SoilML data exchange format and soil web services

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

Eloi Ribeiro - ISRIC - World Soil Information, Netherlands

David Medyckyj-Scott - Landcare Research Ltd, New Zealand

Alistair Ritchie - Landcare Research Ltd, New Zealand

Peter Wilson - CSIRO - Land and Water, Australia

Peter Dahlhaus - Federation University Australia - Centre for eResearch and Digital Innovation (CeRDI), Australia

Linda Gregory - CSIRO - Land and Water, Australia

Andrew MacLeod - Federation University Australia - Centre for eResearch and Digital Innovation (CeRDI), Australia

Bruce Simons - CSIRO - Land Water, Australia

World soil data and information is dispersed and managed by many organizations in multiple formats, causing problems when researchers are interested in global data for studies. The idea of a common data exchange format, soilML, based on XML (eXtensible Markup Language) has been proposed and is derived from a conceptual model that describes soil features like soil body, horizon and profile. Developing a common data format for soil data exchange is essential for cooperation between multiple entities.

Previous implementations of data exchange formats like eSOTER [1], soil INSPIRE [2], Digital exchange of soil-related data (ISO 28258:2013) [3], and ANZSoilML [4] have been recognized as valuable approaches for soil science activities, but have not been taken up widely by user communities [5]. Implementation of a developmental version of soilML has been demonstrated by CSIRO, LandCare Research NZ, and ISRIC, USGS and others in an interoperability exercise sponsored by the Open Geospatial Consortium (OGC) [6]. Soil data web services using soilML were implemented by/and standardised data successfully exchanged. In the same exercise, CeRDI (Centre for eResearch and Digital Innovation - Federation University Australia) developed a prototype viewer [7] that pools soilML encoded data from multiple sources, demonstrating the feasibility and utility of soilML.

The soilML data web services are delivered through Web Feature Service (WFS) complex features and describe soil properties and pedon characteristics. The WFS output data can also be consumed by Web Processing Services (WPS) where pedotransfer functions are applied to soilML data creating new data.

[1] SoTerML Schema Specification: http://www.isric.org/specification/SoTerML.xsd

[2] INSPIRE specification: http://tinyurl.com/inspire-soil

[3] ISO soilML: http://tinyurl.com/iso-soilml

[4] Simons B, Wilson P, Ritchie A, Cox S. ANZSoilML: An Australian - New Zealand standard for exchange of soil data. EGU General Assembly 2013, held 7-12 April, 2013 in Vienna, Austria, id. EGU2013-6802.

[5] Montanarella L, Wilson P, Cox S, McBratney AB, Ahamed S, McMillan B, et al. Developing SoilML as a global standard for the collation and transfer of soil data and information. http://tinyurl.com/egu-soilml

[6] Ritchie A. OGC Soil Data Interoperability Experiment (Engineering Report) - 16-088r1. https://tinyurl.com/soilie-report

[7] http://data.cerdi.edu.au/soil.demo.php

keywords: soilML, data format, data exchange, web services, pedotransfer, soilIE, Web Processing Service
# Uncertainty and results stability of three digital soil mapping algorithms applied to the soil cover of a farm situated on the north of Udmurt Republic, Russian Federation

Joulia Meshalkina – Soil Science faculty of Lomonosov Moscow State University, Russia

Pavel Dokuchaev – Soil Science faculty of Lomonosov Moscow State University, Russia

One could generate multiple soil maps for the same territory with advanced DSM algorithms. Some areas will belong to the same classification unit on all maps produced by the algorithm. Such areas may be named “zones of stability”. The other areas will be classified differently with the same algorithm. The aim of this study was to identify which territory of the study area are zones of stability with a probability of 100%, 95% and 75%. Traditional soil map (under Russian classification) was done for a farm situated on the North of Udmurt Republic of Russia. Eight soil types and subtypes that are typical for the south taiga zone for the Central Russian Plain were detected on the territory of 19 square km. Several digital soil maps (100 for an algorithm) were produced using multinomial logistic regression, support vector machine and random forest algorithms on the base of covariates: legacy data, terrain attributes and land use. Independent random sample was used to test all soil maps. Traditional soil map was the most accurate and gave 82% of right profiles classifications. MNLR, SVM and RF algorithms gave less than 70% of right profiles classifications. Maps of 100%, 95% and 75% stable soil diagnostics were produced for each algorithms. Three algorithms showed similar results. The proportion of 100% stable land areas was about 40%. Stability zones matched the arable lands on the watersheds. Territories under forests and areas situated in ravines and gullies showed the highest level of uncertainty of classification using digital soil mapping algorithms. Slope territory manifested the intermediate level of uncertainty.

**keywords:** Uncertainty of digital soil mapping, Albeluvisols Umbric, Territories with stable soils diagnostics
## Applying the diagnostic approach for the definition of soil functions – a pilot example on carbon sequestration and storage

Erika Micheli – Szent Istvan University, Hungary

Rachel Creamer – WUR, Netherlands

Adam Csorba – Szent Istvan University, Hungary

Sebastien Drufin – INRA, InfoSol, France

Peter Vadnai – University of Miskolc, Hungary

Endre Dobos – University of Miskolc, Hungary

Diagnostic units (horizons, properties and materials) are commonly applied in a number of international and national classification systems, including WRB, Soil Taxonomy, Hungarian and Irish systems. Diagnostic units can be established in the field, with laboratory analyses or derived from existing soil survey databases. These diagnostic units are designed to support the process of objective soil classification. The soil diagnostic units however carry important information by themselves and can be mapped and provide a better visualisation of the soil-landscape continuum, as and have been demonstrated in different projects (such as the e_SOTER).

The LANDMARK project (EU 2020) has applied the diagnostic unit approach to explain five key soil functions evident in agricultural systems; 1) primary productivity, 2) water purification and regulation, 3) carbon storage and regulation, 4) provision of a habitat for biodiversity and 5) cycling of nutrients. Modelling the capacity of a soil to deliver multiple of these soils functions is the main approach of the LANDMARK project, with a focus at farm, regional and European scales. The diagnostic unit classes is one application currently being considered to define the functional behaviour of our soils. This presentation will use the e_SOTER database and maps to provide an example of how the diagnostic approach can be applied. The procedure will be demonstrated on a pilot area in Hungary exemplified for carbon related (sequestration and storage) and primary productivity functions.

**keywords:** Soil classification, Diagnostics, Soil Functions, Digital Soil Mapping
# Information assessment of uncertainty of the soil's isomorphism in pedons and elementary soil areas

Irina Mikheeva - Institute of Soil Science and Agrochemistry of Siberian Branch of Russian Academy of Sciences, Russia

The concept of isomorphism is the general scientific concept which characterizes the relation between any objects expressing somewhat identity of their structure. It has important philosophical value and is directly connected with categories of distinction, identity, similarity, reliability, etc. As the structure of an object assumes its consideration in the form of the set of its elements (or, in certain cases, in the form of association of its parts and subsets), so consideration of isomorphism usually speaks about the relation between systems, sets of any elements. The systems of elements which are in the isomorphism relation are called isomorphic. This concept shouldn't be mixed with a concept of isomorphism in chemistry which has close connection with the concept of consideration by us; however it isn't special case of isomorphism in strict sense.

Determination of types as the some idealized objects — standard practice of any science by means of which it removes a contradiction between real originality of specific objects and action in them general laws. The type of the soil is generalized, idealized subject of classification but it is having a certain distribution in space. General laws for all soil cover are isomorphic, but they are shown in a special way, rather individually for some set of the objects entering type. This is a reason of spatial soil changeability and soil uncertainty. We had proposed considering three conceptual categories of it. They are heterogeneity for significant changes in soil-forming factors, variability for their insignificant changes, and fluctuation for leveled soil-forming factors. Last two changeability categories are the reason of soil uncertainty, they could be observed at different organization levels of the soil and soil cover.

In our opinion, the quantitative evaluation of relationships between different changeability categories of soil properties is necessary for assessing the stability of the soil cover as a hierarchical system. Entropy is one of reliable scientific parameters to assess it. In the same time, the alterations of soils are inevitable process under change of climate and intensive agricultural and other usages, and changes of natural ecology. Information divergence is parameter which could assess alterations of soil uncertainty under soil evolution. We have conducted such investigation in the case study of main soil types at dry steppe zone in the South of Western Siberia.

**keywords:** isomorphism, information, assessment, soil cover, pedons, elementary soil areas, uncertainty, entropy, information divergence
# A Novel Pedometrics-econometrics Approach to Assess Soil Carbon Capability

Katsutoshi Mizuta – University of Florida, Soil and Water Sciences Department, USA

Sabine Grunwald – University of Florida, Soil and Water Sciences Department, USA

Wendell P. Cropper – University of Florida, School of Forest Resources and Conservation, USA

Wonsuk Lee – University of Florida, Department of Agricultural and Biological Engineering, USA

Gustavo M. Vasques – Embrapa, Brazil

Michelle A Phillips – University of Florida, Department of Economics, USA

Brenton D. Myers – University of Missouri, Department of Soil, Environmental and Atmospheric Sciences, USA

Xiong Xiong – University of Minnesota, Department of Soil, Water, and Climate, USA

Willie G. Harris – University of Florida, Soil and Water Sciences Department, USA

The quantitative assessment of soil quality, health, and security is critically important to sustain soil resources at regional and global scales. Numerous indicators and indices (In/Ix) have been developed to quantify soil resources. According to index number theory, the axiomatic features required for an ideal indication system include 1) Homogeneity, 2) Time-reversibility, 3) Transitivity, and 4) Dimensionality. We identified four other axioms for indication system specific to soil science which are 5) Homology in space, 6) Time and space sensitivity, 7) Monotonicity, and 8) Spatial transferability. Widely used methods to assess soil-specific In/Ix have used scoring, ordination, or multivariate regression methods that do not meet those axiomatic features. Another shortcoming is that soil properties have been predicted or modeled instead of soil In/Ix that quantify soil functions, capability, efficiency, risk, or security. An optimum level or status of soil can be expressed using ‘soil efficiency (or capability)’ that identifies the possibly largest/highest output(s) based on a given set of input(s). For example, the efficiency to sequester carbon in soils to the highest level is co-dependent on the soil-environmental conditions, use, and carbon inputs that control the functionality of soil to sequester carbon. We present a novel pedometric-econometric approach that has been rarely used in soil science.

Our objective was to employ the Data Envelopment Analysis (DEA) to assess the Soil Carbon Sequestration (SCseq) Capability In/Ix (SCI) based on the SCseq rate and pedogenic, climatic, biotic, and hydrologic factors in Florida, USA. The SCseq was calculated based on lab-measured soil organic carbon (SOC) stocks in the top soil (~20cm). Historic and current SOC measurements, covering about 45 years, were used to calculate the SCseq rate. After identifying important environmental variables by the Boruta selection method and Spearman correlation coefficient, the DEA was employed along with the SCseq rate and the selected variables to produce the SCI scores.

The scores ranged between 1.00 and 1.09, with values close to 1 expressing high efficiency for carbon sequestration. The SCI score infers on the optimum capability based on a given set of relevant environmental parameters. Thus, the SCI scores produced useful information to guide management and optimize the soil function compared to previous In/Ix assessments. Additional benefit of using the DEA analysis is its transferability to other geographic regions providing a standardized indication system to quantify specific soil functions. Our pedometric-econometric approach indicates its potential to assess other soil functions and security.

**keywords:** soil carbon sequestration, index number theory, soil index, soil organic carbon, pedometric-econometric approach, Data Envelopment Analysis
Modelling Pedo-Econometric carbon scores with VNIR spectroscopy

Katsutoshi Mizuta – University of Florida, Soil and Water Sciences Department, USA

Sabine Grunwald – University of Florida, Soil and Water Sciences Department, USA

Christopher M. Clingensmith – University of Florida, Soil and Water Sciences Department, USA

Gustavo M Vasques – Embrapa, Brazil

Wonsuk Lee – University of Florida, Department of Agricultural and Biological Engineering, USA

Michelle A. Phillips – University of Florida, Department of Economics, USA

Wendell P. Cropper – University of Florida, School of Forest Resources and Conservation, USA

Xiong Xiong – University of Minnesota, Department of Soil, Water, and Climate, USA

Brenton D. Myers – University of Missouri, Department of Soil, Environmental and Atmospheric Sciences, USA

Visible-near infrared (VNIR) spectroscopy has shown success for cost-effective, rapid, and accurate assessment of various soil properties, including soil organic carbon (SOC). Proximal sensing offers potential to provide soil estimates that can be used in complex soil models that assess soil functions, capability, risk, or security. The Data Envelopment Analysis (DEA) is one of the pedometrics-econometrics methods suited to assess soil capability or efficiency. However, the relationship between soil capability derived from the DEA with analytical lab-based soil measurements and the one derived from VNIR- soil estimates are not well known. Our objective was to compare the soil carbon sequestration (SCseq) capability index (SCI) derived using SOC measured in the laboratory (SOCm) and SOC estimated using VNIR spectra (SOCe) in Florida, USA.

The SCseq rate was calculated based on both historical and current datasets that contained SOCm collected from the top soil (~20 cm depth) in 1965-1995 and 2008-2009, respectively. Some pedogenic and environmental factors relevant to soil carbon sequestration were selected by the Boruta variable selection method as well as the Spearman correlation method. The DEA was conducted with the SCseq and the selected variables to produce the SCI scores. The VNIR spectrum was calibrated using Partial Least Square Regression method and Random Forest (RF), after 25 preprocessing methods were applied. The model with the highest prediction accuracy was chosen to generate the SOCe. The DEA was implemented based on these VNIR estimates to predict the SCI scores. The accuracy of the SCI prediction derived from the SOCe using the RF algorithm showed the highest R2 of 0.7. This implies that the SCI scores are site-specific yet predictable using field-based spectroscopy data. The successful estimation of the DEA-SCI scores via VNIR spectra show promise for future applications that assess soil quality, health, and security.

**keywords:** Soil health, Soil quality, Soil security, Visible-near infrared spectroscopy, soil organic carbon, soil carbon sequestration, Data Envelopment Analysis, Partial Least Square Regression, Random Forest, Boruta Analysis
# Predicting artificially drained areas by means of a selective model ensemble

Anders Bjørn Møller - Aarhus University, Denmark

Amélie Beucher - Aarhus University, Denmark

Bo Vangsø Iversen - Aarhus University, Denmark

Mogens Humlekrog Greve - Aarhus University, Denmark

Subsurface drain pipes in agricultural fields have a large impact on crop yields, the hydrological cycle, the leaching of nutrients and pesticides and numerous soil properties. However, the location of artificially drained areas is often unknown. In Denmark, drainage activities have been carried out since the mid-19th century, and it has been estimated that half of the cultivated area is artificially drained (Olesen, 2009).

A number of machine learning approaches can be used to predict artificially drained areas in geographic space. However, instead of choosing the most accurate model produced with one of these approaches, a better prediction can be achieved by combining the predictions of several models (Caruana et al., 2004, Sollich and Krogh, 1996).

As more approaches become available, the importance of the method used for selecting the models for use in the ensemble increases.

