Calibration, validation and test sets. The two formers were defined using a repeated folding step within the 650 sites and the latest corresponded to the 1500 remaining sites. Using a regression rule algorithm called CUBIST, we found that Si-tot was very well predicted by the mid-infrared spectroscopy (validation R² ~ 0.98 and test R²~0.97) and by conventional pedotransfer functions using soil properties (validation R² ~ 0.98). This last model was used to evaluate the MIR model. Based on the MIR predictions, we then mapped the concentration of Si-tot across France using digital soil mapping techniques.

keywords: Si, Monitoring, Digital Soil Mapping, Spectroscopy, predictive models
Comparison of methods to fill data gaps in soil profile databases

Vince Lang – Discovery R&D Center, Hungary

Legacy soil profile databases are among the most important sources of information for many
mapping and modelling approaches among others. Although soil profile information is collected for
several decades, changes in sampling and soil laboratory methods often makes regional and global
database diverse in many scales. Many harmonization approaches have been made, and global soil
databases are available for the users, but these databases are heavily loaded with missing data,
either for individual soil horizons, or complete parameters for the soil profile.

In this study different data mining and missing data filling methods are compared using legacy
soil profile databases including the WISE 3.1 database and the AfSIS legacy soil profile database.
Methods using only the studied soil profile to fill data gaps were also used along with methods
using a training dataset to fill in missing information. Data mining methods were also tested with
the use of the complete database and also tested using sub databases with soil profiles within the
same taxonomic group. Results show that in the case of an available quality checked training
database data filling results compared to methods like equal area spline function can yield similar
results for some soil taxonomic groups and better for taxonomic groups with soil property changes
difficult to model with an equal area spline function.

With the definition of a complete reference global soil profile database data mining techniques
could improve the soil profile data filling approaches and could result in better base information
for mapping and modelling of soil properties and classes.

keywords: soil profile data, data gaps, random forest, equal area spline
Optimizing spatial sampling for multiple objectives

Murray Lark – British Geological Survey, United Kingdom

In the early days of pedometrics its practitioneres were quick to observe that when we have
a variogram of a soil property we can use it to find the best choice of sample grid spacing to
support prediction by kriging. Given a maximum acceptable kriging variance we can find a grid-
spacing that achieves this. Since then the idea of optimizing spatial sampling to achieve objectives
couched in terms of the precision of predictions or estimates has become a commonplace. The
use of spatial simulated annealing has allowed sampling schemes to be optimized with respect to
various objective functions so that, for example, the overall prediction error variance, expressed as
a combination of the kriging variance (which depends on spatial coverage) and variance parameter
error (which requires some spatial clustering of sample points), can be optimized within irregular
regions, perhaps in the presence of existing data or exhaustive covariates.

In practice we may be interested in more than one objective function. In addition to achieving
acceptable precision of estimates or predictions we may also want to have an acceptable proba-
bility of detecting plumes of a pollutant. Other secondary objective functions may include the
total distance to be covered visiting the sample points or the technical properties of a wireless
sensor network (such as the number of alternative routes for data transmission, or the total power
consumption). A simple approach to this problem is to optimize a weighted combination of all
objective functions. However, the weights in such a case are generally arbitrary, and it is known
that not all good solutions are accessible this way.

In this paper I shall present an approach to multiple objective optimization which searches for
design solutions on a Pareto front. These solutions are mutually non-dominating, which means
that no one solution is better than any other on all criteria. The Pareto front consists of a set
of solutions which are Pareto efficient, in this sense, and from which an acceptable subset can be
selected. The challenge is to find the front. I shall show how this can be achieved using a version of
simulated annealing, the AMOSA algorithm, which has been used for various tasks in optimization
including the design of electronic components.

keywords: *Sampling*, *Optimization*, *Multiple objectives*
# Past, present and future of mathematical methods in pedometrics

Murray Lark – British Geological Survey, England

Pedometrics as a science is concerned to understand the variation of the soil in space and time in quantitative terms. This science underpins the provision of meaningful predictions of soil properties at a location and support in space and time, which is required for management or policy decisions. In order to count as meaningful a prediction should have some non-arbitrary measure of its uncertainty. In both senses pedometrics is underpinned by mathematics, most commonly statistical sampling theory or the theory of random functions. Sometimes, when these approaches seem inadequate, pedometricians turn to computation with little or no mathematical underpinning. In my view this is a mistake, but it may be a fruitful one if we can identify the shortcomings of current methodology and then engage in informed speculation about what established or emerging mathematical apparatus might help us to make progress. That, after all, is how Beckett, Webster, Burrough and others developed pedometrics out of soil survey in the period from around 1965 to 1980. In the course of this review I shall look back, examining how ideas from stochastic geometry, information theory, spectral analysis and state-space modelling have contributed to pedometrics. I shall ask why we do things the way we (usually) do, and what problems this may cause (whether we spot them or not). I shall then make some suggestions about where new terrain for further pedometrical cultivation might be found in the mathematical landscape.
Mapping root depth soil water in sub-Saharan Africa

Johan G.B. Leenaars - ISRIC - World Soil Information, Netherlands

Lieven Claessens - ICRISAT; Soil Geography and Landscape Group, Wageningen University and Research, Kenya

Gerard B.M. Heuvelink - ISRIC - World Soil Information; Soil Geography and Landscape Group, Wageningen University and Research, Netherlands

Tom Hengl - ISRIC - World Soil Information, Netherlands

Maria Ruipérez Gonzalez - ISRIC - World Soil Information, Netherlands

Lenny G.J. van Bussel - Wageningen University and Research, Netherlands

Nicolas Guilpart - UMR 211 Agronomie, AgroParisTech; University of Nebraska-Lincoln, France

Haishun Yang - Department of Agronomy and Horticulture, University of Nebraska-Lincoln, USA

Kenneth G. Cassman - Department of Agronomy and Horticulture, University of Nebraska-Lincoln, USA

Soil root zone plant-available water holding capacity (RZ-PAWHC) is one of the most sensitive soil parameters determining crop growth. This study produced the first map of the rootable depth and the RZ-PAWHC of sub-Saharan Africa (SSA). First, geo-referenced data for 28,000 soil profiles were used as input for digital soil mapping techniques to produce soil property maps of SSA (sand, silt, clay, bulk density, organic carbon, cation exchange capacity, exchangeable aluminium and sodium, pH-H O, electric conductivity, coarse fragments, drainage class and depth to bedrock). The variance explained R2 varied between 52% and 77% for these basic soil properties but was critically low (<30%) for coarse fragments content and drainage class. Next, these maps were used as input data to (pedotransfer) functions, rules and criteria, which we developed and parameterised based on literature review, to produce derived maps of soil water retention at field capacity and wilting point, the soil fine earth fraction from coarse fragments content and, for maize, the soil rootable depth. Mapped versus observed water retention had an R² of 72% and a RMSE of 0.10 cm³/cm³. The map of rootable depth could not be adequately validated due to lack of adequate data on rootability. Results were aggregated to a RZ-PAWHC map of SSA with a spatial resolution of 1 km². RZ-PAWHC ranges between 0 and 235 mm with an average of 74 mm. In soils with unrestricted rootability to 150 cm (maximal rooting depth of maize), the average effective PAWHC is 113 mm and, excluding coarse fragments, 137 mm. The total soil volume of SSA potentially rootable for maize (approximately 30,000 km³) was reduced by one-third (10,500 km³) due to soil conditions restricting root zone depth, of which 4,800 km³ are due to inadequate aeration and 2,500 km³ due to sodicity. The overall assessment of RZ-PAWHC could not be adequately validated but the accuracy seems most limited by the accuracy of the soil property maps from which the maps of RZ-PAWHC and especially rootable depth were derived.