The study aims firstly to train a large number of models to predict the extent of artificially drained areas using various machine learning approaches. Secondly, the study will develop a method for selecting the models, which give a good prediction of artificially drained areas, when used in conjunction.

The approaches employed include decision trees, discriminant analysis, regression models, neural networks and support vector machines amongst others. Several models are trained with each method, using variously the original soil covariates and principal components of the covariates.

With a large ensemble, the time necessary for producing a map can easily exceed the practical limits set for the task. For this reason, the study aims not only to produce the ensemble which gives the best prediction of artificially drained areas, but also to take into account the time necessary for producing a map with each method, when selecting the models.

In this way, the developed method should be able to produce a highly accurate and robust map of artificially drained areas within a limited span of time.

## References

Caruana, R., Niculescu-Mizil, A., Crew, G. and Ksikes, A., 2004. Ensemble selection from libraries of models. Proceedings of the twenty-first international conference on Machine learning, ACM, 18.

Olesen, S.E., 2009. Kortlægning af potentielt dræningsbehov på landbrugsarealer opdelt efter landskabselement, geologi, jordklasse, geologisk region samt høj/lavbund. Aarhus University.

Sollich, P. and Krogh, A., 1996. Learning with ensembles: How overfitting can be useful. Advances in neural information processing systems, 190-196.

**keywords:** Soil drainage, Model ensemble, Mapping
# Soil map disaggregation improved by soil-landscape relationships, area-proportional sampling and random forest implementation

Anders Bjørn Møller - Aarhus University, Denmark

Brendan Malone - University of Sydney, Australia

Nathan Odgers - University of Sydney, Australia

Budiman Minasny - University of Sydney, Australia

Amélie Beucher - Aarhus University, Denmark

Bo Vangsø Iversen - Aarhus University, Denmark

Mogens Humlekrog Greve - Aarhus University, Denmark

Detailed soil information is often needed to support agricultural practices, environmental protection and policy decisions. Several digital approaches can be used to map soil properties based on field observations. When soil observations are sparse or missing, an alternative approach is to disaggregate existing conventional soil maps.

At present, the DSMART algorithm represents the most sophisticated approach for disaggregating conventional soil maps (Odgers et al., 2014). The algorithm relies on classification trees trained from resampled points, which are assigned classes according to the distribution of soil types within the polygons. Vincent et al. (2016) successfully implemented soil-landscape relationships into the algorithm.

The study tests the sensitivity of the algorithm towards the input data by using conventional soil maps for Denmark produced by Jacobsen (1984) and the Commission of European Communities (CEC, 1985) respectively, both using the FAO 1974 classification. Furthermore, the effects of implementing soil-landscape relationships, using area proportional sampling instead of per polygon sampling, and replacing the default C5.0 classification tree algorithm with a random forest algorithm were evaluated. The resulting maps were validated on 777 soil profiles situated in a grid covering Denmark.

The experiments showed that the results obtained with Jacobsen's map were more accurate than the results obtained with the CEC map, despite a nominally coarser scale of 1:2,000,000 vs. 1:1,000,000. This finding is probably related to the fact that Jacobsen's map was more detailed with a larger number of polygons, soil map units and soil types, despite its coarser scale.

The results showed that the implementation of soil-landscape relationships, area-proportional sampling and the random forest implementation generally improved the algorithm's ability to predict the correct soil class. The implementation of soil-landscape relationships and area-proportional sampling generally increased the calculation time, while the random forest implementation reduced the calculation time.

In the most successful experiments, the disaggregation provided a good prediction of the soil types, despite the coarse scale of the input maps.

## References

* CEC, 1985. Soil Map of the European Communities at scale 1:1,000,000, Luxembourg.

* Jacobsen, N.K., 1984. Soil map of Denmark according to the FAO-UNESCO Legend. Dan. J. Geogr. 84, 93-98.

* Odgers, N.P., Sun, W., McBratney, A.B., Minasny, B. and Clifford, D., 2014. Disaggregating and harmonising soil map units through resampled classification trees. Geoderma 214-215, 91-100.

* Vincent, S., Lemercier, B., Berthier, L. and Walter, C., 2016. Spatial disaggregation of complex Soil Map Units at the regional scale based on soil-landscape relationships. Geoderma.
keywords: Disaggregation, Models, Soil classes
Digital Soil mapping Based on Airborne Gamma-Ray Imagery and
Fuzzy logic : a Case of Upper Pasak Watershed, Thailand

Ruamporn Moonjun – Land Development Department, Thailand

Dhruba Pikha Shrestha – University of Twente, Netherlands

Victor G. Jetten – University of Twente, Netherlands

Frank J.A. van Ruitenbeek – University of Twente, Netherlands

Airborne gamma-ray imagery (AGRI) provides coarse-resolution (approximately 400 × 400 m pixel) spatial in-formation on gamma-ray emitting elements, i.e. potassium (K), thorium (Th) and uranium (U), in the upper half meter of the soil. These radioelements are a potential information source for soil mapping since their abundance is related to soil geochemistry, specifically the chem- ical composition of parent materials and their weathering products resulting from geomorphic and pedogenic processes.

The aim of this study was to evaluate the potential of AGRI for improving digital soil survey
process in a mapping soil applying digital soil mapping method in complex landscapes. The study
was conducted in a well-characterized complex soil landscape: the 560 km² upper Pa Sak watershed
in Petchaboon province, Thailand. Clustering of gamma ray and elevation data (DEM) was carried
out using fuzzy logic to generate various classification layers. Final map was produced using total
weighted inverse distance of all the classes in a 3 by 3 window. Class label was assigned to the one
with the largest total inverse distance over the entire set of fuzzy classification bands.

The result shows relatively higher classification accuracy for soil parent material differentiation
(overall accuracy of 72%) as compared to the classification for soil types (67%).

keywords: Digital soil mapping, Soil mapping, GIS, Remote Sensing, Fuzzy, Watershed, Thailand
A 2D multifractal analysis based on detrended fluctuation analysis
applied to El Pardo landscape

Maria Carmen Morató – Dept of Applied Mathematics, ETSIAAB, Universidad Politécnica de Madrid, Spain

Maria Teresa Castellanos – Dept of Applied Mathematics, ETSIAAB, Universidad Politécnica de Madrid, Spain

Pedro L. Aguado – Dpto. Producción Agraria, ETSIAAB, UPM, Spain

Juan Pablo Del Monte – Dpto. Producción Agraria, ETSIAAB, UPM, Spain

Ana Maria Tarquis – Universidad Politécnica de Madrid, Spain

Multifractal detrended fluctuation analysis (MFDFA) has been well known technique applied to 1D signal, time series and soil transect data (Morató et al., 2017). It has the advantage that removes the trend of the data making possible a multifractal analysis. Recently, MFDFA algorithm has been generalized to deal with two-dimensional signals (Caiping Xi et al., 2016). This type of analysis could be a potential tool to easily simulate distinctive topography spatial distribution and to simulate erosion or understand the main factors that creates a complex dynamic in the landscape evolution.

We present an application of 2D MFDFA to landscape data for the first time based on the altitude. The study zone is a matrix of 2053 x 2053 pixels, with a resolution of 5m (25 m2 by pixel), obtained from a digital terrain model (DTM) using the latest informatics tools. This zone corresponds to quite homogeneous region with respect to soil characteristics and climatology but with topographic distinctive areas, known as “Monte de El Pardo” (Aguado et al., 2014), due to the directions of the two river basins, Trofa and Manzanares, and the gradual change in the direction of the Trofa river towards Manzanares river along the centuries.

A detailed description of the application of the 2D MFDFA is showed: the statistical accuracy, the sensitivities of the sample size, the selection of scaling range, the choice of the q-orders and the calculation amount. Several interpretations are made based on the results obtained and a comparison is established with the 1D MFDFA (Castellanos et al., 2017).

References

Aguado, P.L., Del Monte, J.P., Moratiel, R. and Tarquis, A.M. 2014. Landscape spatial characterization of DEM through multifractal analysis. The Scientific World Journal, 1-9. http://dx.doi.org/10.1155/2014/56

Caiping Xi, Shuning Zhang, Gang Xiong, Huichang Zhao. A comparative study of two-dimensional multifractal detrended fluctuation analysis and two-dimensional multifractal detrended moving average algorithm to estimate the multifractal spectrum. Physica A, 454, 34–50.

Castellanos, M.T. Morató, M.C., Aguado, P.L., del Monte, J.P. and Tarquis, A.M. 2017. Spatial charaterization of El Pardo landscape using Detrended Fluctuation analysis. Geophysical Research Abstracts, 19, EGU2017-19204.

Morató, M.C., Castellanos, M.T., Bird, N.R. and Tarquis, A.M. 2017. Multifractal analysis in soil properties: Spatial signal versus mass distribution. Geoderma, 287, 54-65.

keywords: modelling landscape, detrended fluctuation analysis, multiscaling, anysotropy
Implementing Pedometrics Outside the Discipline: Context, Trans-
lation and Scalability

Cristine Morgan – Texas A&M University, USA

Jason Ackerson – Texas A&M University, USA

Yohannes Yimam – Texas A&M University, USA

The discipline of pedometrics can serve as a vehicle for the integration of multiple scientific disciplines (e.g. statistics, math, engineering, information technology, environmental physics, pedology, edophology) but can also provide an opportunity to translate soil science knowledge and understanding to real-world applications. Improving this translation requires pedometric tools to be evaluated in terms of fitness for use: considering not only the mathematical rigor of a tool/approach, but how well it will perform the function society needs it to perform. Key considerations for implementing pedometrics outputs should include availability and translatability of pedometric tools, access to pedometric tools/practitioners, and compatibility of pedometric approaches with existing institutional structures. We will show examples of success where pedometric tools and products can be integrated to solve problems by scientists in other disciplines, agribusiness, as well as by land managers. These examples will identify some missing components of common pedometric practices, such as insensitivity to the context of where/who/when the tool will be employed. As novel pedometric tools are being discovered and products are being produced, they need to be evaluated and developed with an understanding/consideration of the societal and scientific context where they are to be used (paradigm).

keywords: proximal sensing, digital soil mapping, scaling, translation
# Soil-landscape controls on the impact of extreme warm and dry events on terrestrial ecosystems within continental Europe and the Mediterranean Basin

Vera Leatitia Mulder - Soil Geography and Landscape Group, Wageningen University, Netherlands
Christel Melissa van Eck - Department of Geosciences, Environment and Society, Université Libre de Bruxelles, Belgium

Pierre Friedlingstein - College of Engineering, Mathematics and Physical Sciences, University of Exeter, United Kingdom

Dominique Arrouays - INRA InfoSol Unit, France

Pierre Regnier - Department of Geosciences, Environment and Society, Université Libre de Bruxelles, Belgium

Vegetation activity within continental Europe and the Mediterranean Basin is changing and extreme warm and dry events cause substantial reductions in vegetation activity. This region covers a wide variety of climate regimes, soil and land cover types. Distinguishing the response of the vegetation to extreme heat, drought and compound events as a function of land cover type, climate regime and soil type allows to better explain regional and local differences in vegetation response. This research analyses the temporal evolution of the impact of extreme heat, drought and compound events on the observed NDVI during the growing season, aggregated by land cover, soil and climate types. The results show 31% reduction of the median vegetation activity, though highly variable depending on local conditions in climate, soil and vegetation type. The maximum impact was observed for extreme warm and dry compound events, one month after the extreme event occurrence, and recovery took up to 4 months. Furthermore, the impact of extreme heat and drought on vegetation activity increases when moving to colder climates with the largest change occurring in high latitude natural land cover types.

The soil properties affecting the impact of extreme droughts on vegetation activity were mainly related to root depth limitations, water regulating properties and ecophysiological properties. Within a temperate climate, water availability and water regulating properties of the soil were found most important; in water limiting conditions those showing limiting root development conditions linked to abrupt textural changes (e.g. Luvisol), accumulated salts (e.g. Solonchak) or unfavorable soil structure (compaction, swelling/shrinking) were more impacted and had a slower recovery. Within a hot and dry temperate climate, there were major differences in the overall productivity rather than a different response to extreme events. These differences were mainly related to growth limiting conditions due to accumulated salts in the soil profile (e.g. Calcisol, Solonchak). Furthermore, it was found that vegetation activity of stable forest ecosystems was strongly impacted by extreme climatic events, especially in Northern Europe. The controlling factors defining the impact of extreme warm and dry events were found to be a complex combinatory of soil, climate and ecophysiological properties (e.g. Podzols and mycorrhiza networks in Northern Europe).

With the expected increase of extreme climatic events, the current stable and productive systems will be most affected and large reductions in vegetation activity can be expected, partly due to the lack of ecophysiological adaptation of plants towards the change in climate.

**keywords:** Soil-landscape controls, Extreme climatic events, Terrestrial ecosystems, Large extent
Pedometric methods to optimize sampling and improve classification on a pilot site in the Mount Kenya region

Evans Mutuma - Kenya Agricultural and Livestock Research Organization, Kenya

Ádám Csorba - Szent István University, Hungary

Amos Wawire - Kenya Agricultural and Livestock Research Organization, Kenya

Endre Dobos - Miskolc University, Hungary

Erika Michéli - Szent István University, Hungary

The objective of the study was to evaluate the variability of SOC in different Reference Soil Groups (RGS) and to compare the determined classification units with the KENSOTER database based on a soil sampling campaign carried out on the agricultural lands of the Upper Catchment of Tana River (Meru County, Kenya) covering an area of 1200 km². The geology of the area consists mainly of volcanic rocks (Pliocene lahars, phonolites, tuffs basalts, ashes) and recent superficial deposits. A total of 28 open and 49 augered profiles were evaluated, which were selected using Conditioned Latin Hypercube sampling design. During the field work the profiles were classified according to the World Reference Base for Soil Resources (WRB) 2015. A total of 269 soil samples were collected, which samples were analyzed for total carbon, organic carbon, particle size distribution, percent bases, cation exchange capacity and pH among others. Based on the earlier surveys, geological and environmental setting, Nitisols were expected to be the dominant soils of the sampled area. Based on this study the major differences to KENSOTER database are the presence of high activity clays (CEC value range 27.6 cmol/kg - 70 cmol/kg), high silt content (range 32.6 % - 52.4 %) and silt/clay ratio (range of 0.6 - 1.4) keeping these soils out of the Nitisols RSG. There was good accordance in the morphological features with the earlier survey but failed the silt/clay ratio criteria for Nitisols. This observation calls attention to set new classification criteria for Nitisols and other soils of warm, humid regions with variable rate of weathering to avoid difficulties in interpretation. To address the classification problem, this paper further discusses the taxonomic relationships between the studied soils. On the contrary most of the diagnostic elements (like the presence of Umbric horizon, Vitric and Andic properties) and some qualifiers (Humic, Dystric, Clayic, Skeletic, Leptic) represent useful information for land use and management in the area.

keywords: World Reference Base, Nitisols, Classification, Latin Hypercube Sampling, Taxonomic relationships
Comparison between random forest and partial least square re-
gression of on-line vis-NIR spectroscopy measurements of soil to-
tal nitrogen and organic carbon

Said Nawar - Cranfield university, United Kingdom

Abdul M. Mouazen - Ghent University, Belgium

Accurate and detailed spatial soil information about within field variability is essential for variable-rate applications of farm resources. Soil total nitrogen (TN) and organic carbon (OC) are important fertility parameters that can be measured with on-line (mobile) visible and near infrared (vis-NIR) spectroscopy, whose the calibration method may considerably affect the measurement accuracy. This study aims at comparing the performance of local farm scale calibrations with those based on spiking of local samples into an European continental dataset (ECD) for TN and OC estimation using two modelling techniques, namely, random forest (RF) and partial least squares regression (PLSR). An on-line sensor platform equipped with a mobile, fiber type, vis-NIR spectrophotometer (AgroSpec from tec5 Technology for Spectroscopy, Germany), with a measurement range of 305-2200 nm was used to acquire soil spectra in diffuse reflectance mode from two fields in the UK. After dividing spectra into calibration (75%) and validation (25%) sets, spectra in the calibration set were subjected RF and PLSR with leave-one-out cross-validation to establish calibration models of TN and OC. On-line predicted values of TN and OC were used to develop maps using ordinary kriging in ArcGIS software (ESRI, USA). Results showed that RF outperformed PLSR models for both datasets used, whereas the lowest model performance was obtained with the local dataset. The effect of spiking local samples into the ECD was significant, and resulted in high coefficients of determination (R2) values of 0.97 and 0.98, low root mean square error (RMSE) of 0.01 and 0.11, and high residual prediction deviations (RPD) of 5.78 and 7.17, for TN and TC, respectively. The on-line predicted maps showed better spatial similarities between laboratory measured and RF predicted maps, as compared to PLSR predicted maps. Therefore, these results suggest that ECD vis-NIR RF calibration models can be successfully used to predict TN and OC under on-line measurement conditions.