keywords: soil depth, root depth, soil water, soil map, Africa
Building a national (german) mid infrared database for soils

Matthias Leenen – University of Bonn, INRES, Soil Science and Soil Ecology, Germany

Stefan Pätzold – University of Bonn, INRES, Soil Science and Soil Ecology, Germany

Tobias Heggemann – University of Bonn, INRES, Soil Science and Soil Ecology, Germany

Oihane Fernandez-Ugalde – European Commission, Joint Research Centre (JRC), Directorate D -
Sustainable Resources, Italy

Gergely Toth – European Commission, Joint Research Centre (JRC), Directorate D - Sustainable Resources, Italy

Gerhard Welp – University of Bonn, INRES, Soil Science and Soil Ecology, Germany

Mid infrared spectroscopy (MIRS) is a lab based analytic technique, which allows to derive a lot of soil parameters at little expense and effort. MIRS is actually a link between very precise conventional lab analyses and often less precise non-invasive sensors which gather data directly in the field (e.g. EMI, Vis-NIRS). Since MIR spectra only deliver proxy information on interesting soil parameters, calibrations are required. They provide statistic relations between the results of conventional lab analyses and MIR spectra (e.g. via PLSR). As a rule, these calibrations are only locally valid; this hampers a more broad use of MIRS in soil analysis. Within our BonaRes- I4S study, our aim is to minimize this drawback by setting up a national MIR database. This primarily means to contact various soil archives and to collect thousands of soil samples which already have been analyzed by conventional lab methods. We will record MIR spectra of these samples. Our hypotheses are that a broad and comprehensive spectral database may allow two things: (A) Robust, multivariate statistics make it possible to construct calibration models being supraregionally valid. (B) For new soil samples to be analyzed, their MIR spectra may be used, to select the most similar spectra in the database. These spectra are then used to build a quasi-local calibration model. A dataset of about 1000 soil samples from the LUCAS archive of the Joint Research Center in Ispra (Italy) is used to demonstrate that supraregional calibration models are possible for diverse soil characteristics.

keywords: proximal soil sensing, MIRS, topsoil quality
# Have extractable phosphorus and potassium contents evolved in French agricultural soils since 2004?

Blandine Lemercier - AGROCAMPUS OUEST, France

Nicolas Saby - INRA InfoSol, Orléans, France

Laetitia Gouny - INRA InfoSol, Orléans, France

Philippe Eveillard - UNIFA, France

Pascal Denoroy - INRA UMR ISPA, Bordeaux, France

In the last 25 years, amounts of phosphorus and potassium brought to soils through mineral fertilisers have decreased by 70% in France, without organic compensation. This situation conduced to a reduction of nutrients balances. The aim of this study was to estimate if the decreased fertilisation could be linked with a significant decrease of extractable P and K contents in soil. The French National Soil Test (FNST) database was used to study this hypothesis. It gathers results of physico-chemical analysis performed on agricultural topsoils samples, at farmers' request. The spatial and temporal sampling strategy were not controlled. The sampling year is known and the localisation is given by the municipality of origin of the sample. Two periods were compared: 1990-2004 and 2005-2014. A total of 2,516,743 and 2,161,811 soil test results for K₂O and P₂O₅ respectively were available in the FNST database, equitably allocated between the two periods. Analytical results were grouped within administrative spatial entities (cantons - groups of municipalities; small agricultural regions - 713 units in France), and statistical estimator of nutrients contents were calculated after a resampling procedure (Monte Carlo approach) aiming at limiting sampling biases. Two indicators were studied: the evolution of extracible nutrients contents and the evolution of the agronomic diagnosis performed with the software RegiFert. The agronomic diagnosis evaluates the necessity of fertilisation to reach the potential yield considering the crop, the soil nutrient status and other soil characteristics. Results are provided according to 3 diagnosis classes: low, intermediate and high fertility. Evolution of contents was assessed using the non-parametric Mann-Whitney test, and evolution of diagnosis was assessed using the adequation ² test with a limit p-value of 0.1, combined with expert rules on the evolution of the count of results into each fertility class between the two periods. Extractable P contents exhibit a clear decrease: P content decreased by more than 10% in 42.2% of the total cultivated area (TCA), with contrasted regional situations according to intensity of the animal production. The evolution of extractable K content was very low: it decreased for only 4.5% of the TCA. Evolution of agronomic diagnosis is very narrow, meaning that evolution in nutrients contents was too limited to significantly impact fertilisation recommendations. However, the overall decline in nutrients availability in soils encourage vigilance and soil fertility monitoring through regular soil testing.

**keywords:** Soil monitoring, extractable nutrients, soil tests, National scale
# Modelling the electrical conductivity of soil in the Yangtze delta in three dimensions

Hongyi Li - Jiangxi University of Finance and Economics, China

Benjamin P. Marchant - British Geological Survey, United Kingdom

Richard Webster - Rothamsted Research, United Kingdom

Zhou Shi - Zhejiang University, China

Numerous processes, past and present, have given rise to lateral and vertical variation in the soil and to its individual properties such as its salinity and electrical conductivity. The resulting patterns of variation are complex and appear to comprise both random and deterministic components. The latter dominates vertically as trendsin most soil profiles, and in the situation we describe it is prominent in the horizontal plane, too. Describing this variation requires flexible choice of covariance function. The processes of model estimation and prediction by kriging in three dimensions are similar to those in two dimensions. The extra complexity of the three-dimensional variation requires practitioners to appreciate fully the assumptions that their choices of modelimply and to establish ways of testing the validity of these assumptions. We have examined several covariance functions more commonly used to describe simultaneously variation in space and time and adapted them to model three-dimensional variation in soil. We have applied these covariance functions to model the variation in salinity in reclaimed land in the Yangtze delta of China where the apparent electrical conductivity (ECa) has been measured at numerous points down to 1.1 m. The models take into account random and deterministic components in both the horizontal and vertical dimensions. The most suitable mixed model was then used to krigethe ECa on a fine grid from which three-dimensional diagrams of the salinity are displayed.

**keywords:** Saline soil, Electrical conductivity, Three-dimensional variation, Space-time covariance functions, REML, E-BLUP
Sensing of soil organic carbon with portable spectrometers

Shuo Li - CSIRO Land & Water, Australia

Craig Lobsey - University of Southern Queensland, Australia

Songchao Chen - INRA InfoSol Unit & UMR SAS, INRA, AGROCAMPUS OUEST, France

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

Raphael Viscarra Rossel - CSIRO Land & Water, Australia

The development of portable vis-NIR (vis-NIRp) and mid-infrared (mid-IR) spectrometers is enabling the rapid, real-time and accurate proximal sensing of soil properties. However, before using these techniques in field applications, we need to assess their performance under laboratory conditions. Here we assessed two portable spectrometers: a mid-IR (mid-IRp) and a vis-NIR (vis-NIRp) for the determination of soil organic carbon (SOC) concentrations. We also compared their predictions to those made using the bench-top mid-IR (mid-IRb) spectrometer. Our aims were to determine the accuracy and uncertainty of scanning mid-IR and vis-NIR spectroscopy to estimate the SOC content of soil to a depth of 1 m using portable spectrometers.