**keywords:** Vis-NIR spectroscopy, spiking, random forest, partial least squares regression, soil mapping
Geochemical Signatures of Pristine Volcanic Ash and Soils from
Krakatau as revealed by a Portable XRF spectrometer

Malik Nelson – Payakumbuh Agricultural Polytechnic Institute, Indonesia

Dian Fiantis – Andalas University, Indonesia

Budiman Minasny – Sydney University, Australia

The Krakatau complex was formed after the well-known eruption of the late Mt. Krakatau in 1883, the complex consists of four islands: Sertung, Panjang, Rakata and Anak Krakatau. Mt. Anak Krakatau was formed from the 1930s and is composed of lava and pyroclastic deposits after the devastating eruption of the late Mt. Krakatau in 1883. Anak Krakatau is still active, with frequent eruptions producing volcanic ashes. Nearby was Sibesi, an island in the Sunda Strait between Java and Sumatra, which was completely destroyed during the eruption of Mt. Krakatau in 1883. We collected and investigated the volcanic ash samples from Mt. Anak Krakatau, Rakata, Panjang and Sibesi islands. Geochemical analysis was carried out using a portable x-ray fluorescent spectrometer (XRF). In addition, chemical weathering indices were calculated from the geochemical elements. The linear discriminant analysis was used to separate out materials from the 4 islands using their geochemical concentration (SiO2, Al2O3, Fe2O3, CaO, MgO, K2O, SO3, P2O5, Ti and Zr). While the four islands were nearby and influenced by the eruption of Mt Krakatau, the analysis showed that the geochemical characteristics of volcanic ash for each of the island is distinct. Mahalanobis distance differentiated samples of Anak Krakatau with other three soils. Base loss in Sibesi islands was larger compared to Rakata, Panjang and Anak Krakatau. Meanwhile desilication was larger in Rakata compared to Sibesi, Panjang and Anak Krakatau. The following sequence of weathering can be summarized: Sibesi > Rakata ≤ Panjang > Anak Krakatau. As a conclusion, the portable XRF allows a rapid acquisition of geochemical signatures of volcanic ash samples. Comparison between sites, allow differentiation of origins of the materials and a calculation of a relative weathering sequence.

keywords: *Dyscriminant analysis*, *Krakatau*, *Geochemical signatures*
Challenges in using mid-infrared spectroscopy for the determi-
nation of soil physical, chemical, and biochemical properties on
undisturbed soil samples

Anne Neuser – Bergische Universität Wuppertal, Germany

Jannis Heil – Bergische Universität Wuppertal, Germany

Bernd Marschner – Ruhr-Universität Bochum, Germany

Britta Stumpe – Bergische Universität Wuppertal, Germany

Diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy in the mid-infrared range (MIR) has become an established analytical tool for quantitative and qualitative analysis of soil samples. The heterogeneity of soil requires sample preparation procedures to optimize the reproducibility and accuracy of the spectroscopic measurement. These procedures have not been standardized. Generally, soil is dried and finely ground before measurement to avoid reflections of surface water films and minimize the intra- and inter-particle variability, respectively. Additionally, the sample surface is levelled to a plain surface for an ideal reflection. However, these sample preparation techniques are limited to disturbed samples only. Thus, a potential DRIFT mapping of undisturbed soil samples requires an adjusted calibration to allow for an accurate prediction of soil properties.

In this study, we developed a method for calibrating the prediction of DRIFT spectra collected from undisturbed soil samples. In a first step, differences of spectral information measured from undisturbed and ground soil samples have been evaluated. Therefore, we record the DRIFT spectra of 120 German and 120 West-African (Ghana and Burkina Faso) chemically well characterized soils. DRIFT spectra of both, ground and sieved only soil samples are recorded and both calibrated against different physio-chemical soil properties, such as texture, CEC, organic carbon, pH, or iron oxides. In preliminary experiments, we found that spectra of sieved and ground sampled significantly differed in specific spectral regions representing clay minerals, as well as organic matter. It can be assumed that the prediction of surface related soil parameters, such as CEC, could be superior using sieved soil spectra, as grounding alters the surface structure of the soil.

In a further step, microtopgraphy effects on spectra quality from disturbed as well as undis-
turbed soil samples have been evaluated. Therefore, spectral information has been taken from two
dimensional disturbed and undisturbed soil samples at a high spatial resolution of 2 mm. The
spectra quality was significantly higher in the disturbed soils since microtopography was absent
in these samples. Thus, a digital elevation model (DEM) will be constructed using close-range
digital photogrammetry to correct for these topography effects of the DRIFT mapping data from
undisturbed soil samples.

With this new method, there is a potential of imaging soil parameters on a microscale that can
help considerably in locating and understanding soil processes on a small scale.

keywords: diffuse reflectance infrared Fourier transform spectroscopy, microscale soil mapping, microtopography correction, soil imaging
Rapid sensing of petroleum-contaminated soils with mid infrared
spectrometers

Wartini Ng – University of Sydney, Australia

Budiman Minasny – University of Sydney, Australia

Brendan Malone – University of Sydney, Australia

Petroleum contamination in soil has been recognized as a global issue. With potentially millions of contaminated sites around the world, there needs to be rapid and efficient assessment technologies to detect contamination in the soil. Conventional soil analysis in the laboratory is not only time-consuming, but also expensive. This study evaluates the usage of a portable mid-infrared (MIR) spectrometer as an alternative to conventional soil analysis to assess petroleum contamination based on its absorbance on various wavelengths. A laboratory study was first done to evaluate the effect of common soil factors, such as soil texture, organic matter content, and the types and concentrations of contaminants on infrared absorbance. By utilizing continuum removal technique on the spectra, the effect of those factors can be isolated and quantified. The absorption peaks affected by petroleum contamination (currently reported as TRH: Total Recoverable Hydrocarbon) were found in the 2990 - 2810 cm⁻¹ region which is related to the aliphatic -CH2 and -CH3 band assignments. The addition of organic matter as compost affected peaks between 2100 - 1700 cm⁻¹ region (particularly the peak at 1980, 1870, and 1790 cm⁻¹), which is attributed to quartz overtone but not the 2990 - 2810 cm⁻¹ region. Meanwhile, soil texture was mainly affecting the absorbance peaks in the 2990 - 2810 cm⁻¹ region. Soil that has finer particle sizes tends to scatter more of the light; and therefore, reducing the overall absorbance. Statistical models were then developed from the identified absorption features to predict the concentration of total recoverable hydrocarbon (TRH) as a whole, and as fractions of semi-volatiles: C10-C16, C10-C16 minus Napthalene (F2), C16-C34 (F3), C34-C40 (F4). These models were then validated with field contaminated samples. This study will also try to model the prediction of components of polycyclic aromatic hydrocarbons (PAH), particularly naphthalene (the most volatile PAH), benzo(a)pyrene and dibenz(a,h)anthracene (the most toxic PAH compounds).

keywords: Soil contamination, Soil sensing, Spectroscopy, Mid infrared, Petroleum hydrocarbon
Predicting and Mapping Topsoil Black carbon of France

Wartini Ng – University of Sydney, Australia

Budiman Minasny – The University of Sydney, Australia

Brendan Malone – University of Sydney, Australia

Nicolas Saby – Institut national de la recherche agronomique (INRA), France

Dominique Arrouays – Institut national de la recherche agronomique (INRA), France

Bernard Barthes – Institut de recherche pour le développement (IRD), France

Michael Clairotte – Institut de recherche pour le développement (IRD), France

Black Carbon (BC) in soil is considered a stable or resistant form of carbon, and thus it is important to determine its amount relative to the Total C (TC). As measurement of BC is quite time-consuming, involving chemo-thermo-oxidation at 375°C, decarbonation, and elemental analysis, it is anticipated that BC can be predicted using a rapid sensing analysis such as the mid-infrared spectroscopy. BC was only measured for topsoil at 158 sites in the French soil monitoring network. On the other hand, total carbon, and mid-infrared reflectance were measured at 2130 sites on a regular 16 × 16-km grid. Contrary to what was found in the literature, we found that BC was poorly predicted by the mid-infrared spectroscopy (Validation R2 ~ 0.25). Nevertheless, BC was well predicted just using 3 soil variables (clay content, pH and Total C) with a mean validation R2 of 0.45. Including soil properties along with the infrared spectra did not improve the prediction. We concluded that this form of thermally-resistant Carbon is a combination of various organo-mineral and pyrogenic forms of carbon, which may not have unique spectral feature. BC content also varies across the area with a median BC/TC value of 0.05, and a minimum of 0.01 and a maximum of 0.32. Based on soil properties prediction, we map the concentration of Black Carbon across France.

**keywords:** Black carbon, Mid Infrared Spectroscopy (MIR), Pedotransfer Functions, French soil monitoring network
Using digital soil morphometrics to study landscape – vegetation
pattern relations

Jelmer Nijp - Wageningen University, Netherlands

Arnaud Temme - Kansas State University, USA

George van Voorn - Wageningen University, Netherlands

Ryan Teuling - Wageningen University, Netherlands

Merel Soons - Utrecht University, Netherlands

Jakob Wallinga - Wageningen University, Netherlands

Lammert Kooistra - Wageningen University, Netherlands

Peatlands are hotspots for soil organic carbon: despite covering just 3% of the earth surface, about 20% of all terrestrial soil carbon is locked away in northern peatlands. These carbon store of global importance are put under pressure by changes in land use and climate, which have the potential to destabilize soil carbon stocks. Such transitions may occur smoothly or as a 'tipping point', where peatlands may abruptly and unpredictably 'tip' to a state of carbon uptake to a state with carbon emissions after exceeding a climatic threshold. Recent advances indicate that changes in spatial patterns of vegetation may function as 'early warning signals' to predict impending tipping points. Yet, such indicators critically rely on the assumption that the landscape is uniform.

In this research we study the effect of landscape on vegetation patterning in a peatland in northern Sweden. Digital soil morphometrics are derived from Digital Terrain Models (2 2m) and related to metrics describing peatland pattern characteristics, which are calculated from classified vegetation maps using aerial images. Our results are of importance for understanding the complex interactions between soil formation and vegetation in 'real' rather than theoretical peat-landscapes, and are important in understanding the resilience of peat carbon stocks to forthcoming climatic changes.

keywords: Peatlands, Soil organic carbon, Digital soil morphometrics, Vegetation, Climate, Aerial image classification
Prediction of mineralizable nitrogen (N) in soils using ensembles
of regression models

Stevanche Nikoloski - Josef Stefan International Postgraduate School, Ljubljana, Slovenia; Teagasc, Environment Soils and Land-use Department, Johnstown Castle, Co. Wexford, Ireland, Slovenia

Marko Debeljak - Josef Stefan International Postgraduate School, Ljubljana, Slovenia, Slovenia

Rachel Creamer - Wageningen University & Research, Departement of Environmental Sciences, The Netherlands, Netherlands

David P. Wall - Teagasc, Environment Soils and Land-use Department, Johnstown Castle, Co. Wexford, Ireland, Ireland

Saso Dzeroski - Josef Stefan International Postgraduate School, Ljubljana, Slovenia, Slovenia

Aneta Trajanov - Josef Stefan International Postgraduate School, Ljubljana, Slovenia, Slovenia

Mineralization of nitrogen (N) is the process by which organic N is transformed into inorganic forms that are available for plant uptake. The N-mineralization is one of the key soil processes which enable plant growth and efficient crop production. Soils that provide large quantities of plant-available N through mineralization of soil organic matter require less fertilizer N applications per unit yield.

The supply of soil N through mineralization processes can vary largely between different soil types and is effected by soil and crop management, and environmental conditions. Knowledge of soil N supply will help to improve the use efficiency of applied N fertilizers. Therefore, there is a critical need to develop models for predicting mineralization of N in agricultural soils.

In this study we use chemical, physical and diagnostic soil attributes to predict the potential soil mineralization of N. The data were provided by Teagasc, Ireland, and describe 93 permanent grassland sites across Ireland. The soil samples covered a broad range of soil types and physio-chemical properties, sampled between March and October 2012 - 2014. They were taken for surface horizons only, which varied in depth, but generally were 0-25 cm depth.

To predict the mineralizable N potential of the soil, we applied several tree-based ensemble methods for regression. The ensemble methods used in our research were bagging, random subspaces, random forests and combination of bagging and random subspaces. The predictive performance of different ensemble methods was evaluated with 10-fold cross-validation. The obtained models achieved very high predictive performances (correlation coefficient around 0.9), with random subspaces performing the best.

Since the Random Subspaces algorithm does not provide interpretable models, but only predictions, we used the feature ranking method ReliefF to select the most relevant attributes for the prediction of the mineralizable N in the soil. The five most relevant attributes that were selected are: total carbon, organic carbon, active iron, active aluminum and total nitrogen in the soil. The results of this study show that mineralizable N in the soil can be accurately predicted using only a few chemical soil parameters, which means that the soil sampling requirements for prediction of mineralized N can be considerably simplified.

keywords: Nitrogen mineralization, Ensemble regression models, Feature Ranking, Random Subspaces
Insights in the possibilities of an electromagnetic induction sensor
to map the military remains, buried in the former World War 1
front zone

Nicolas Note - Ghent University - Department of soil management, Belgium

Timothy Saey - Ghent University - Department of soil management, Belgium

Wouter Gheyle - Ghent University - Department of Archaeology, Belgium

Birger Stichelbaut - Ghent University - Department of Archaeology, Belgium

Hanne Van den Berghe - Ghent University - Department of Geography, Belgium

Veerle Van Eetvelde - Ghent University - Department of Geography, Belgium

Jean Bourgeois - Ghent University - Department of Archaeology, Belgium

Marc Van Meirvenne - Ghent University - Department of soil management, Belgium

On November 11th 1918, the military impact on the Western front zone was called to an end. Almost a hundred years later, traces of this war are still reflecting in the landscape: cemeteries, mine and shelling craters, bunkers, high concentrations of metal shrapnel, bomb shells rising from the subsoil, etc. ... These above surface remains are easy to identify in the current landscape. A more rising question is what is still buried beneath the surface. This unknown data is one of the research topics in the UGent's integrated research project 'Non-invasive landscape archaeology of the Great War' (2014-2018). For this project, 220 ha were already scanned at the former front zone in Belgium with an electromagnetic induction sensor (EMI) by the Department of Soil Management to investigate the buried remains of this War. Site selection and archaeological feature interpretation of the scan results were guided by the expertise and photograph database of the Centre for Historical and Archaeological Aerial Photography (Ghent University, In Flanders Fields Museum). This collaboration opened up a series of insights in the possibilities and limitations to investigate buried WW1 archaeological structures in a non-invasive way. Because of the scale and the spread of the EMI-surveys, we can compare data parameters collected at several locations in this formal war zone. For example: by applying a metal filter on the EMI data, we perceive that fields in the northern area of the Belgian front zone indicate a lower rate of metal shrapnel than the southern part. Our research showed also that EMI metal parameters can be correlated with the shelling densities seen on historical aerial photographs. Based on this secondary data source which almost entirely covers the front zone, the level of metal shrapnel pollution can be simulated for this region. Only field validations (excavations) of metal objects are the missing link to simulate metal quantities, buried beneath Flanders Fields.

keywords: World War One, Electromagnetic induction, Soil scan, Destruction map, Historical aerial photographs
Comprehensive evaluation of statistical approaches for digital soil
mapping with large sets of environmental covariates

Madlene Nussbaum - Berne University of Applied Sciences (BFH), School of Agricultural, Forest and Food Sciences (HAFL), Switzerland

Andreas Papritz - Department of Environmental Systems Science, ETH Zurich, Switzerland

Marielle Fraefel - Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Switzerland

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

Urs Grob - NABO, Agroscope, Switzerland

Armin Keller - NABO, Agroscope, Switzerland

Spatial assessment of soil functions requires maps of basic soil properties. Unfortunately, these are either missing for many regions or are not available at the desired spatial resolution or down to required soil depth. Conventional map acquisition remains costly. Meanwhile, soil legacy data and comprehensive sets of spatial environmental data are available for many regions. Digital soil mapping (DSM) approaches – relating soil data (responses) to environmental data (covariates) – are facing the challenge to build statistical models from large sets of covariates originating for example from hyperspectral remote sensing or multi-scale terrain analysis.

We evaluated six approaches for DSM in three study regions in Switzerland (Berne, Greifensee, ZH forest) by mapping effective soil depth available to plants, pH, soil organic matter (SOM), cation exchange capacity, clay, silt, gravel content and bulk density for four soil layers (0-10, 10-30, 30-50 and 50-100 cm, totalling 48 responses). Models were built from 300-500 environmental covariates by 1) model selection for linear models through grouped lasso, 2) robust external drift kriging (EDK), 3) geoadditive models selecting penalized smoothing spline terms by componentwise gradient boosting (geoGAM), and two tree-based methods 4) boosted regression trees (BRT), 5) Random Forest (RF). Lastly, we computed 6) weighted model averages (MA) from predictions by methods 1–5.

Lasso, georob and geoGAM successfully selected strongly reduced sets of covariates (subsets of 3-6 %). To automatically select a sparse trend model for EDK was however difficult and the applied ad hoc procedure was computationally inefficient and overfitted the data. Differences in predictive performance, tested on independent validation datasets, were mostly only small and did not reveal a single best method over all 48 responses. Nevertheless, RF was on average often best among 1–5 (29 out of 48 responses), but was outcompeted by MA at 15 out of these 29 responses. At the same time also RF tended to overfit the data. At the same time also RF tended to overfit the data. Performance of BRT was similar to RF, but slightly lower. GeoGAM performed poorly on some responses and was only best on 5 out of 48 while predictive precision of lasso was in between. Models had generally low bias. Only the computationally very efficient lasso had slightly larger bias larger while it tended to under-fit the data.

keywords: Methods comparison, Geoadditive modelling, Componentwise boosting, Machine learn-
ing, Model averaging, Overfitting
# Effect of variable manure rate applications on grass yields and implementation of knowledge about soil conditions

Sylvan Nysten – Aeres University of Applied Sciences, Netherlands

Kees Westerdijk – Aeres University of Applied Sciences, Netherlands

Linda Nol – Aeres University of Applied Sciences, Netherlands

GPS techniques have significantly changed agricultural management. Smart Farming Technologies integrate GPS-techniques, local knowledge and farmers 'strategies, spatial information such as yield maps and soil maps and variable rate applications. In horticulture, Smart Farming Technology is nowadays very common in developed countries; however, for grassland production it is still in its infancy. Since 2015 a trial is carried out with yield maps generated by a forest harvester on grassland. The goal of this trial is to investigate in which way variable rates of manure, based on a yield maps, can help improve yield and soil quality as a basis for sustainable production of grass. The trial is executed at eight hectares grassland on sandy soil at Hardenberg, the Netherlands. Two strips were treated with a regular rate of manure and two strips with variable rates of manure. In 2015, results showed that variable rate based on yield of the previous cut, did not result in a higher yield (Nysten et al., 2016). In 2016 the knowledge about the growing conditions (such as groundwater table, nutrition level, weather etc.) and the potential yield for the different cuts (both spatial and temporal) were considered in the fertilization strategy. Locations with a higher potential yield received a higher rate of manure prior to that cut.

## References

Nysten, S.W.P., Westerdijk, C.E., Kocks C.G., Kempenaar C., 2016. Showcase Hardenberg (NL): effect of variable manure rate applications on grass yields. Grassland Science in Europe Vol. 21 – The multiple roles of grassland in the European Bioeconomy, pag 238-241

**keywords:** Smart Farming, GPS, Grass yields, Variable manure rate application
Spatial prediction model applied to digital soil mapping of the
Marinheiro stream watershed, Sete Lagoas (Brazil)

Amanda Ribeiro De Oliveira – UNIVERSIDADE FEDERAL DE MINAS GERAIS, Brazil

Adriana Monteiro Da Costa – UNIVERSIDADE FEDERAL DE MINAS GERAIS, Brazil

João Herbert Moreira Viana – Embrapa, Brazil

The information coming from soil surveys, such as the physical and chemical properties and the spatial distribution, is essential for the management and the land use planning, for the optimization of urban and rural expansion, and for environmental preservation policies. Due to the growing demand for detailed soil surveys, Digital Mapping Soil techniques emerge as an important set of tools for the prediction and distribution of soils, which allow to optimize the mapping services, making them faster, replicable and economically more viable. In this context, the aim of this study was to develop and to evaluate the performance of a soil prediction model (SPM) for the Marinheiro stream watershed, Sete Lagoas (Brazil). The proposed model was created from the “Weights of evidence” method, implemented in the open source software Dinamica EGO. The sampling input was composed of 42 soil profiles and 157 field checking points, all of them analyzed and described in fieldworks. Probability maps were generated for each of the 16 sampled soil classes, which showed high spatial correlation with the landscape. The high probability (over 95%) maps of each class were combined into a single soil map, which resulted in 12 mapping units (MU). The “Latossolos”, “Argissolos”, “Nitossolos” and “Cambissolos” (Brazilian soil classification system’s classes) were the most frequent classes among the MU, with the “Latossolos” class having the largest total area, approximately 50%. When compared visually, the predicted and conventional soil maps showed similarities between the outlines and the distribution of mapping units. However, some soil occurrences in the predicted map did not match to the conventional map, e.g. “Chernossolos” spots of the mapping unit “MX”. The validation of the predicted map was performed by two ways: the first by the method of comparison, overlaying 500 randomly generated points sampled in the predicted to the reference map, and the second, selecting another random set of 27 extra points, which were checked in later fieldworks, and compared to both maps. The results show a concordance of 81.8% and 81.5%, for the first and the second methods, respectively. In general, the digital model performed satisfactory in predicting the spatial occurrence of the soil classes in the watershed, being a promising technique for mapping, optimizing costs and saving time.

keywords: soil survey, weights of evidence, Dinamica EGO, soil mapping units
# Prediction of Soil Carbon Stock in Oxisols of the Eastern Plains in Colombia by VNIR Spectroscopy

Iván Orjuela-Osorio – Universidad Nacional de Colombia, Colombia

Diana Mayorga Lozano – Universidad Nacional de Colombia, Colombia

Yolanda Rubiano Sanabria – Universidad Nacional de Colombia, Colombia

Jesús Hernán Camacho-Tamayo – Universidad Nacional de Colombia, Colombia

Aquiles Enrique Darghan Contreras – Universidad Nacional de Colombia, Colombia

Carbon content is a key indicator of soil quality and ecosystem functions. Changes in land use influence soil carbon (SC) content and accumulation of carbon dioxide (CO2) in the atmosphere. SC content monitoring is used to record long-term changes due to anthropic actions. During the last decades evidence of substantial opportunities for the soil organic carbon (SOC) sequestration has been found in the soils of the Eastern Plains of Colombia. It is necessary to have reliable data to generate the quantitative and precise evaluation of the soil carbon stock (SCS), which is useful as a "baseline" to estimate the total C content. However, this tracking requires a high investment in laboratory analysis.

The use of sensors and diffuse reflectance spectroscopy is an increasing alternative to provide spatial, reproducible distribution, with efficient resource management, to determine SCS at appropriate regional scale. This work aim was predict the C content in Oxisols of the Eastern Plains of Colombia by visible plus near infrared spectroscopy (VNIRS) and calibrate and validate a reliable SC prediction model that allows monitoring and develop methodologies for an continuous and efficient monitoring of SCS in these soils.

A rigid network sampling system was use at the Carimagua Experimental Station of the Colombian Research Corporation - Corpoica (04°34'25"N and -71°20'15"W), in an area of 5100 ha. Samples were taken in horizons A and B. The main soils are Typic Hapludox. Soil was dried at 40°C until constant moisture, and screening in mesh 2 mm. Waste plants were removed.

The total carbon content was determined by elemental analyzer (CN TruSpec Nitrogen Carbon Determinator, LECO co., St. Joseph, MI, USA). Soil Carbon range determinated (%C) was: in the A horizon 1,19 to 2,25 and in B horizon 0,49 to 1,4. The spectral responses were acquired in laboratory by sensor ASD Pro FieldSpec 350-2500 nm (Analytical Spectral Devices Inc.), with 64 scans and spectral resolution of 1 nm for the spectral region of 355-2500 nm. The regression algorithm used was Partial Least Square Regression (PLSR).

The accuracy and precision assessment of SC prediction model was R2: 0.880; RMSE: 0.185(%C); and RPD 2.90. The model predicted at least 70% of SCS analytically determined in A and B horizons. There is a positive correlation between the closeness to the drainage network and the predicted C content. Negative correlation was determined with the slope value, indicating that there may be movement of material that favors carbon accumulation near the drainage network. The results of this research can be used as a base for the SC accumulation monitoring in this area of the country.

**keywords:** Soil Carbon Prediction, Model Prediction, Soil Monitoring, VNIRS, Oxisols
# Estimating regression parameters in the presence of spatial and temporal correlation: A case study to quantify costs of soil constraints to the Australian grains industry

Thomas Orton – University of Queensland, Australia

Matt Pringle – Queensland Government, Australia

Thilak Mallawaarachchi – University of Queensland, Australia

Neal Menzies – University of Queensland, Australia

Yash Dang – University of Queensland, Australia

Soil salinity, sodicity, acidity, elemental toxicities and compaction are important soil constraints to agricultural sustainability in many soils of Australia. There is considerable variation in the existing information on the costs of each of the soil constraints to Australian agriculture. To quantify the costs of forfeited grain yield due to soil constraints, we have fitted models that predict yield across Australia as a function of climate and soil. The type of regression model we fit is a model tree, based on the Cubist software (i.e. a collection of multiple linear regression models, each fitted on a different subset of the data); this approach was selected because of its ability to deal with complex interactions and non-linear relationships. An important source of error in the model is the lack of management data, which coupled with the irregular soil data locations mean that it is important to consider the structure of the dataset (the spatial and temporal sampling locations) when fitting parameters. The collection of multiple regressions from a model tree can be written as a linear fixed-effects function in the framework of a linear mixed model. Therefore, these regression parameters can be fitted while accounting for the correlation of residuals, specifically through the restricted maximum likelihood (REML) method. We investigate the effect this has on the fitted parameters and the importance this might have on predictions derived from the model. We apply the methods to our case study and present maps showing the cost of lost yield due to acidity and sodicity across Australia. This work is presented in a model fitting framework, but has relevance to regression kriging where some form of regression tree is assumed to give the trend, as is often the case in digital soil mapping studies.