For our experiments, we used 578 soil specimens systematically sub-sampled from four depths of each of 150 soil cores sampled to a depth of one-meter from a 600 ha farm in northern New South Wales, Australia. The sub-samples were ground dried, crushed and ground to particle sizes of 2 mm and 0.5 mm for our analysis. We developed predictive models using the machine learning algorithm Cubist and used 50 bootstraps to derive robust predictions and assess the uncertainty of our models.

As might be expected, the prediction of SOC with the mid-IRb (R2 ≤ 0.96, RMSE ≤ 0.17) on 0.5 mm particle size outperformed predictions made with spectra of mid-IRp (R2 ≤ 0.91, RMSE ≤ 0.26) and vis-NIRp (R2 ≤ 0.88, RMSE ≤ 0.30). Predictions made using mid-IRp and 0.5 mm particle size were more accurate than those made with vis-NIRp, but their uncertainties were larger. There is no clear difference found from the prediction results for mid-IRp (R2 ≤ 0.84, RMSE ≤ 0.35) and vis-NIRp (R2 ≤ 0.81, RMSE ≤ 0.38) on 2 mm particle size.

We conclude that the both potable mid-IRp and vis-NIRp spectrometers performed well to estimate SOC under laboratory conditions. Particle size has more impact than spectral ranges on the prediction of SOC concentrations. Though better prediction accuracy can be obtained using finer particle size, preparation requires more effort. The accuracy of predictions using samples ground to 2 mm were sufficiently accurate and would enable many more sample measurements, which can benefit the spatial characterisation of soil organic C.

keywords: mid-IR, vis-NIR, particle size, soil core, uncertainty
# How does particle size, water and excitation time affect proximal soil sensing by X-ray fluorescence?

Shuo Li - CSIRO Land & Water, Australia

Catherine Power - Environment & Community Officer, Glencore Coal, Australia

Thomas Bishop - Faculty of Agriculture and Environment, The University of Sydney, Australia

Raphael Viscarra Rossel - CSIRO Land & Water, Australia

Portable XRF technology has gained acceptance as an analytical approach for the estimation of soil elemental and chemical properties. The technique is relatively easy-to-use, rapid real-time and non-destructive. However, there have been few studies that have assessed the XRF technique under different conditions. Thus, our aims here was to design an experiment to assess the effects that different particle sizes, water contents and excitation times have on the XRF spectra of four different soils with different clay contents from four intact soil cores.

We sub-sampled each of them into 5 replications and used a 3-way factorial design, with three particle sizes ( 1 mm, 2 mm and natural aggregates), three gravimetric water contents (air-dry, 10% and 20%), and three excitation times (15, 30 and 60 seconds). X-ray spectra were recorded using a portable XRF spectrometer to give 540 spectra in all. Six (Si, K, Ca, Ti, Fe and Cu) of characteristic elemental peaks were identified for analysis. We used an analysis of variance (ANOVA) to identify significant differences between our factors and using the peak intensity and peak area of six elemental peaks as our response variable.

As might be expected, longer excitation times generally improved detection limits. There was a strong linear relationship between intensity of XRF spectra and measurement times. The results from the ANOVA showed that, for Si, all treatments were significantly different for both mean peak intensity and area. For K, Ca, Fe and Cu there was no significant difference in mean peak intensity and area between 2 mm particle size and the soil aggregate. However, peak height and area for Ti were significantly different. There were significantly difference in peak intensity and area between the 1 mm particle size and the soil aggregate, except in the case of Cu. Similarly no significant difference for moisture was found between air-dry and 10% moisture content except in the case of Cu for peak area. When the soil wetter, for Ti, K, Ca, Ti and Fe there were significant difference in peak intensity and area between air-dry and 20% moisture content, except Cu in terms of peak intensity.

Our results suggested that measurement time has the biggest effect on signal intensity, while particle size and moisture are secondary effects. However, signal intensity was better for drier soil with smaller particle sizes. Thus, all of these factors will affect the accuracy of proximal soil sensing with portable XRF.

**keywords: portable, XRF, ANOVA**
Fine spatial resolution mapping of soil organic carbon in China

Zongzheng Liang - Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, China

Yuanyuan Yang - Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, China

Songchao Chen - INRA InfoSol Unit & UMR SAS, INRA, AGROCAMPUS OUEST, China

Ruiying Zhao - Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, China

Raphael Viscarra Rossel - Land and Water Flagship, CSIRO, Australia

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

Accurate digital soil maps of soil organic carbon (SOC) are needed to evaluate soil fertility, estimate soil organic carbon stocks, and for ecological and environment modeling. We used 5982 soil profiles collected during the second national soil survey of China with 19 soil landscape-environment predictors to derive a spatial model of the concentration of SOC in the 0-20 cm. The environmental predictors relate to the soil forming factors and include elevation, vegetation and climate. We developed the model using the machine learning algorithm Cubist combined with the non-parametric bootstrap to derive estimates of model uncertainty. We optimized the Cubist model using a 10-fold cross validation and the best model used 17 rules, 5 committees and 5 neighbors. The cross validation correlation coefficient between the observed and predicted values was 0.62 and the relative error was 0.72 g/kg. We then generalized the model over China and mapped the SOC distribution at 90 m resolution. The predicted spatial distribution of SOC over China shows larger values area in the eastern Tibetan Plateau, the north of Heilongjiang province, the northeast of Mongolia, and the small part of the Tianshan Moutain in Xinjiang. The small concentrations of SOC occur in the Loess Plateau and most of the desert areas in Northwest China. Results show that the average SOC content in surface soil is 24.82 g/kg. Our work provides an up-to-date map that can decision-making in the formulation of policy for carbon offset schemes. The map will also help the design of future soil surveys and the development of monitoring networks in China.

keywords: soil carbon, digital soil mapping, big data mining, uncertainty, China
The anatomy of errors from soil property measurements and predictions (soil pH example)

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

Jim Thompson - West Virginia University, USA

Skye Wills - United States Department of Agriculture, Natural Resources Conservation Services, USA

Phillip Owens - United States Department of Agriculture, Agriculture Research Service, USA

Dylan Beaudette – United States Department of Agriculture, Natural Resources Conservation Services, USA

Steve Peaslee – Agriculture, Natural Resources Conservation Services, USA

Kabindra Adhikari - USDA-ARS, USA

David Lindbo – United States Department of Agriculture, Natural Resources Conservation Service, USA

Spatial predictions of soil properties are performed in digital soil mapping and error predictions are assessed against measured values when available. Rarely the errors associated with measurements, pedotransfer functions and database transections are directly assessed in error spatial predictions. Because of discrepancies between soil measurements and spatial predictions, one to one comparisons for error assessments are difficult and impractical, especially in the context of large databases. Thus, various data generalizations are used, which could also affect the spatial error predictions. The objective of this study was to assess the soil pH errors associated with the use of: (i) different measurement methods; (ii) pedotransfer functions and (iii) database transections. Data from the International Soil Exchange protocol were used to assess the accuracy of measured soil pH by various laboratories. Pedotransfer functions to convert soil pH 1:1 water to soil pH 1:5 water and equal-area spline functions and soil horizon weighted means were used and compared for the GlobalSoilMap standard soil thickness (0-5, 5-15, 15-30, 30-60, 60-100; and 100-200 cm). The accuracy of soil pH 1:1 water measured by various laboratories across the world on same soil samples was 0.7 pH, while the accuracy within laboratories varied between 0.2 and 0.3 pH. The use of a simple linear regression equation to predict soil pH 1:2 water from pH 1:5 water had a root mean square error (RMSE) of 0.44 pH. The errors between spline function and weighted means for soil pH 1:5 water for the STATSGO database varied from 0.2 to 0.4 pH between standard depths, but only from 0.1 to 0.2 for the SSURGO database. The errors of splined soil pH 1:5 water between SSURGO and STATSGO for all standards depths varied between 0.5 and 0.7 pH. However, the errors of splined soil pH 1:5 water between measured data (KSSL) and STATSGO and SSURGO were greater and varied between 0.7 and 0.8 pH. An ideal comparison to assess the impact of different measurement methods, pedotransfer functions and database transections would require that spatial predictions and measured values be made on the same scale, from the same area and on the same samples. Given that such comparisons are the exception and not the norm, the quantification of the errors that result from differences in measurement methods, pedotransfer functions and database transections are a good indicator of the accuracy predictions to be considered for informing management decisions.

keywords: error predictions, pedotransfer, soil pH
Can conditioned latin hypercube sampling capture pedodiversity?

Mareike Ließ – Helmholtz Centre for Environmental Research - UFZ, Germany

Conditioned latin hypercube sampling (clhs) has advanced to be a standard sampling approach for statistical soil-landscape modelling, also known as digital soil mapping. Latin hypercube sampling (lhs) was developed to sample a number of n parameters in such a way that the density function of each of the n parameters is well represented by the selected k samples. This is achieved by subdividing the density function of each parameter into k quantiles and selecting one sample per quantile. Finally, the so obtained k values for each parameter are combined with those of the other parameters, either randomly or in some predefined way. However, if these parameters represent soil forming factors within the reality of an actual landscape or their respective proxies from satellite images, digital elevation models, geophysical sensors etc., then the selection of a certain landscape position for sampling and, hence, the selection of a certain parameter value in one of the parameter quantiles, automatically results in the selection of values in one of the quantiles of the other parameters as well. As a consequence, the selection of one sample per quantile needs to involve an optimisation approach as implemented in clhs. Meanwhile, several adaptations were made to overcome practical difficulties that might arise during the actual sampling campaign in the field, other problems remain widely unnoticed: (1) The values which fall into the various quantiles of a certain parameter do not have the same probability of being selected as was the case inlhs, a flaw which is even enhanced while more parameters are considered and/ or more points are selected. And (2) as a consequence to the optimisation process, the selected points are not independent from one another. Finally, (3) due to the selection of one particular parameter combination, important other landscape positions cannot be sampled and will neither be present in the dataset, nor in the thereby obtained statistical soil-landscape model. So the power of clhs to capture the heterogeneity and, hence, the pedodiversity of a landscape is very limited as it will always result in a too simplified version of the reality within the selected samples, and will, therefore, most probably lead to biased models. Stratified random sampling methods, which sample landscape units formed by e.g. methods of pattern recognition, are more suitable for this purpose.

keywords: conditioned latin hypercube sampling, digital soil mapping, sampling design
Spatial variations and controlling factors for estimation of soil
organic carbon stocks in three rural soil areas of Taoyuan, Taiwan

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

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

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

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

The spatial variations of soil organic carbon (SOC) stocks and controlling factors in mainly
agricultural area of Taoyuan, northwestern Taiwan, was less concerned in last decade. In this
study, dataset for estimating the SOC stock in agricultural soils was obtained from a detail soil
survey by Taiwan Agricultural Research Institute (TARI) recently. The databases have all the
SOC and few soil bulk densities of three townships of Taoyuan, near 27,559 ha, with different land
uses. Near 4410 soil profiles were sampled in every 250 meters by 250 meters by soil auger from
surface to 150 cm depth. The bulk densities were estimated by pedotransfer function for arable
soils in Taiwan. SOC contents and SOC stocks in 3 soil depths (0-15, 15-30, and 30-60 cm depth)
were interpolated by ordinary kinging methods with GS+ and ArcGIS. Results showed that the
average SOC contents (g/kg) in 3 soil depths were 20.4 g/kg, 15.8 g/kg, and 12.9 g/kg, respectively.
The distribution of SOC contents decreased with increasing the soil depth. The SOC stock (mean
± standard deviation) was 29.3 ± 7.03 ton/ha for 0-15 cm depth, 24.0 ± 6.10 ton/ha for 15-30 cm
depth, 40.6 ± 11.3 ton/ha for 30-60 cm depth and 93.9 ± 23.9 ton/ha2 for 0-60 cm depth (whole
soil layer). The average SOC stocks in 0-15 cm and 0-30 cm are higher than those of arable land
of Taiwan in previous studies. The total SOC stock of 3 townships areas was 0.81 Mt (megatons)
in 0-15 cm depth, 1.47 Mt in 0-30 cm depth and 2.59 Mt in 0-60 cm depth, respectively. The
spatial distribution of SOC stocks decreased with decreasing the elevation, where the higher ones
were mainly distributed in the eastern part close to the hill land area, and the lower ones were
mainly distributed in the western part along the seashores and the rivers. The spatial variations of
SOC contents and SOC stocks were the same. Additionally, the effects of elevation, aspect, slope,
curvature, drainage, terrain ruggedness index (TRI), terrain wetness index (TWI), and normalized
difference vegetation index (NDVI) on SOC content and SOC stock can be evaluated by GLMM
model with SAS. Analysis results indicated that the elevation (p< 0.0001), NDVI (p≤ 0.0002),
drainage (p≤ 0.0005), aspect (p≤ 0.001) and TWI (p≤ 0.0158) are the mainly controlling factors
on the spatial distribution of SOC content and SOC stocks in 3 townships of Taoyuan, Taiwan.

keywords: SOC contents, SOC stocks, ordinary kinging, spatial variations, ontrolling factors,
rural soils
Secondary soil information extraction from 3D soil mapping prod-
ucts

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

Xiao-Dong Song – Institute of Soil Science, Chinese Academy of Sciences, China

Gan-Lin Zhang – Institute of Soil Science, Chinese Academy of Sciences, China

Yu-Guo Zhao – Institute of Soil Science, Chinese Academy of Sciences, China

De-Cheng Li – Institute of Soil Science, Chinese Academy of Sciences, China

Three-dimensional (3D) digital soil mapping can produce a complete soil property distribution. However, even with the complete distribution, important soil information such as the patterns of depth and thickness of soil diagnostic or impeding layers are still seriously lacking, which limits the application of 3D soil mapping results for eco-hydrological modeling and land management. In this study, we developed a method for extracting the presence/absence, depth and thickness of specific soil layers from 3D soil mapping results. The approach was demonstrated in the Heihe river basin, northwestern China. We predicted 3D continuous distribution of soil CaCO3 content using its relationship with climate, parent material, terrain, and vegetation conditions. Based on the predicted 3D soil data, we extracted key points of vertical variation at each pixel location and then divided the profile of this location into different layers. For each layer, we identified the presence/absence, depth and thickness of soil calcic layers. The results showed that the spatial patterns of the depth and thickness of calcic layers can be accurately extracted. It was found that the depth of calcic horizons becomes shallower with the increase of aridness while its thickness exhibits an opposite pattern. We concluded that the proposed method is effective for extracting secondary soil information from 3D soil mapping results.