**keywords:** parameter estimation, regression, Cubist, spatial correlation, soil constraints
DSM online service: from the soil to the cloud

José Padarian – The University of Sydney, Australia

Budiman Minasny – The University of Sydney, Australia

Alex McBratney – The University of Sydney, Australia

A digital soil mapping exercise over a large extent and at a high resolution, is a computationally
expensive procedure. It may take days or weeks to obtain the final maps and to visually evaluate
the prediction models when using a desktop workstation. To increase the speed of time-consuming
procedures, the use of supercomputers is a common practice, but managing the big amount of
raster data typically used, and implementing the code is not trivial, and access to the required
infrastructure is not always possible. Google has developed a product specifically designed for
mapping purposes (Earth Engine), allowing users to take advantage of its computing power and
the mobility of a cloud-based solution. We explore the feasibility of using this platform for digital
soil mapping and we delineate what an application specifically designed for DSM would need to
become a fully functional cloud-based soil mapping solution.

keywords: *Cloud Computing*, *Digital Soil Mapping*, *Big Data*
Predicting soil organic carbon in Ap horizons in Sistan region,
eastern Iran

Mohammad Reza Pahlavan-Rad - Soil and Water Research Department, Sistan Agricultural and Natural Resources Research and Education Center, AREEO, Zabol, Iran, Iran

Ali Shahriari - Dept. of Soil Science and Engineering, University of Zabol, Zabol, Iran, Iran

Mojtaba Hadizadeh - Soil and Water Research Department, Sistan Agricultural and Natural Resources Research and Education Center, AREEO, Zabol, Iran., Iran

Kamran Eftekhari - Soil and Water research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran., Iran

Colby Brungard - New Mexico State University, USA., USA

Understanding spatial distribution of soil organic carbon content (SOC) in the arid areas is necessary for the management of these regions. In the present study, the distribution of SOC was investigated using digital soil mapping for an area of ~ 227,000 ha in Sistan region, Sistan and Baluchestan province, eastern Iran. The study area is a flood plain with a slope of 1-2% and is an arid region with mean annual precipitation of about 65 mm. It has intense winds and high wind erosion in the springer and summer. 163 soil samples were collected from Ap horizon at the different soil series. 24 environmental variables were used in modeling and 8 variables were selected in the final models. The random forest technique was used to link between environmental variables and SOC. 10-fold cross-validation was used to evaluate models. Results showed that the mean soil organic carbon value was very low with a value of 0.52% in the study area. The most important environmental variables were channel networks, elevation and clay content, aspect and valley depth. The optimal values of RMSE and R2 of the estimated map were obtained 0.23, and 0.17, respectively. The predicted map indicated a general feature from the soil organic carbon status in the Sistan region.

keywords: Flood plain, Digital soil mapping, Random Forest
Digital mapping of soil salinity in eastern Iran

Mohammad Reza Pahlavan-Rad - Soil and Water Research Department, Sistan Agricultural and Natural Resources Research and Education Center, AREEO, Zabol, Iran., Iran

Ali Reza Akbari Moghaddam - Soil and Water Research Department, Sistan Agricultural and Natural Resources Research and Education Center, AREEO, Zabol, Iran., Iran

Khodadad Dahmardeh - Soil and Water Research Department, Sistan Agricultural and Natural Resources Research and Education Center, AREEO, Zabol, Iran, Iran

Colby Brungard - New Mexico State University, USA., USA

Soil maps are the major sources of information for land management, natural resources and environmental. Soil salinity is one of the most important factors of limiting product in Sistan plain, eastern Iran. Digital soil mapping can be used for predicting soil properties such as soil salinity. In this study, 460 soil samples were collected from 0-30 cm depth on a 750 m grid in 41000 ha from the lands of Zahak of Sistan plain and then the electrical conductivity of saturation paste was measured. 361 samples were used for training and 99 for testing. Random forest technique was used to link between soil salinity (EC) and environmental covariates. Different terrain analyses were derived from digital elevation model (DEM) map and NDVI and SI were extracted from the satellite images. The results showed that soil salinity ranges from < 4ds/ m to > 32ds/ m in the study area. NDVI, aspect and SI were the most important covariate in the prediction of soil salinity. RMSE and ME were 10 and -0.3 for data train and 19.5 and -4.1 for data test, respectively. Produced map showed that soils close to the river had lower salinity and soil salinity increased with distance from the river. The results showed that produced map corresponds with the facts in the study area. The use of other covariates in modeling that do not use in this study can be produced a higher accurate map.

keywords: Low Relief, Random Forest, Dry region, Sistan
Accounting for the measurement error to improve the accuracy
of spatial modelling of soil carbon

Sanjeewani Nimalka Somarathna Pallegedara Dewage – University of Sydney, Australia

Budiman Minasny – University of Sydney, Australia

Brendan Malone – University of Sydney, Australia

Uta Stockmann – University of Sydney, Australia

Alex McBratney – University of Sydney, Australia

Infrared soil spectroscopic data is considered as a timely, low-cost input for spatial modelling of soil carbon. However these data are associated with a comparatively higher measurement error than the standard dry combustion technique. Geostatistics is centred on building models of spatial dependence and optimal prediction of the spatial process. This optimal prediction can be hindered by such measurements errors. Therefore, it is vital to have a mechanism to filter out the measurement error variability for an optimal prediction. This study establishes a methodology to filter out the measurement error variability through the inclusion in the covariance structure of the spatial model. We investigated the applicability of residual maximum likelihood (REML) and Markov Chain Monte Carlo (MCMC) simulation methods to generate parameters of the Matérn covariance function directly from the data in the presence of measurement error. The study was carried out in the lower Hunter Valley, New South Wales, Australia where a combination of laboratory measured, and vis-NIR and MIR inferred soil carbon data on topsoil and subsoil are available. The results revealed that the measurement error can be effectively recognised through the inclusion in the covariance structure of the spatial model. When the measurement error was included in the spatial model, the prediction variance was almost halved. Therefore, the results ultimately yielded a greater certainty in spatial predictions of soil carbon content when the measurement error was acknowledged. Further, the MCMC technique was successfully used to define the posterior distribution of measurement error. This is an important finding, as the MCMC technique can be used to estimate the measurement error if it is not explicitly quantified.

**keywords:** Infrared spectroscopy, measurement error, variogram, REML-EBLUP, MCMC, filtered kriging
Monitoring of salt content in soil profile by hyper spectral imaging
spectroscopy

Xianzhang Pan - Institue of Soil Science, Chinese Academy of Sciences, China

Shiwen Wu - Institute of Soil Science, Chinese Academy of Sciences, China

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

Visible and near infrared spectroscopy is a critical technology to predict soil attributes, how-
ever, when monitoring the changes of soil properties in soil profile, it is not a good choice to use
those spectrometers based on sampling point. In order to acquire the vertical changes of soil salt
content under evaporation condition, in this study, the ImSpector, a direct sight imaging spectro-
graph, was used to collect the spectral reflectance of saline soil profiles. The external parameter
orthogonolization (EPO), a spectral preprocess method, was incorporated in the calibration model
to remove moisture effects on soil spectra. The results showed an improved prediction of soil salt
content for soil profile after EPO. In conclusion, the hyper spectral image was an effective tool to
monitor the changes of soil profile.

keywords: soil salt content, soil profile, imaging spectroscopy, external parameter orthogonoliza-
tion
DSM based renewal of the Hungarian Soil Spatial Data Infras-
tructure

László Pásztor – Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Hungary

Annamária Laborczi – Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Hungary

Gábor Szatmári – Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Hungary

Katalin Takács – Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Hungary

Gábor Illés – National Agricultural Research and Innovation Centre, Forest Research Institute, Hungary

Zsófia Bakacsi – Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Hungary

József Szabó – Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Hungary

Present soil data requirements increasingly demand advanced or new kinds of spatial soil infor-
mation, which cannot be fully satisfied by legacy soil maps or formerly elaborated spatial databases.
Several national programs have emerged recently in Hungary, whose successful completion necessi-
tates the application of suitable data, which need to be spatially exhaustive and consistent as well
as both globally and locally reliable. Also, accurate soil information is needed not only for primary
soil properties, but for specific basic and or higher level soil features, which formerly had not even
been mapped. The DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in
Hungary) project was started intentionally for the renewal of the national soil spatial infrastruc-
ture aiming to significantly extend the potential how countrywide soil information requirements
could be satisfied.

The predictand soil properties are selected on thorough demand assessment and then are multi-
mapped by the aid of selected sets of reference data, spatial auxiliary information and inference
methods. Optimized products are achieved by iteration, which is based on the results of accu-
racy assessment supplemented with the evaluation of the implemented resources. Reference soil
information on target variables comes from survey originated, typically legacy data sources. The
relevant auxiliary environmental variables, were selected partly to characterize local soil formation,
partly to provide direct information on the surface and certain indirect information on subsurface
conditions ruled by the predicted soil features. Spatial inference has been carried out using a variety
of geostatistical and machine learning tools: regression kriging, stochastic simulation, classification
and regression trees, random and quantile regression forests. The uncertainty of prediction has
been also modelled by detailed accuracy assessment targeting both global and local uncertainty.

The main result of DOSoReMI.hu is a collection of spatial soil information in the form of
unique digital soil (related) map products, which were optimally elaborated for the regionalization
of specific soil features. Significant part of them were never mapped before, even nationally with
high spatial resolution. Based upon the collected experiences, the full range of GSM.net products
is also targeted to contribute to the worldwide activities. Based on indigenous data infrastructure,
it is suggested that national initiatives could produce more accurate and reliable products. It
is hoped to achieve further progress in the performance by expanding the pool of environmental
co-variables and by testing additional methods and combining some of them. The web publishing
of the results has been also elaborated.
keywords: DSM, GlobalSoilMap, spatial data infrastructure
# Soil, scale dependence and spatial variability: A new approach for assessing how soil variability changes with scale.

Stacey Paterson – Sydney University, Australia

Budiman Minasny – Sydney University, Australia

Alex McBratney – Sydney University, Australia

Understanding how soil variability changes with scale is critical to soil scientists' ability to model and manage the soil resource. Improving our understanding of the link between spatial scale (e.g. resolution, extent, support, and spacing) and variability can improve efficiency in soil survey, accuracy of mapping, up and downscaling, and our understanding of how much variability is captured at any given scale.

This study aims to investigate how soil variability changes with spacing and extent. Using a legacy dataset with soil texture observations spanning continental Australia we calculated variograms at different scales. We changed the extent and bin sizes to model spatial variability at scales from a few meters to the entire continent. We calculated the Hausdorff Besicovitch Dimension (D value). This provided us with a measure of roughness that is readily comparable between scales and properties. We first calculated this dimension from distinct variograms modelled at different scales using the ‘variogram method’. We also trialed an alternative method which allows the calculation of a continuous D value. This method calculated a continuous D value from a ‘composite variogram’ which combined variograms from multiple scales.

When applied to Australian soil texture data, we found that soil is highly variable at short scales and that roughness or spatial variability decreases over smaller areas at finer scales. We find that as scale changes, the D values change gradually, rather than abruptly. We extend this analysis to consider other environmental properties and regions.

**keywords:** variogram, soil texture, scale, fractal
Comparison of Mid and Near Infrared Spectroscopy for Prediction
of Soil Properties for a National Spatial Dataset

Estefanía Pérez-Fernández – The James Hutton Institute, United Kingdom

Angela Main – The James Hutton Institute, United Kingdom

Jean Robertson – The James Hutton Institute, United Kingdom

Soil monitoring, and the modelling of soil data in time and space, rely on a good source of accurate data. In the case of soil, a highly complex and variable matrix, data also need to be abundant in order to accurately represent such variability. As an alternative to tedious and expensive conventional soil laboratory analyses, infrared (IR) spectroscopic techniques have increasingly been used to obtain accurate estimates of chemical and physical parameters of soil in a quick and cost-effective manner. The two main regions of the IR spectrum, near (NIR 750-2500 nm) and mid infrared (MIR 2500-25000 nm), can provide interrelated chemical information about a soil sample, each with different capabilities. The less demanding sample preparation and ease of use of NIR spectroscopy has made this technique more popular for soil monitoring purposes. In combination with predictive calibrations, NIR spectroscopy has been widely used to assess various soil properties –typically moisture, texture, and nutrients. There has been significantly less use made of MIR spectroscopy, which has often been used for qualitative interpretation purposes rather than for prediction of soil properties. Nonetheless, calibrations using MIR data have also been reported, and in many cases they have proved more accurate than NIR calibrations for estimation of certain soil parameters. Such improvements may be due to the extra fundamental information contained in the MIR spectrum, which compared to NIR, provides a more detailed overview of the chemical, mineralogical and, in turn, geological characteristics of soil.

The aim of this study is to investigate the benefits of using MIR spectra, if any, to produce predictive calibrations for simultaneous estimation of multiple soil properties and therefore advance towards the development of this fast and cost-effective tool for soil monitoring. We used MIR spectra of soil samples from a national scale spatial dataset, collected between 2007-2009 across Scotland on a 20 km grid, and that covers a wide range of soil types and land uses. Known reference values for an extensive collection of chemical and physical soil properties were available for these samples. We report results from multivariate calibrations developed using some of this data and compare them against NIR calibrations already available for the same dataset and soil parameters. Results are discussed from a comparative point of view, reflecting the strengths and limitations of each technique in terms of spectral information, accuracy and number of parameters successfully predicted with either technique.

keywords: mid infrared spectroscopy, near infrared spectroscopy, calibration, soil monitoring
# Combining inventory data with ancillary datasets to predict forest soil organic carbon

Charles Perry – USDA Forest Service, USA

Grant Domke – USDA Forest Service, USA

Luke Nave – University of Michigan, USA

The national forest inventory (NFI) of the United States has worked over the past several years to replace estimates of carbon (C) stocks obtained from country-specific models with estimates leveraging field observations. Early work focused upon standing and downed dead wood, and the newest report to the international community includes field-based estimates of C stored in the forest floor and the mineral and organic soil. Previous estimates of forest floor C stocks employed region- and forest type-specific coefficients describing net C accumulation based on the assumption that litter C mass increased with age. Earlier estimates of soil organic C (SOC) stocks relied upon correlations between forest communities and soil map units (i.e., the State Soil Geographic (STATSGO) and Soil Survey Geographic (SSURGO) databases) that were populated generally by expert opinion across forested landscapes as systematic soil sampling traditionally focuses upon agricultural lands; changes in these stocks were modeled as a response to changes in forest structure. We developed the new models using machine learning (i.e., RandomForests) to assess a suite of potential predictors. For the forest floor, the final models included elevation, longitude, aboveground biomass, the ratio of precipitation to potential evapotranspiration, forest-type group, mean annual maximum temperature, and latitude. The best model of SOC in the mineral soil continues to use soil map unit descriptions as categorical predictors (i.e., soil order), but it also includes a new set of region- and site-specific predictors to improve accuracy: latitude, longitude, elevation, forest-type group, precipitation, mean annual maximum temperature, the ratio of precipitation to potential evapotranspiration, soil order, and surficial geology. We also developed generalized depth models (by soil order) to facilitate estimates of forest SOC across depth. Our new estimates increased the relative size of the SOC pool from 44% to 56% of the total forest C stock. Perhaps most noteworthy, our revised, field-based estimates of forest floor C reduced the stock by 44% (2081 ± 77 Tg), suggesting that defaults in the Intergovernmental Panel on Climate Change Guidelines for National Greenhouse Gas Inventories may be substantially overestimating the contributions of this pool in temperate forest ecosystems.

keywords: forest soils, soil organic carbon stocks, inventory, estimation, machine learning
# The Interactive Digital Soil Map of Sweden - a free web application for downscaling

Kristin Piikki - Swedish University of Agricultural Sciences (SLU), Skara, Sweden, Sweden

Mats Söderström - Swedish University of Agricultural Sciences (SLU), Skara, Sweden, Sweden

Henrik Stadig - Hushållningssällskapet Skaraborg, Sweden

Johan Martinsson - Dataväxt AB, Sweden

The Digital Soil Map of Sweden (DSMS) is a public soil geodatabase covering > 90% of the arable land in the country. It contains e.g. a 50 × 50 m2 raster of topsoil clay content that has been predicted from fusion of detailed airborne gamma radiation measurements, airborne laser-scanning data of elevation and Quaternary deposit maps. A free online decision support system (DSS; markdata.se) makes the DSMS clay content data available for free for fields selected by the user and makes it possible to design and export prescription files for e.g. variable rate seeding or liming. This type of free DSS has rendered a large interest among farmers. However, it is also common that farmers already have results from their own soil analyses, and it is of interest to use these soil analyses in combination with DSMS.

Therefore, the aim was to develop an interactive function within DSMS with which local soil analyses can be uploaded. Automatic modelling of DSMS data in combination of uploaded soil data was tested. In the present study, we compared the accuracy (cross validation mean absolute error, MAE) of the following clay content maps for 403 farms:

* Map only: DSMS.

* Map + samples: DSMS locally adapted by residual kriging.