keywords: 3D digital soil mapping, secondary soil information extraction, soil calcium carbonate, calcic horizons
# Estimation of surface soil moisture from remotely sensed data using random forests

Ivan Alberto Lizarazo Salcedo - Universidad Nacional de Colombia, Colombia

Accurate information about the concentration of soil moisture in the top 5 cm soil layer is very important in many applications, such as drought prediction, watershed management, irrigation scheduling, precision farming, climate change analysis and meteorology. Soil moisture products acquired from passive satellite missions have been widely applied in environmental studies. However, a primary limitation for using such products for local scale assessment is their low spatial resolution (e.g. 9 km for the NASA's Soil Moisture Active Passive - SMAP - Level 4 product). Thus, estimation of soil moisture with higher spatial resolution is needed for many applications. In this study, the random forest (RF) algorithm is used to estimate surface soil moisture (SSM) over a region representative of grasslands and agricultural landscapes, located in the east of the Colombian territory, for a rainy season in 2015. The main body of the paper describes application of the RF algorithm to construct a regression model from variables based on remotely sensed datasets: (i) downscaled gridded precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS); (ii) the normalized difference water index (NDWI), (iii) the normalized burn ratio thermal index (NBRT) from Landsat; (iv) the enhanced vegetation index (EVI) from Landsat; and (v) topographic attributes derived from the Shuttle Radar Topographic Mission (SRTM) digital elevation database. Soil moisture field measurements collected in 2009-2011 were used to train and test two RF models, one for the wet season and the other one for the dry season. Then, the RF regression model is used to estimate SSM over the study area at 30-m spatial resolution in June 2015. Accuracy of the estimation was assessed using the root mean square difference (RMSD) and the mean absolute difference (MAD) between the predicted soil moisture and a reference dataset obtained from a 3-km SMAP Level 3 product. Results demonstrate that, while the RF model is able to accurately estimate surface soil moisture for the wet season, its estimation is less accurate for the dry season. The paper examines what are the most informative variables in the RF models and discusses whether topographic and multispectral indices, based on SRTM and Landsat datasets, could replace precipitation data for surface soil surface estimation. It concludes stating advantages and limitations of using remotely sensed data for surface soil moisture estimation at local scales. Interested readers can find details on data, methods and results at https://goo.gl/IL2utQ and https://goo.gl/5hLwjU.

**keywords: soil moisture, remote sensing, random forests, SMAP**
Can large spectral libraries improve local calibrations of soil or-
ganic carbon?

Craig Lobsey - University of Southern Queensland, Australia

Raphael Viscarra Rossel - CSIRO, Australia

Diffuse reflectance spectroscopy in the visible and near infrared (vis-NIR) can measure soil properties, such as organic (C). Compared to conventional laboratory methods, it enables practical and inexpensive measurements at finer spatial and temporal resolutions, which are needed to improve the assessment and management of the environment. However, the accuracy of the measurements, and the economic viability of the technique depends on the costs associated with the development of spectral libraries for calibration. Soil spectral libraries (SSL) developed at the regional, continental and global scales, have demonstrated the usefulness of vis-NIR for soil analyses. Calibrations derived using these libraries, however, are often shown to predict poorly at local scales. Here we present a new method - RS-LOCAL - that uses re-sampling and a small representative set of local samples to 'tune' large spectral libraries so to improve calibrations at local scales. We demonstrate the implementation of RS-LOCAL by estimating soil organic C at two sites with very different soil types in Australia and New Zealand. We found that with as few as 12 to 20 local samples and a large SSL RS-LOCAL could accurately predict soil organic C, and that it outperformed all other local techniques that we tested. Thus, RS-LOCAL can reduce analytical cost and improve the economic viability of soil spectroscopy.

keywords: spectroscopy, visible near infrared, soil organic carbon, local calibration
Predicting the spatial pattern of soil particle-size distribution using a pedogenesis model

Yuxin Ma – The University of Sydney, Australia

Budiman Minasny – The University of Sydney, Australia

Welivitiyage Don Dimuth Prasad Welivitiya – The University of Newcastle, Australia

Brendan Malone – The University of Sydney, Australia

Garry Willgoose – The University of Newcastle, Australia

Alex McBratney – The University of Sydney, Australia

Particle-size distribution (PSD) plays an important role in influencing a number of soil physical, chemical, and biological properties such as bulk density, hydraulic conductivity, water holding capacity, erodibility, carbon storage and many others. This paper aims to predict the spatial pattern and evolution of soil texture (sand, silt and clay), the most common descriptor of PSD, at a catchment located in Hunter Valley, NSW, Australia, using a mechanistic pedogenesis model called SSSPAM.

The SSSPAM model simulates spatial and temporal variation of soil texture within a soil profile based on erosion due to overland flow and physical weathering within the profile. Using an existing 30m DEM as input, the model simulated soil evolution over 70,000 years with particle size output every 100 years. We ran the model over this period to ensure that the particle-size distribution had reached a steady-state condition, typically before 100,000 years. To validate and analyse the influence of different process parameters and dynamics of particle size, the model was calibrated using a digital soil map and observed soil texture for some sites within the study area.

We found a strong relationship between runoff excess generation, exponential weathering rate and the soil particle-size distribution. Lower discharge produced finer particles. Higher exponential weathering rates broke down the large particles more rapidly and allowed finer particles to be retained. Increasing the erodibility factor did not change the results dramatically. Other parameters like weathering fragmentation geometries, depth-dependent weathering (humped exponential and dynamic reversed exponential) functions and the exponents of the equations will be further studies in the subsequent paper.

The model is able to explore the soil particle-size distribution and how the particle size will change only considering physical weathering mechanism and erosion from overland flow. We will discuss how such soil formation model combined with the empirical spatial data can be used to predict the variation of soil properties accurately together with associated uncertainty analysis.

keywords: Particle Size Distribution, Spatial Pattern, Pedogenesis Model
Soil Properties Prediction Using Diffuse Reflectance Spectroscopy,
Attenuated Total Reflectance Spectroscopy and Fourier Trans-
form Infrared Photoacoustic Spectroscopy

Fei Ma - Institute of Soil Science, Chinese Academy of Sciences, China
Changwen Du - Institute of Soil Science, Chinese Academy of Sciences, China
Jianming Zhou - Institute of Soil Science, Chinese Academy of Sciences, China

The approach of spectral analysis is one of proximal sensing methods in soil science such as
soil management and precision agriculture. Soil properties can be measured and revealed as a
spectral curve according to spectra scans when deal with numerous soil samples in a rapid and
non-pollution method. There is continuous and growing interest in mid-infrared ranges for soil
properties characterization.

As the sensitive, efficient and accurate techniques in soil science, the diffuse reflectance spec-
troscopy, the attenuated total reflectance spectroscopy and Fourier transform infrared photoacous-
tic spectroscopy have their own advantages on soil identification and soil quantitative analysis. In
this article, we selected 1456 paddy soil samples in in eastern of China and collected diffuse re-
reflectance spectroscopy, the attenuated total reflectance spectroscopy and Fourier transform infrared
photoacoustic spectroscopy of the samples, respectively. The purpose of the study was to using
the complementarity of three techniques to develop spectral library for quantitative assessment of
soil nutrients such as pH, SOM, N and P for survey and management. A self-adaptive model was
constructed based on PLS model for soil identification and prediction. The results showed that at-
tenuated total reflectance spectra can provide more accurate prediction in pH and P values, and the
diffuse reflectance spectroscopy can get good results in SOM and N values. Moreover, the Fourier
transform infrared photoacoustic spectroscopy can provide more functional groups information. It
might provide a supplemental method for use in soil management.

keywords: Diffuse Reflectance Spectroscopy, Attenuated Total Reflectance Spectroscopy, Fourier
Transform Infrared Photoacousticspectroscopy, Soil Properties Prediction, A self-adaptive model
Managing quantifiable uncertainty for digital land suitability as-
sessments

Brendan Malone – University of Sydney, Australia

Darren Kidd – Tasmanian Department of Primary Industries, Parks, Water and Environment,
Australia

Budiman Minasny – The University of Sydney, Australia

Alex McBratney – The University of Sydney, Australia

We are seeing an increasing activity in land resource assessments (LSA) at varying spatial scales throughout the world. These efforts have been largely assisted by suites of digital information technologies such as digital soil and climate modeling and mapping, and associated environmental resource mapping such as remote sensing data and digital elevation models. A common scenario however is that these digital LSAs are made with the assumption that the input information is error free. Similarly, the criteria with which assessments are made are often discretely defined.

In this paper we briefly review the status quo of digital land suitability analysis. The study then focuses on Tasmania, Australia where there has been the integration within Government policy and directives, a fully digital LSA system for a number of specific agricultural enterprises. Here we address two issues that present limitations to current LSAs. First we take into account the quantified uncertainties of the input variables. Secondly, we adopt membership functions rather than discrete threshold values for the assessment criteria.

For taking into account the input variable uncertainties, simulations are used to generate plausi-
ble realisations of soil and climatic variables for input into an LSA. It is found that when comparing
to a LSA that assumes inputs to be error free, there is a significant difference in the assessment
of suitability. Using an approach that assumes inputs to be error free, 56% of the selected study
area was predicted to be suitable for hazelnuts (our selected agricultural enterprise). Using the
simulation approach it is revealed that there is considerable uncertainty about the ‘error free’
assessment, where a prediction of ‘unsuitable’ was made 66% of the time (on average) at each
grid cell of the study area. In the specific case of our study area, the cause of this difference is
that digital soil mapping of both soil pH and conductivity have a high quantified uncertainty in
this study area. The use of parameter membership functions and or adopting expert rules around
soil properties that can be easily managed, we find penalties for taking into account uncertainties
are not as severe. Despite differences between the comparative methods, taking account of the
prediction uncertainties provide a realistic appraisal of enterprise suitability.

keywords: Digital soil assessment, Quantifying Uncertainty, Land Suitability Analysis, Agriculture
Useful applications of conditioned Latin hypercube sample for digital soil mapping

Brendan Malone – The University of Sydney, Australia

Colby Brungard – New Mexico State University, USA

Budiman Minasny – The University of Sydney, Australia

Alex McBratney – The University of Sydney, Australia

The conditioned Latin hypercube sampling strategy (cLHS; Minasny and McBratney 2006) provides the necessary facility of efficiently sampling variables from their multivariate distributions. It is particularly applicable for digital soil mapping projects, where within a given target area one wants to derive a more-or-less optimal sampling configuration given prior information in terms of exhaustive ancillary data such as from digital elevation models, remote sensing data etc. cLHS basically ensures biases in the decision-making of a soil surveyor are removed during a soil sampling campaign, and provides the means for actually visiting all areas of the target area landscape in the most efficient way possible.

Some questions that may arise during the planning and implementation of cLHS for digital
mapping purposes are:
1) How many samples should be collected?
2) How does one further
improve the efficiency of cLHS by taking into account prior sampling locations within the target
area?
3) How can one generalize cLHS in the situations where selected sites cannot be visited?
This paper presents some relatively simple solutions to these questions.

For the first question, we can answer it via an iterative approach observant to the law of dimin-
ishing returns. Here we assess a distance criterion between population and sample for increasing
sample sizes and select the configuration that minimises the work required while also ensuring the
population multivariate distributions are efficiently sampled. For the second question, we evaluate
the data space occupied by the prior sampling points, before implementing a cLHS on the data
space that has low occupation. For the third question, we use either a Gower's similarity index
or fuzzy membership approach to rank alternative sites to visit if an original location needs to be
moved somewhere else.

We illustrate solutions to these questions using combinations of spatial data that are both
continuous and categorical format.

keywords: Soil sampling, Conditioned Latin hypercube, Digital soil mapping, Optimisation
Comparison of Multifractal parameters between binary and grayscale
synthetic images

Juan José Martin-Sotoca - CEIGRAM, ETSIAAB - Grupo de Sistema Complejos, UPM, Spain

Ivan G. Torre - Grupo de Sistemas Complejos, UPM, Spain

Juan Carlos Losada - Grupo de Sistemas Complejos, UPM, Spain

Ana M. Tarquis - Universidad Politécnica de Madrid (UPM), Spain

In soil science image analysis has become increasingly popular during the last decade thanks
to the technological advances, particularly in X-ray computed tomography (CT) to characterize
the spatial distribution of soil pore structures. This is a key factor to obtain different parameters
that will influence in several models related to water flow and/or microbial growth processes. One
important process often used, as one of the first steps in image analysis, is binarization. Actually,
there are several segmentation methods that have been applied to CT soil images. To check the
robustness of these methods, different authors have created different synthetic images.

Recently, Torres et al. (2017) showed the multiscale nature of CT- soil images. Based on the scaling properties of these images, Martín-Sotoca et al. (2017) developed a Truncated Multifractal method (TMM) that is able to construct a 2D synthetic image, used to test the Singularity-CA binarization method. In this method, a multifractal algorithm is used to create an air-filled pore space and a pebble space that simulates the granules of high intensity that typically appear in CT soil images. Therefore, the TMM can simulate the two main characteristics that some CT soil images present: the scaling nature of the gray scale image and the low contrast at the solid/pore interface with non-bimodal greyscale value histograms.

In this work we present the relationships found between the multifractal parameters used to
create the 2D grayscale image and the histogram and multiscale parameters found. In order to
obtain so different intensities of noise were applied in the generating process. A comparison with
real CT- soil images will be showed.

References

Hapca, S.M., Houston, A.N., Otten, W. and Baveye, P.C. New objective segmentation method based on minimizing locally the intra-class variance of grayscale images. Vadose Zone J. 12 (3), 2013.

Martín-Sotoca, J.J., A. Saa-Requejo, J.B. Grau, A.M. Tarquis. New segmentation method based on fractal properties using singularity maps. Geoderma, 287, 40–53, 2017.

Ojeda-Magaña, B., Quintanilla-Domínguez, J., Ruelas, R., Tarquis, A.M., Gómez-Barba, L. and Andina, D. (2014). Identification of pore spaces in 3D CT soil images using PFCM partitional clustering. Geoderma, 217-218, 90-101.

Torre, Iván G., Juan C. Losada and A.M. Tarquis. 2017. Multiscaling properties of soil images. Biosystems Engineering, http://dx.doi.org/10.1016/j.biosystemseng.2016.11.006.