* Map + samples: DSMS locally adapted by regression kriging.

* Samples only: Map derived by inverse distance weighting interpolation

In 31% of farms, the DSMS was better (had a smaller MAE) than maps obtained by IDW. The opposite was true for 15% of the farms. In the remainder of the farms, the most accurate map was obtained by local adaptation/downscaling the DSMS by residual or regression kriging using the farmers' own samples. Consistently choosing the best method for each farm, significantly (p < 0.001) reduced the MAE of produced soil maps compared to consistent usage of a specific method.

In the developed web tool, farmers can upload laboratory results and DSMS is currently locally updated by residual kriging, using an automated procedure with a standard variogram model circumventing the general principles of the number of observations generally required in geostatistics. The MAE of all mapping methods are presented, and the user can choose the one with lowest MAE, or the one with a preferential spatial variation pattern – i.e. the one they think look nicest.

The DSMS is published under CC-BY licence by the Geological Survey of Sweden (SGU).

**keywords:** Downscaling, Digital Soil Map, Clay content, Precision Agriculture, Decision Support System
# Soil Microbial Diversity Across Different Agroecological Zones in New South Wales (NSW)

Vanessa Pino – The University of Sydney, Australia

Alex McBratney – The University of Sydney, Australia

Neil Wilson – The University of Sydney, Australia

Mario Fajardo – The University of Sydney, Australia

A synergistic relationship between soil (pedodiversity) and microorganisms (biodiversity) is emerging as never before. Understanding this relationship will enable us to improve and protect crucial biogeochemical processes which underpin soil ecosystem services. We explored the multidimensional disposition of this biotic-abiotic functioning by modelling the biogeographical patterns of soil microbes using biomolecular sequencing, pedometrics and digital mapping approaches. Understanding the main factors controlling soil microbial spatial patterns is a need that has been recognised in many scientific disciplines, from the molecular to the environmental sciences. From a soil scientific perspective, we hypothesise that the extent of soil microbial diversities relies on basic but multivariate units defining soils (e.g. soil horizons, profiles classes) instead of any single discrete ‘factor’ (e.g. soil pH). In this research, we have evaluated this hypothesis analysing bacterial, archaeal and fungal communities at a large spatial extent along latitudinal and longitudinal environmental gradients of about 900 km each across NSW, Australia. Soil microbial and physicochemical attributes, obtained from 98 sampling sites using Illumina DNA sequencing (11,557,499 sequences; 423,740 OTUs) and pedometric approaches (19 soil properties; 13 environmental covariates), were analysed from linear to more complex multivariate associations. We first studied the local diversity (-diversity) of paired conserved and disturbed (e.g. cropping) ecosystem sites. This enabled the spatial prediction and mapping of the three microbial kingdoms at a resolution of 1 km across all of NSW. These maps showed microbial diversity following a combination of soil and environmental attributes in which western NSW showed larger microbial diversity than eastern NSW. Despite this gradient, fungi and archaea were consistently lower and higher in Vertosols (Australian Soil Classification System), respectively, whereas the distribution of bacteria is less clear. Our results suggest that microbial -diversity is intimately related with most physicochemical soil attributes but this association, whether linear or not, varies not only upon the microbial taxa but also because of other additional soil factors. Therefore, microbial diversities are more consistent with grouped features defining soil entities (e.g. horizons and profiles classes) rather than on individual soil factors. Further work will include a more detailed analysis of microbial/pedological dissimilarities (e.g. -diversity) in a multiscale approach.

**keywords:** Soil biodiversity, Biopedometrics, Soil microbiology
Spatial modelling of landscape heterogeneity in soil moisture con-
tent with the assimilation of optical and radar remote sensing data

Laura Poggio – The James Hutton Institute, United Kingdom

Alessandro Gimona – The James Hutton Institute, United Kingdom

Allan Lilly – The James Hutton Institute, United Kingdom

Soil moisture is a major factor influencing ecosystem processes and it is therefore important to both understanding and modelling ecosystems at a range of spatial scales. Given its ecological significance and ability to account for the heterogeneity in several observed patterns, it is important to determine the spatial and temporal heterogeneity of the moisture characteristics of soils at the landscape scale. Soil moisture is difficult to measure over regions and remotely sensed soil properties could be used as an indicator. Recent radar data such as Sentinel-1 provide useful information to model soil hydraulic properties at national and regional scale especially when combined with other optical remote sensing data and with morphological information.

This study models soil hydraulic properties such as field capacity (FC) and wilting point (WP) for topsoil layer in mineral soils of Scotland. Relatively few measured locations are available: 67 from the National Soil Inventory of Scotland and 221 from other experiments. This is not a sufficient spatial distribution and variability for further modelling with remote sensing data. Therefore a Generalized Additive Model (GAM) was built to obtain FC and WP values at further locations using other available soil properties as covariates, such as organic matter content (loss on ignition), pH and soil texture. The obtained values were used to spatially model FC and WP. The approach used was scorpan-kriging with GAM for the trend and kriging for the residuals.

The results show a good spatial structure and relationships with the landscape. The use of radar data improved the model when compared to only optical and morphological information. The use of radar data allowed also an initial assessment of seasonality of soil moisture in Scotland. These information are important for the modelling of soil functions and ecosystem services, especially in the context of climate change and potential improvement of soil quality and productivity.

keywords: soil water content, scorpan-kriging, sentinel data, uncertainty
Using in situ Vis-NIR combined with other sensing data to map
clay content, soil organic carbon, and bulk density at the field
scale

Matteo Poggio – Washington State University, USA

David J. Brown – Washington State University, USA

Caley K. Gasch – North Dakota State University, USA

Yuanhong Song – Washington State University, USA

Raphael Viscarra-Rossel – CSIRO, Australia

Craig Lobsey - CSIRO, Australia

While there has been substantial research on the use of proximal soil sensors for digital soil mapping, questions remain as to how data from multiple sensors with different sampling densities and interrogation depths can best be combined statistically to generate 3D soil maps. In this study, we used a combination of soil Vis-NIR spectra and insertion force collected with a penetrometer foreoptic, apparent electrical conductivity (ECa), Normalized Difference Red-Edge index (NDRE) and terrain indices to predict clay content, bulk density and soil organic carbon SOC across three agricultural fields in northern Idaho. With a hydraulic push-type soil coring systems for insertion (e.g. Giddings), we collected soil spectra and insertion force data along 41m x 41m grid points (2 fields – around 140 locations) and 50m x 50m grid points (1 fields – 152 locations) to 80 cm depth (10 cm step increment) for bulk density and clay content and to 30 cm depth for SOC (10 cm step increment). Based on terrain indexes, we stratified the fields and randomly selected 36 locations per field outside the grid points (12 for calibration and 24 for validation purposes). Next to the 36 points, we extracted two set of calibration cores. To generate soil maps, we applied three fundamentally different approaches. First, we developed a chemometric calibration of penetrometer data to predict clay content bulk density and SOC profiles at regular interrogated grid points. These predicted profiles were then interpolated with 3D and 2.5D regression-kriging, using NDRE, ECa and terrain indices as covariates. Second, we used 3D ordinary kriging to interpolate grid penetrometer VisNIR and insertion force measurements, then used these exhaustive data layers combined with NDRE, ECa and terrain indices in a regression-kriging framework to map the soil target properties. For the regression component in the hybrid interpolation technique, we adopted to different tree algorithms, the Random Forest algorithm and a combination of CUBIST algorithm with bootstrap technique. Finally, we used co-kriging to predict target soil properties using grid profile measurements of VisNIR and insertion force, combined with spatially exhaustive ECa and terrain indices.

keywords: soil spectroscopy, precision agriculture, digital soil mapping
# Geophysical mapping of wetlands using DUALEM - challenges and possibilities

Christian Prinds - Aarhus University, Denmark

Rasmus Jes Petersen - Aarhus University, Denmark

Henrik Nørgaard - Aarhus University, Denmark

Mogens Greve - Aarhus University, Denmark

Bo Vangsø Iversen - Aarhus University, Denmark

How do we easily, cheaply and efficiently measure major flow path ways from agricultural fields to lowlands and wetlands? - an intriguing question and a somewhat hot topic in the public discussion in Denmark at the moment.

The Danish agricultural community will in few years need to substantially reduce the nutrient load from fields to fresh water bodies in order to comply to regulations of the European Water Framework Directive and the natural health of wetland ecosystems. Therefore, Aarhus University together with Copenhagen University and GEUS have joined forces in the TReNDS project where the path ways of drainage water are investigated and here natural wetlands may come into play as the perfect nitrate reduction site due to the presence of organic rich sediments e.g. peat. Natural wetlands are a common feature in the Danish landscape as places of converging water conduits (in the shape of streams, drains, ditches, diffuse flow, and groundwater discharge). Therefore, solid knowledge about major flow path ways in wetlands are of immediate concern.

The present study does not answer the whole question but digs into the possibilities of using fast ground conductivity meters (DUALEM421) for accessing relevant soil features in wetlands and the upland/wetland interface such as soil texture, location of organic sediments, presence of high permeability layers, and location of drains.

Although the equipment seems ideal for mapping shallow soil properties, we face significant challenges when using DUALEM in wetlands. These include practical issues when employing vehicles and sensitive measurement devices into rough topography and soft and periodically inundated soils. Also, soil parameters such as lithology, temperature, and water content affects the conductivity measurements and the penetration depth of the equipment in different ways. In our study, conductive organic layers such as peat and gyttja cause a low penetration depth, hence certain areas hold only little information and inversion results may have significant uncertainty.

Data is inverted using Aarhus Workbench by Aarhus GeoSoftware.

keywords: GCM, DUALEM, Workbench, Mapping, Wetlands, nitrate
Soil agrochemical monitoring – source for country-scale predic-
tions and fertilization optimization

Elsa Putku – Agricultural Research Centre, Estonia

Priit Penu – Agricultural Research Centre, Estonia

Tambet Kikas - Agricultural Research Centre, Estonia

The soil agrochemical monitoring, also called soil fertility programme is the most large-scale
monitoring programme in Estonia. This monitoring is aimed at identifying the status and changes
of soil properties and determining the fertilization requirements of arable soils. Most of the arable
soils in the fertility programme are under CAP subsidies which support environment friendly man-
agement and organic production. In 2015 there were 2923 applicants for these support measures.
One of the requirements for getting this support is taking soil samples from their fields (except
for permanent grassland) with five year interval. General scheme of the soil fertility programme
is taking an average soil sample, consisting of 25-40 subsamples, for the area of 5 hectare. It's
mandatory to determine available content of potassium and phosphorus as well as actual acidity
in soil. Additionally, soil organic carbon concentration (SOC, %) has to be determined for a field.
Thus, the collected data since 2002 are large (over 300 000 samples) and provides solid basis for
further developments.

Optimizing fertilization is one of the key aspects that interests farmers and is also important in environmental aspect. Currently, Agricultural Research Centre (ARC) provides fertilization requirement maps for the farmers participating in the soil fertility programme. Then the farmer uses this information in planning their fertilization. The next step for the ARC would be providing the farmers with digital fertilization plan depending on the culture and soil properties, i.e., automated and spatial support system. First attempts achieving this goal have been made. Additionally, SOC (%) data provides a basis for regional prediction of soil organic carbon in arable soils. Previous attempts of predicting SOC (%) all over Estonia were complicated due to the limited spatial coverage of the soil samples. In soil fertility programme, the data are scattered over the country. Therefore, the aim was to develop a prediction approach for soil type-specific SOC (%) of arable soils. The predictions are based on the Estonian large-scale soil map and soil fertility programme data using geostatistical methods. Results can be used in e.g., optimizing site-specifically nitrogen fertilization. This brings actual decision-making to farmers much closer and is not restricted to soil sampling in laboratory.

keywords: legacy soil data, prediction models, soil organic carbon, fertilization
Digital soil mapping with Soil Land Inference Model (SoLIM)
considering the spatial distance to soil samples

Cheng-Zhi Qin - State Key Laboratory of Resources &amp; Environmental Information System,
Institute of Geographic Sciences &amp; Natural Resources Research, CAS, China

Yi-Ming An - State Key Laboratory of Resources & Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, CAS, China

A-Xing Zhu - State Key Laboratory of Resources & Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, CAS, China

The Soil Land Inference Model (SoLIM) primarily proposed by Zhu et al. (1997) has been
widely applied to digital soil mapping. With this model, the soil property value of each interest
locations is estimated based on the similarity of environmental condition between the interest
location and soil samples. Therefore, current SoLIM infers in attribute domain of environmental
covariates (such as slope gradient, topographic wetness index, and parent material) and ignores
the spatial distance between the interest location and soil samples. In this study we revised current
SoLIM to consider the spatial distance to soil samples during predictive soil property mapping.
This revised model is based on an assumption that the more similar environmental conditions and
the shorter distance between an interest location and a soil sample the more similar soil property
values will be. We tested the revised SoLIM with soil organic matter mapping in a low-relief
watershed. Experimental results showed that the SoLIM can get lower prediction error when the
spatial distance to soil samples was considered.

keywords: digital soil mapping, Soil Land Inference Model (SoLIM), spatial distance
High resolution land-use classification toward more accurate digital soil mapping of malagasy soils.

Nandrianina Ramifehiarivo – Laboratoire des RadioIsotopes- University of Antananarivo, Madagascar

Herintsitohaina Razakamanarivo – Laboratoire des RadioIsotopes- University of Antananarivo, Madagascar

Michel Brossard – IRD, French Guiana

Mamy Ravalontsalama – IRD (UMR Espace-dev), France

Eleneide Doff Sotta – EMBRAPA, Brazil

Tantely Razafimbelo – Laboratoire des RadioIsotopes- University of Antananarivo, Madagascar

Martial Bernoux – FAO, Italy

Malagasy soil map of 1:200,000 only cover 20% of the national area and the remaining larger-scale studies don't cover more than 2% of the territory. Advanced research on soils showed that land-use map is an important tools for soil mapping. This study aimed to develop an updated high-resolution land-use map with remote sensing products for an accurate mapping of Malagasy soils. Landsat 8 OLI and SPOT 5 images of the study area (3,600 km² in the Ankeniheny-Zahamena Corridor, Eastern Madagascar) were used for mapping six studied land-uses including Water (W), Closed Canopy Forest (F), Tree Fallow (TF), Shrub Fallow (SF), Degraded Land (DL) and Bare Soil (BS). Automatic classification by Baraldi algorithm associating expert classification using Normalized Difference Water Index (NDWI) was applied on the Landsat Image with R and Monteverdi softwares. For SPOT5 image, an Oriented Object segmentation combined with hierarchical classification using vegetation and soil indexes was considered with E-cognition software. Results showed that with 199 validation points, precision of land-use map is 80% for Landsat and 82% for SPOT5 classification. Classification is slightly more accurate for Landsat (90%) than SPOT5 (88%). However, mapping of TF is difficult for both classifications (79%), DL for Landsat (72%) and SF for SPOT5 (70%). As soil properties depended on change in vegetation cover, a better differentiation on fallows areas still needs to be enhanced. However, the present study is an important step forward towards more accurate soil mapping. Automatic classification of land-use map can be useful for monitoring sustainable management of forest and agricultural land.

keywords: Classification, Degraded land, Forest, Land cover, Vegetation Index
# Are data collected to support farm management suitable for monitoring soil indicators at the national scale?