Wang, W., Kravchenko, A. N., Smucker, A. J. M. and Rivers, M. L. (2011). Comparison of image segmentation methods in simulated 2D and 3D microtomographic images of soil aggregates. Geoderma, 162, 231–241.

keywords: CT-scan soil images, synthetic images, multiscale analysis
# Development of a stakeholder-oriented communication strategy for raising acceptance of soil protection measures

Kirstin Marx – Thünen Institute, Germany

Johanna Fick – Thünen Institute of Rural Studies, Germany

Soil functionality is crucial in the production of food, feed or renewable resources as well as for the ecosystems themselves. Over the past decades, the trend to larger machinery increased the pressure on soil functionality. On-farm soil protection measures can promote a higher level of sustainable land use, e.g., weather-dependent field traffic and machinery that prevents compaction damage. Nevertheless, different types of data from different sources (e. g., weather data, machine parameters and specifications, etc.). Also, new ways of decision-making for farmers and decision support for policy-making and good governance are needed in this context and present an ambitious goal.

To reach this goal, SOILAssist analyses important agricultural trade publications in Germany, with a focus on articles about soil protection. First, a frequency analysis reveals the most common topics and the frequency of keywords used. Secondly, there will be results on how stakeholder knowledge can improve by knowledge transfer. Do farmers like to use real-time information on soil humidity, deformation rates and other soil functions? Fortunately, there is access to real-time information on soil, so the transfer of research to on-farm soil management is ensured. Then, the results of a nation-wide farmer survey done in early 2017, show recent data on soil protection knowledge. Bringing together all three aspects, a stakeholder-oriented communication and advice strategies can be initiated. The use of this experience-based and somewhat uncertain, but valuable, knowledge guarantees to be useful for getting a better picture of the acceptance of soil protection measures. Agricultural extension and new governance principles can also profit from easier decision processes on field traffic. In addition, a decision support tool might not only deliver standardized data on soil, but also help to apply sustainable land use practices, and last but not least a web-based knowledge portal.

This abstract refers to the project "SOILAssist" launched in October 2015 as a part of the German research program 'BonaRes – Soils as Sustainable Resource for the Bioeconomy' (funded by the Federal Ministry for Education and Research, BMBF 031A563A).

**keywords:** Sustainable agriculture, soil quality, soil information system, decision support system, soil management, soil compaction, knowledge transfer, governance
# Crossing the bridge between soil ecology and pedometrics at global scale

Jerome Mathieu – UPMC, France

Patrick Lavelle – UPMC, France

Understanding soil functioning is critical for soil and ecosystem management in response to global change. Soil functioning is the result of the interactions between biotic and abiotic component of soils. Understanding soil functioning requires understanding these interactions in the great variation of situations existing on earth. Biotic and abiotic components of soils are typically studied by different fields of science, hampering our understanding of these interactions. One of the challenge is to develop approaches on relevant spatial and temporal scales. Indeed soil science typically focuses on large spatial areas and on long periods of time, while soil ecology typically focuses on processes at the scale of the soil profile and on short time scale. Consequently, soil macrofauna is poorly considered in models of soil formation, and soil context is poorly taken into account in soil ecology. In such context, it is critical to develop studies of soil biota on large areas, with a focus on groups with a strong impact on soil processes, such as soil macrofauna.

The goal of this presentation is twofold. First we will show the key results of the project MACROFAUNA. During this project, we studied the biodiversity and abundance of soil macrofauna at the global scale in more than 2000 sites, with a standardized ISO method: the TSBF protocol. In particular, we will see how large environmental gradients - such as climate - interact with local conditions - such as human activities and soil properties - to shape soil macrofauna biodiversity and abundance. The second objective of this talk is to invite soil scientists to join the next version of the project. We want to make the project more relevant for the understanding of the role of soil macrofauna on soil formation and maintenance. For this we need to ground the project in the existing framework of pedometrics. In particular we need guidelines on how to improve our spatial sampling strategy, and to identify simple measures of soil biota which would be useful as inputs or parameters for models of soil formation.

**keywords:** soil biota, soil biodiversity, global scale, sampling strategy
# Contribution to the study of erosive dynamics in the Ghézala dam watershed (Northern Tunisia)

Majid Mathlouthi - 1Research Laboratory in Sciences and Technology of Water in INAT, Tunisia
Fethi Lebdi - National Agronomic Institute of Tunisia (INAT), Tunisia

In Tunisia, about 25.3 million of m3 of sediment deposited annually in dams reservoirs. Given the irregularity and complexity of erosion-transport-deposition process and for the rational management of water resources and soil, modeling as a study tool and decision support has become necessary. The current situation of the watershed Ghézala dam located in the basin of Lake Ichkeul (Northern Tunisia) shows that the combined action of various factors of the natural environment and human action contributes to the degradation of soils in the watershed. Indeed, the pressure exerted on the soil by plowing as well as overgrazing to meet the needs of the population of this area has exposed the soil to a continued deterioration manifested by different erosive phenomena endangering the only source of revenue for the area. The control of sedimentation in the dam reservoirs provides a global evaluation of the process of erosion and transportation of sediments taking account the bed-material load. The knowledge of sedimentation is also useful for dam management. In Tunisia, little was known about erosion, transportation and sedimentation phenomenon having regard to their consequences. The depth measurements on Ghézala dam reservoirs were carried out in 1993 and 2011. Measurements give, in spite of lack of precision, first information as on sedimentation quantity and variability, so on method of sediment deposits. According the results of measurements for 2 years of the turbidity of the major rivers in the extreme northern Tunisia and watersheds of Lake Ichkeul, the average turbidity of the Ghézala River is 6.56 g/l. This corresponds to an average annual erodibility of 1885 t/km²/yr. It remains however to be done to refine and especially quantify our knowledge of the chain of events erosion-sediment transport, spreading and sedimentation; this study is a contribution to the study of these problems which are increasingly aware of specialists and non-specialists. Using a distributed physical parameters runoff and erosion model throughout the watershed, can improve the understanding of the variability of the factors responsible for soil loss and thus solve the problems related to the conservation soil. Sediment discharge is quite sensitive to hydrologic simulation, as the topography and the rain's intensity affecting the sediment transport capacity and their delivery to river course.

**keywords: water erosion, sedimentation, dam watershed, human action**
Pedometrics Quadrancentennial

Alex McBratney – The University of Sydney, Australia

Jaap de Gruijter – Wageningen University & Research, The Netherlands

Speculations and climaxes

Delphic detonations

A tot of yesteryear

A tad of pedography

A parsec or two

On precociousness

On perspicacity

Scaling the Wageningense Berg

Blown goals and far misses

Dazzling (de)feats

Specular notions

Salted and peppered past

A gorgeous destiny

And perchance an air
Improvements in spatial soil sample design efficiency

Stephen McNeill - Landcare Research New Zealand, New Zealand

Linda Lilburne - Landcare Research New Zealand, New Zealand

Samuel Carrick - Landcare Research New Zealand, New Zealand

Carolyn Hedley - Landcare Research New Zealand, New Zealand

Bryan Stevenson - Landcare Research New Zealand, New Zealand

Paul Mudge - Landcare Research New Zealand, New Zealand

Sharn Hainsworth - Landcare Research New Zealand, New Zealand

The questions asked of the pedometrician are becoming more complicated over time, in response to increasingly intricate and searching questions from end users. Examples include the need for longitudinal models for soil properties, the characterisation of spatial models over the landscape, and monitoring of critical levels of anthropologically-influenced soil properties. While usually underpinned by a functional model, the parameters of the model are ultimately derived from field data, which is gathered from historical surveys, serendipitously-acquired data, and custom-designed field campaigns. This blend of data frequently suffers from a degree of non-representativeness and lack of relevance to the study at hand, but their inclusion is important since the resources required to acquire completely new data for a new study can be very expensive.

In our present work, we need to acquire data to meet the requirements of several broadly related research questions. Historical data provides useful information but will not meet the requirements for the studies in terms of statistical power and spatial coverage. Some criteria (e.g. topographic slope) are common to many projects, while others have more specific requirements. The key research question here is how to achieve the power so that the results are conclusive, while also minimising the field resources required, and simultaneously sharing data and resources.

We use a combination of tools to minimise resource cost and maximise the power of all studies. First, simulation is used for estimation of statistical power, driven by historical soil information. Second, we use a relatively new spatially balanced design method (balanced acceptance sampling) to optimise efficiency and spatial balance. Finally, we use an optimisation model to simultaneously maintain statistical power and minimise resources over several different projects.

There are several practical issues with this approach. First, a gain in the field sampling efficiency depends on the overlap in requirements for different projects; broadly, the more specific a project's requirements, the less likely that sample locations for that project will benefit other projects. Second, achieving field sampling efficiency gains across projects does not guarantee that the statistical power of a given project will be satisfactory; a project might share useful effort from another survey and yet might be under-powered. Despite these limitations, these tools provide an objective way to explore sampling requirements, field effort, and statistical power across several projects to reveal potential benefits for effort and efficiency in field sampling.

keywords: Sampling design, Spatial sampling, Sampling efficiency, Balanced sampling, Sampling optimisation
Digital soil mapping in hilly relief area in Southeastern Brazil

Martin Meier – Federal University of Viçosa, Brazil

Márcio R. Francelino – Federal University of Viçosa, Brazil

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

Eliana de Souza – Federal University of Viçosa, Brazil

Maps of soil are important guiders in land use planning. Governmental organizations dealing with land use repeatedly use conventional mapping method despite the increasing confidence in data obtained from remote sensing and auxiliary data applied to landscape studies. Digital soil mapping (DSM) is also increasingly gaining confidence producing soil maps for land use planning. The main objective of this study was to compare a soil map generated by a digital soil mapping technique with the conventional soil map. The area of study is the Denis Gonçalves Rural Settlement, located in the Zona da Mata Region, State of Minas Gerais, Brazil. The area has a complex relief formed by hilly mountains defined as “Mares de Morros”. We registered 133 points, among soil profiles and observations, where samples were collected for chemical and physical analysis to perform the soil classification and conventional mapping. For DSM we used ArcGIS software’s to process data and R for the classification using variables of generated with Digital Elevation Model, Landsat Imagery, and WorldClim data, in addition to derived covariates. Seven classifiers (i.e. Random Forest, Support Vector Machines with Linear Kernel, Support Vector Machines with Polynomial Kernel, Extreme Gradient Boosting, Ranger Random Forest, Weighted Subspace Random Forest and Bagged AdaBoost) among a pool of 15 classifiers, were chosen for the classification, based on their performance (Kappa index > 0.50). For map comparison, we transformed the conventional map to a raster format and used map algebra. As the conventional map is made of associations of soil classes, the concordance between the DSM and the conventional soil maps considered the agreement of a given pixel in the DSM map with the correspondent soil class associated in map unity of the conventional map, regardless the component of association. The conventional map showed that the soil with greatest occurrence in the area was Dystric Xanthic Ferralsol (DXF), which in association with Dystric Chromic Ferralsol (DCF) and Dystric Haplic Cambisol (DHC) occurs in 35.1% of the area, followed by Dystric Chromic Ferralsol, Dystric Xanthic Ferralsol and Dystric Chromic Acrisols (DCA) with 34.7%, followed by Dysric Xanthic Ferralsol alone with 12.1%, Gleysols (GX) associated with Cambisols with 10.38%, and Cambisols associated with Dystric Xanthic Ferralsol with 3.6%, Dystric Haplic Gleysols alone with 3.0%, and associations of Leptosols (RL), outcrops rock and Cambisols with 1.1%. The assessment of digital classification using a cross-validation, showed that the Ranger classifier presented the highest Kappa (0.55) with overall accuracy of 0.65. The confusion matrix of Ranger classifier shows that confusion wish higher between Ferralsos and Cambissols, due to their association on the landscape. The best map of DSM showed an agreement of 62% with the soil map generated by the conventional method of mapping, with main disagreements occurring on areas of Gleysols. The most intensely occupied area occurs over soils at the bottom of the valley, which are on the banks of the Gleysols. It occurs as result of the soil higher fertility, the flat relief and to the access to water. We concluded that DSM can be used for producing soil map of Rural Settlements, and can be used with god confidence to optimize the Agrarian Reform policy by generating maps.

keywords: Pedology, Soil mapping, Ranger classifier
Spatial data infrastructures for handling soil data

Jorge Mendes de Jesus - ISRIC - World Soil Information, Netherlands

Eloi Ribeiro - ISRIC - World Soil Information, Netherlands

Niels Batjes - ISRIC - World Soil Information, Netherlands

Bas Kempen - ISRIC - World Soil Information, Netherlands

Distribution of soil data using Internet Technologies (IT) is known to be complicated [1][7]. Many technical, maintenance and legislative requirements have to be addressed. Spatial Data Infrastructures (SDI) are frameworks or platforms specially prepared to facilitate distribution of metadata and data in a simple and effective way. With SDIs, it is easier to organize metadata following ISO (International Organization for Standardization) standards, visualize data, download and transform data formats. Here we will describe the soil SDI structure implemented at ISRIC - World Soil Information.

ISRIC's data portal [2] is an SDI-based on open source technologies and open web services defined by OGC (Open Geospatial Consortium), and aimed specifically at soil data. The SDI has 4 major components: Geonetwork [3], Geoserver [4], Geonode [5] and WoSIS (PostgreSQL/PostGIS) [5][6]. The Geonode and Geoserver are integrated components, where visualization and data download is done in Geonode with resources from Geoserver; with these two systems the aspects of data visualization and access are addressed. Metadata for each dataset are described according to ISO 19115:2003/19139. Metadata are stored and publicly available in the Geonetwork system indicating permissions (licences) for use of the associated datasets. A short metadata description is provided in Geonode to help users.

The fourth component of the SDI is WoSIS (World Soil Information Service), a large soil profile database (PostGIS) that provides standardised data (point data) for multiple depths and soil parameters. WoSIS is connected to Geoserver to allow data download from Geonode.

References:

[1] Kempen B, Mendes de Jesus JS, Batjes NH, Ribeiro E, Leenaars JGB and Hengl T 2016. A spatial data infrastructure for storing and exchanging global soil data, Abstract Book, EUROSOIL 2016 (16-21 October 2016), Istanbul, p. 104. http://tiny.cc/eurosoil. Last access 2017 January 26.

[2] http://geonode.isric.org

[3] http://geonetworkopensource.org/

[4] http://geoserver.org/

[5] http://geonode.org/

[6] http://www.isric.org/data/wosis/

keywords: spatial data infrastructures, soil data, metadata, web services, open source