Barry Rawlins – British Geological Survey, United Kingdom

Ben Marchant – British Geological Survey, United Kingdom

Sean Stevenson – NRM Laboratories, United Kingdom

Wendy Wilmer – Rothamsted Research, United Kingdom

National-scale soil monitoring is required to quantify the status and the rate of change of soil indicators and to determine whether policy or management interventions are required to maintain soil functionality. Many countries are without an ongoing national-scale soil monitoring network (SMN). For example, in the UK there is no plan to resample any of the three existing SMNs. The European-scale LUCAS SMN does sample within the UK but not with the intensity required to produce maps of the variation in soil properties with sufficient accuracy and resolution to determine where threats are emerging. We explore the potential to use an alternative source of soils information to monitor soil indicators. Namely, the results of soil analyses requested by farmers to determine the fertiliser requirements for their fields.

NRM laboratories analyse around 350 000 soil samples each year for farmers and agronomists from across England and Wales. We use model-based geostatistical methods to predict maps of soil indicators such as P, K and Mg concentrations and soil pH from these data. The maps are generally consistent with the results of the existing SMNs and where differences do occur they can be explained by differences in analytical protocols. However, in contrast to the LUCAS SMN, the sites where the NRM samples are collected are selected according to the needs of farmers rather than a statistical design. The sampling could be biased in favour of areas where farmers have particular concerns and observed trends in the soil indicators might be the result of the changing priorities of these farmers rather than actual changes in the soil indicators. Therefore, we demonstrate how the data from LUCAS SMN can be harmonised or combined with the NRM data within a linear model of coregionalization. This leads to high-resolution predicted maps of the indicators that are both unbiased and accurate since they benefit from both the unbiased sampling of the LUCAS SMN and the large spatial density of the NRM samples.

**keywords:** Soil Monitoring, Farmers' data, Geostatistics, Linear model of coregionalization, Harmonisation of soil surveys
Using combined model for soil pollution spatial analysis

Zhouqiao Ren - Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, China
Xiaonan Lu - Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, China
Xunfei Deng - Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, China

How to use the limited soil heavy metal survey point data to carry out regional soil pollution
analysis, and provide a decision-making basis for soil management and pollution control has always
been a difficult and hot issue. Various of Kriging and related geo-statistics methods are widely
used in these studies, but little attention is paid to the land situation as the host of soil. However,
different land use and soil pollution of heavy metals in spatial distribution has obvious correlation.
In 2014, to safeguard the country's food safety and create a healthy living environment, the con-
centration of five soil heavy metals (Hg, Cd, Cr, Pb and As) was measured in 120 sampling sites
in Yuhuan, which is a highly developed island county in China, and located in southeast coast of
Zhejiang. In this paper, we propose a combined model of Random Forest and Kriging interpo-
lation to estimate the regional soil pollution by the 120 sampling data. Firstly, Random Forest
was used to estimate the effect of the composition of land use types on the observed variables(5
kinds of soil heavy metals), and the regression model was established between various soil heavy
metal observations and land use types. And then, it is used to predict the value of observation
points and unknown points. Secondly, the Ordinary Kriging interpolation is applied to the residual
value of each observation point to get the 'residual value' of each unknown point, which means
areas to be predicted. Finally, the predicted values of unknown points for each unknown point
are obtained by subtracting the 'residual value' from the forecast value of random forest. At the
same time, we apply ordinary Kriging interpolation directly to the value of each observation point
to estimate the values over the region. In the end, RMSE and R2 were selected to compare the
accuracy among Random Forest, Ordinary Kriging and the combined model. The result shows
that the proposed method can achieve better prediction of the spatial distribution of heavy metals
in soil than independent models.

keywords: combined model, random forest, ordinary kriging, soil pollution, spatial analysis
# Three-dimensional mapping of soil organic carbon (SOC) based on multi-scale digital terrain analysis and data mining in Jiangxi Province, PR China.

Tobias Rentschler – Eberhard Karls Universität Tübingen, Germany

Karsten Schmidt – Eberhard Karls Universität Tübingen, Germany

Peter Kühn – Eberhard Karls Universität Tübingen, Germany

Thomas Scholten – Eberhard Karls Universität Tübingen, Germany

Soils as limited resource are the largest terrestrial sinks of organic carbon (SOC). Spatial and depth related quantification of SOC offers substantial improvements in the delineation of specific soil functions, e.g. soil quality, water retention capacity and nutrient cycling. Previous models of SOC mostly use specific depth increments, so called standard depths, in a two-dimensional way to reproduce a virtual layered, but limited, three-dimensional mapping. Few approaches in soil-landscape modelling, especially in the application of machine learning techniques, utilize spatially modelled depth functions for SOC predictions. In the context of the biodiversity experiment BEF China located in the subtropics a framework based on depth functions and non-linear machine learning techniques, i.e. random forests (RF), multivariate adaptive regression splines (MARS) and support vector machines (SVM), was tested to quantify spatial and depth-related SOC. The initial datasets consist of 67 plot-based mixed samples and 90 subplot-based point samples with five depth increments (0-5, 5-10, 10-20, 20-30, 30-40, 40-50 cm). The workflow is ordered as follows: (i) derivation of descriptive environmental covariates on different scales (in total 290 covariates), (ii) derivation of depth functions at each sample point with 3rd order polynomials and natural logarithmic functions, (iii) spatial modelling of the polynomial coefficients, (iv) comparison of different non-linear machine learning methods and (v) solving the polynomial functions in given depths. The error (RMSE) of the depth functions ranges from 0.1 to 0.17 % SOC with a coefficient of determination (R²) higher than 0.9. The spatial models of the four plot-based coefficients with SVM are better (mean R² of 0.32) than MARS (0.31) and RF (0.28). The mean of RMSE is 0.22 (RF), 0.23 (SVM) and 0.25 (MARS). Models of the point-based samples have a R² of 0.2 and RMSE of 0.35 in average. Therefore, SVM is used for the final prediction. Models that combine both samples are more uncertain (R²≤0.14). On the one hand this may be due to the spatial clustering of the point-based samples, on the other hand we suppose that the averaging of the plot-based samples reflects different scales of measurement and therefore should not be used complementary with the point-based samples.

**keywords:** Soil organic carbon, Depth function, Multi-scale digital terrain analysis, Machine learning, Three-dimensional digital soil mapping
Soil Organic Carbon Content of Central Chile and Its Projection
Under Climate Change Scenarios

Luis Reyes-Rojas – University of Wisconsin-Madison, Department of Soil Science, USA

Kabindra Adhikari – University of Wisconsin-Madison, Department of Soil Science, USA

Stephen Ventura – University of Wisconsin-Madison, Department of Soil Science, USA

James Bockheim – University of Wisconsin-Madison, Department of Soil Science, USA

Soil Organic Carbon (SOC) is the largest terrestrial carbon pool and it influences a number
soil properties and processes in a landscape. Variation in SOC distribution is primarily influenced
by topography, land use and changes in climatic variables. We estimated SOC content for Central
Chile (147,959 km² area) using 484 pedon data and a range of environmental variables including
temperature, rainfall, land use, topography, soil types and geological information as predictors.
Random Forest was chosen as a prediction model and SOC content maps were created for 6 depths
corresponding to the GlobalSoilMap specifications. Equal area splines were used to harmonize
discrete SOC data from the pedons and values were weighted-averaged to 6 depths. Results
showed SOC of 53 g kg⁻¹ at 0-5 cm, 50 g kg⁻¹ at 5-15 cm, 40 g kg⁻¹ at 15-30 cm, 25 g kg⁻¹ at
30-60 cm, 14 g kg⁻¹ at 60-100 cm, and 6 g kg⁻¹ at 100-200 cm depth intervals. Model validation
using 25% pedons showed a R² of 0.65, 0.71, 0.79, 0.77, 0.69, and 0.29 for 6 depths, respectively.
The lowest RMSE (8.5 g kg⁻¹) was observed at 60-100 cm, and the highest (21.8 g kg⁻¹) at 0-5
cm. To quantify the response of SOC in a changing climate (temperature and precipitation), two
future climate scenarios—RCP4.5 and RCP8.5—from NASA GISS models were considered and
SOC contents were projected for year 2050 and 2080. Our estimation suggested that Central Chile
would lose on average 11% SOC for the RCP4.5 and RCP8.5 scenarios in the year 2050, and 16%
by 2080 compared to current estimation. In the next step, we estimate the projected SOC stock
changes using pedon bulk density data which are still under processing

keywords: digital soil mapping, soil organic carbon, climate change, soil legacy data
Laser-induced breakdown spectroscopy (LIBS) for efficient quantitative determination of elemental plant nutrients in soils: A contribution to precision agriculture

Daniel Riebe – University of Potsdam, Germany

Toralf Beitz – University of Potsdam, Germany

Hans-Gerd Löhmannsröben – University of Potsdam, Germany

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

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

Robin Gebbers – Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Germany

One important aim of precision agriculture (PA) is the optimization of crop growth by means of site-specific measures, e.g. fertilizer application. Thus, PA should contribute to a resource efficient and ecofriendly soil management. Due to the expenses associated with traditional methods of soil analysis, requiring sample collection and laboratory analysis, PA technologies are still not in widespread use. Therefore, the aim of the project “I4S – Integrated System for Site-Specific Soil Fertility Management” is the development of a field-deployed, sensor-based analysis system offering rapid, cost-effective and spatially-resolved fertilizer recommendations.

In this system, laser-induced breakdown spectroscopy (LIBS) could be ideally suited to assessing elemental nutrient contents of soils. In addition to low cost, durability and small size, a reliable quantification procedure is a crucial requirement for such a system. However, the texture as well as the composition of the soil can affect the spectra. This matrix dependence is the key challenge to be addressed in the application of LIBS for soil evaluation. The focus of this work was the establishment of a LIBS method for soil analysis in a laboratory environment for future field application.

Natural soil samples of various origins, textures and compositions were used to characterize the matrix dependence of the LIBS spectra. Reference samples were prepared by adding defined amounts of the target elements to the soils (standard addition). Signals not superimposed by peaks of other elements were identified for each element. The reference samples also provided calibration curves for the respective soil type when the initial concentrations in the soils were taken into account. Additionally, the common laboratory method ICP-OES following aqua regia extraction was used to obtain reference values. Various approaches of calibration free evaluation of the data were also evaluated. In addition to traditional single-pulse experiments, the advantages of dual-pulse LIBS in relation to signal intensity, reproducibility as well as overcoming the matrix dependence of soil spectra were investigated. These methods were subsequently applied to validation samples collected on a dense grid within a field.

**keywords:** Laser-induced breakdown spectroscopy, LIBS, precision agriculture, elemental nutrients
# The use of Self Organizing Maps in hydrological modeling with SWAT

David Rivas - IMDEA Water Institute, Madrid, Spain

Ana Maria Tarquis - Universidad Politécnica de Madrid, Spain

Angel de Miguel - ALTERRA. Wageningen Environmental Research, Netherlands

Barbara Willaart - IIASA, International Institute for Applied System Analysis, Austria

Soil properties constitute an important aspect to take into account in environmental research and especially in hydrological modelling. This work presents the methodology applied for the set-up of the soil property database in the SWAT model (Soil and Water Assessment Tool) as a functional part for the simulation of the water resources in the Cega-Eresma-Adaja watershed (Douro river basin, Spain). During SWAT calibration, the most common and recurrent issues are related with soil parameters and the difficulty usually arises from the identification of the correct value ranges during iteration.

This case study covers an area of 8.000 km² from which 407 soil samples were used to create soil units implementing an optimized process through unsupervised learning of artificial neural network (ANN), better known as self-organizing maps (SOM). The goal of this research consists in generating a reduced number of soil units using two performance metrics for the SOM: the mean distance from the cluster centroid and the Devies-Boulding (DB) index. Low DB values represent low intra- and high inter-cluster variability indicating a more satisfactory clustering result from the mathematical point of view.

As a result, 16 clusters were identified as the most suitable number of clusters including the standard deviation for each soil property as a measure of the variability. The SWAT model was run twice using: (1) a map of the soil units (1:400.000 scale) with properties assigned by matching data from sampling points through rastering procedure and (2) soil map derived from SOM using also the soil sample data rasterized in the whole area and optimized to reduce the number of clusters. By comparing these two soil maps as elements for data input in the SWAT model, the SOM procedure clearly shows more advantages in terms of calibration accuracy.

keywords: self-organizing maps, soil units, clustering
Spectroscopy and remote sensing for assessment of peatland degra-
dation

Jean Robertson – The James Hutton Institute, United Kingdom

Laura Poggio – The James Hutton Institute, United Kingdom

Estefania Pérez-Fernández – The James Hutton Institute, United Kingdom

Peatlands cover more than 20% of Scotland's land area, and habitat mapping and assessment of their condition is a key issue both at Scottish and EU level. These carbon-rich soils have a vital functional role and, importantly, degraded peatlands are net emitters of carbon. In order to return degraded peatlands to their naturally C sequestering state, restoration measures are often essential. However, for many areas, the location and extent of degradation is poorly defined. There is a real need for methodologies and approaches to support faster and cheaper monitoring of peatlands and other high organic soils, particularly on a large scale. The aim of this study is to use a combination of spectroscopy (FTIR, Fourier Transform Infrared) and remote sensing (RS) data to predict key attributes and properties of peat and highly organic soils, such as humification (Von Post), bulk density, carbon content and water holding capacity. This will then allow the rapid and novel assessment and modelling of above and below ground habitat (peatlands/highly organic soils) conditions and provisioning of ecosystem services, such as carbon storage, water quality, and biodiversity. The spectroscopic calibrations used to predict the key properties will provide rapid ground-truthing for remote sensing upscaling, with the initial focus on the calibration for the soil organic carbon content, using partial least square regression. This pilot study in Scotland is based on a raised bog in relatively good conditions (Dava Bog) where a lot of information is already available for soil, vegetation, land use and remote sensing data (Landsat, 30m resolution and Sentinel, 10m resolution) and where preliminary modelling conducted between physical properties, vegetation and remote sensing showed good results. Interpretation of the FTIR spectra for the Dava Bog will be reported, along with the calibrations developed to predict the organic matter content and preliminary results for the links to remote sensing data.

keywords: peatland degradation, FTIR spectroscopy, Remote Sensing
Digital soil mapping of available water capacity for metropolitan
France

Mercedes Roman Dobarco - French National Institute of Agricultural Research, France

Manuel Martin - French National Institute of Agricultural Research, France

Nicolas P.A. Saby - French National Institute of Agricultural Research, France

Hocine Bourennane - French National Institute of Agricultural Research, France

Dominique Arrouays - French National Institute of Agricultural Research, France

Isabelle Cousin - French National Institute of Agricultural Research, France

Christine Le Bas - French National Institute of Agricultural Research, France

Soil hydraulic characteristics are often required for modeling hydrological processes, crop pro-
ductivity, biogeochemical cycles, and movement of pollutants. The objective of this study was
producing a digital soil map of available water capacity (AWC) for metropolitan France following
GlobalSoilMap (GSM) specifications. The digital soil mapping approach consisted in predicting
sand and clay for the six GSM depth intervals and then applying pedotransfer functions (PTF)
for estimating AWC (cm3 cm-3) by layer. We used the French SOLHYDRO database to develop
continuous PTF for soil moisture at field capacity (pF ≤ 2.0) and permanent wilting point (pF ≤
4.2) using sand and clay as predictor variables. The calibration dataset comprised profiles from the
French Soil Survey program. The independent dataset consisted of profile data from the RMQS,
which is distributed on a 16 km x16 km grid. Particle size measurements were processed with
equal-area spline functions, and later transformed with the additive log ratio transformation (alr).
Regression models were fitted for sandalr and clayalr, followed by cokriging of the residuals using
a linear model or coregionalization. We considered the uncertainty of the input data by sampling
from the conjoint distribution of sandalr and clayalr in a sequential Gaussian simulation approach,
for producing uncertainty estimates for AWC by depth interval. Total AWC for the profile was
calculated by aggregation to the predicted soil depth (Lacoste et al., 2016) to a maximum of 2
m. The lack of georeferenced observations for AWC did not allow us to validate both the spatial
models and the PTF estimates together, and therefore we validated separately the performance
of the PTF with cross-validation (R2 ≤ 0.67 – 0.84, RMSE ≤ 0.027 – 0.043 cm3 cm-3) and the
spatial predictions of sand and clay.

Lacoste, M., Mulder, V. L., Martin, M. P., Arrouays, D., 2016. Evaluating large-extent spatial modeling approaches: a case study for total soil depth for France. Geoderma Regional, 7, 137 – 152.

keywords: Available water capacity, Digital Soil Mapping, Pedotransfer functions
Analyzing the effect of heavy metals' contamination on ecosystem services of urban soils in different functional zones of Moscow-city

Olga Romzaikina – RUDN University, Russia

Vyacheslav Vasenev – RUDN University, Russia

Elvira Dovletyarova – RUDN University, Russia

Urbanization is a global trend with important environmental consequences. Features, functions and key components of urban ecosystems differ considerably from those of natural ecosystems. Urban soils provide important ecosystem services, like purifying surface and ground water, neutralizing pollutants and supporting greenery. The absorption and neutralization of pollutants is one of the key regulating services of urban soil. Urban soils are polluted with heavy metals, oil products and poly-aromatics hydrocarbons (PAH). The main sources of urban soils' pollution includes industry, transport and household waste Urban areas consist from functional zones (i.e., industrial, residential and recreational), which affects high spatial diversity of urban soils and services they perform. Analyzing spatial distribution of heavy metals and benz(a)pyrene in (BP) in urban soils is needed to evaluate the regulatory services in various functional areas of the city.

The research focused on urban soils in Moscow city (Russia). Soil survey organized in 2015-2016 and covered various functional zones, including recreational (parks, squares, urban lawns) and residential zones. Samples were taken by depths 0.0-0.2; 0.2-1.0m and 1.0-2.0m, therefore both topsoil and subsoil layers were involved into analysis. In the collected samples, the following chemical properties were measured: pH, Cl, petroleum, BP and heavy metals' content (Ng, Ni, Cu, Zn, Pb, Cd, As, Cr, Mn, Co). The results of chemical research were processed using the R studio to analyze and map the distribution of heavy metals using geostatistical methods.

In result, the contents of BP were above the health thresholds for all functional zones. The maximal 0.06 mg / kg were found in the residential area. An excess over health thresholds was shown for Pb (33,06 ± 3,64 mg / kg), Ni (8,20 ± 2,19 mg / kg) and Co (5,30 ± 0,78 mg / kg) in soils under lawns along roads. The contents of Cu (69,59 ± 6,31mg / kg) and Co (2,92 ± 0,18 mg / kg) exceeded corresponding thresholds in residential zones. The created maps of soil pollution were related to the standard values (relative permitted amount) for different soil acidity and texture. The resulted maps enable assessing the performance of the urban soils' regulating service to mitigate heavy metals' pollution in different functional zones of Moscow. It also allows selecting the areas with high-risk of pollution and to the opposite the most resistant zones to support soil management and land-use planning in cities.

keywords: heavy metals, urban soils, urbanization, digital soil mapping
# A new look at soil phenoforms – definition, identification, mapping

David G. Rossiter - ISRIC, Netherlands

Johan Bouma - WUR, Netherlands

The "soil genoform" vs. "soil phenoform" distinction was introduced by Droogers and Bouma (1997) to account for management-induced physical differences among pedons with the same pedo-genesis, sufficient to cause large differences in soil functions. We propose a rigorous conceptual and operational definition of soil genoforms and soil phenoforms, and suggest pedometric techniques to identify and map them. We define soil genoforms as soil classes as identified by a soil classification system. This avoids the difficulty of defining when human intervention has been sufficient to create new genoforms – by definition this is when new classes (e.g., Stagnic Anthrosols) are recognized in the classification system, based on diagnostic horizons (e.g., anthrostagnic) or properties. We then conceptually define soil phenoforms as persistent variants of a genoform with sufficient physical differences to substantially affect soil functions.

This excludes chemical variants, except as these are severe enough to cause physical differences, e.g., defloculation and structure change caused by addition of high-sodium water. Phenoforms must be non-seasonal and non-rotational; this excludes the normal cycle of tillage, crop growth, cover crops, and residue incorporation, etc. They must be persistent enough that substantial management interventions are necessary to change them. This excludes results of management such as surface crusting which can be broken up by a single tillage, but does include subsoil compaction that would require several years of recovery crops such as deep-rooted white radish combined with conservation tillage or no-till.

Soil phenoforms can be identified by measurements of soil physical properties at locations within a soil genoform with different management and investigating if these are different enough to affect soil functions, notably soil hydrology and crop yield.

Identified phenoforms can be mapped by digital soil mapping techniques, typically using current and historical management as covariates in mixed interpolators such as regression kriging.

Since phenoforms can be changed by management, such mapping exercises should be repeated every few years, especially when changed management in an area is observed.

**keywords: soil phenoforms, soil monitoring, soil functions**
# Past, present and future of information technology in pedometrics

David Rossiter – ISRIC, The Netherlands

Although pedometric approaches were taken as early as 1911 (Mercer & Hall, Student) and 1937 (Youden & Melich), the post-WWII development of information technology radically transformed the possibilities for pedometrics. The first development is of course the electronic digital computer. Early examples where this made pedometric techniques viable are numerical taxonomy of soils in the early 1960's and geostatistics from the mid 1970's. By the time of the first Pedometrics conference in 1992 sufficient computing power was available for stochastic simulation and complex geostatistical procedures such as cokriging and disjunctive kriging. In the intervening 25 years computing power has grown to almost magical proportions, allowing any individual scientist to carry out the most complex procedures. The second development is the growth of networking towards the internet. This has fostered much easier collaboration among dispersed pedometricians, rapid communication with journals, collaborative programming and publication, and easy access to resources. This is the third development: the tremendous growth in on-line storage, especially of open data, including GIS coverages and remotely-sensed images, and computer programs. This has allowed pedometricians working on geographic problems to integrate sources from multiple disciplines, most notably in digital soil mapping using a wide variety of covariates related to soil genesis. It has also allowed the development of an open-source movement of collaborative development of computer programs useful for pedometrics. A fourth development is the explosive growth of sensors to provide data for pedometrics, including spectroscopy, electromagnetic induction, gamma-ray detectors etc., connected to each other and to central data stores. A fifth development is wireless technology, including mobile computing and telephony, again greatly facilitating rapid and extensive data collection - in pedometrics, the more dense the data, the greater the analytical possibilities and the lower the uncertainty. A final development is the Global Positioning System, making accurate georeference of field data a routine part of data collection, and thereby assuring the highest possible accuracy in maps made by predictive pedometric methods.
Mapping soilscapes using soil co-occurence networks

Pierre Roudier - Landcare Research, New Zealand

Dylan Beaudette - USDA-NRCS, USA

Dion O'Neale - University of Auckland, New Zealand

Soils are arguably the most complex material on Earth, and present an important spatial variability. Across the landscape, different soils types will often be spatially intermingled. This is reflected by soil mapping units (SMU) being often composed of more than one soil taxa. The notion of soilscape reflects the fact that a landscape can be delineated into different such spatial units "including a limited number of soil classes that are geographically distributed according to an identifiable pattern" (Lagacherie et al., 2001).

The research field of network science offers new mathematical tools to visualize and analyze existing soil survey data, and explore its complex relational interactions. In this paper, we extract and visualize pedological information by analyzing the co-occurrence of soil taxa using a network approach.

For any level of a given hierarchical soil classification system, the co-occurence of soil taxa within and between neighbouring SMUs can be described as a network graph. The structural properties of the resulting graphs can be analyzed, and tools such as community structure detection are used to classify their different nodes. Soilscapes are then delineated by mapping the identified communities back in geographical space. This approach also gives a method for quantifying the pedological complexity of different regions based on their constituent soil types, using metrics such as graph connectivity. Different levels of granularity for both the regional data and the soil classification data allow for views at different scales.

Network science offers the opportunity of new insights from looking at existing data in a new way. Soil survey data from S-Map (NZ) and USDA-NRCS (USA) are used to illustrate the value and originality of these new approaches.

keywords: Networks, Soil classification, Soilscapes
Approximation on the reconstruction of pore spaces in 3D CT soil images by cubic interpolation

R. Ruelas – Departamento de Ingeniería de Proyectos, Universidad de Guadalajara, Jalisco, México

B. Ojeda-Magañas – Departamento de Ingeniería de Proyectos, Universidad de Guadalajara, Jalisco, Mexico

J. Quintanilla-Domínguez – Grupo de Automatización en Señal y Comunicaciones (GASC), Universidad Politécnica de Madrid (UPM), Madrid, Mexico

Ana María Tarquis – Universidad Politécnica de Madrid, Spain

L. Gómez-Barbara – Doctorado en Tecnologías de Información, CUCEA, Universidad de Guadalajara, Jalisco, Mexico

Diego Andina – Grupo de Automatización en Señal y Comunicaciones (GASC), Universidad Politécnica de Madrid (UPM), Madrid, Spain

The soil mechanics depends on its solid-pore architecture, and this also influences the biological, chemical, and physical processes that happen on it. Actually, non-destructive imaging techniques, as X-ray computed tomography (CT), can be used in order to study the features of a particular soil.

The analysis of 3D CT images of soil samples can be carried out by experts, though this can be done automatically through image processing. In the latter case a set of 2D images, corresponding to equally spaced images from the original 3D CT images, have to be previously segmented for the identification of the pores. Then, and from the pores in each 2D image, different interpolation techniques can be used to approximate the 3D pore spaces.

In this work an approach, based on cubic interpolation, was applied for the reconstruction of the pore spaces, and interesting results are provided as it allows visualizing and quantifying some morphometrics parameters of soil samples, which can be used afterwards to different purposes.

This novel approach has potential for various applications in soil science.

References

Cortina-Januchs, M. G., Quintanilla-Dominguez, J., Vega-Corona, A., Tarquis, A. M. and Andina, D. Detection of pore space in CT soil images using artificial neural networks. Biogeosciences, 8, 279–288, 2013.

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.

Houston, A. N., Schmidt, S., Tarquis, A. M., Otten, W., Baveye, P. C. and Hapca, S. M. Effect of scanning and image reconstruction settings in X-ray computed microtomography on quality and segmentation of 3D soil images. Geoderma 207–208, 154–165, 2013.

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

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, segmentation, 2D interpolation, soil pore structure
# Designing soil monitoring schemes for large areas based on high resolution digital soil mapping products: a case study from France

Nicolas Pa Saby – INRA, France

Budiman Minasny – The University of Sydney, Australia

Dominique Arrouays – INRA, France

Jaap de Gruitjer – WUR, Netherlands

Brendan P Malone – The University of Sydney, Australia

Alex McBratney – The University of Sydney, Australia

Throughout the world, there is an urgent need to determine where soil quality and functionality are threatened by dynamic processes such as loss of organic matter, contamination, acidification, etc. However, there are a number of challenges and difficult choices associated with the implementation of soil monitoring networks to track these processes. For instance, the designers need to decide where they should measure and how frequently. Should they select sites at random or sample on a purposive design such as a regular grid? Sampling is also constrained by the financial and other resources available. Thus an efficient sampling strategy is sought for applications also in soil survey for mapping. Digital soil maps have been produced at continental and regional extents at various spatial resolutions. These maps of soil properties along with their uncertainty can be used to establish a network for soil monitoring sites. In this work, we used a design-based sampling methodology, called Ospats which optimised spatial stratification and allocation for stratified random sampling of points. Ospats uses a grid of points from digital soil maps with known uncertain predictions of the target variable. We demonstrate its application for establishing a network of monitoring sites in France using the digital soil maps of carbon content at various resolutions (5km, 2.5Km and 90 m). We developed a specific algorithm to handle large spatial datasets. This algorithm allows allocating additional grid points to an existing Ospats stratification constructed using a coarser grid. We compared the Ospats stratification to two extremes: a stratification close to those by the cum-root-f method and, a compact geographical stratification based only on the locations of the grid points. We based our comparisons on the data from the soil French soil monitoring network. Our results clearly showed that Ospats considerably improves the efficiency of the stratification. We also explored the sensitivity of the method for incorrect quantification of the uncertainty of the initial carbon map. This method is potentially useful for setting up new monitoring networks.

**keywords:** monitoring, design-based statistics, statistical design, carbon, digital soil mapping, France
# The use of Zipf's-Pareto law to describe the structure of soil cover of Bryansk Opolje

Vera Samsonova - Soil Science faculty, Lomonosov Moscow State University, Russia

Dmitry Krotov - Bryansk State Agrarian University, Russia

Joulia Meshalkina - Soil Science faculty, Lomonosov Moscow State University, Russia

Victor Voronin - Bryansk State Agrarian University, Russia

The soil cover of any landscape is, on the one hand, a continuous formation, on the other hand, it may be considered as a set of different soils on some hierarchical level of a soil classification. Analysis of correspondence between the entities of a soil classification and the soil cover allowed E. Dmitriev (1988) to propose the concept of pedo-coenosis. The Zipf's-Pareto law is used in biology to describe the structure of coenoses. This law is widely applied in various scientific fields for the analysis of community structure in a broad sense.

The study was performed on example of the Bryansk region landscapes, which are the part of the Russian Plain. Peculiarities of soil units' areas distribution on the soil maps of scale 1:50,000 were studied. The main features of the Opolje are a relatively high location (elevation marks about 180-200m above the sea level) and the presence of loess-like loams. The soil cover is dominated by automorphic gray forest soils (Phaeozems Albic), to date almost completely under cultivation. Light erodibility of loess-like loams causes the development of erosion processes. The hydromorphic analogues of gray forest soils (Phaeozems Gleyic) were associate to depressions. A small amount of sod-podzolics soils (Albeluvisol Umbric) and alluvials meadow soils (Fluvisols Umbric) were marked as part of the territory. Three areas of Bryansk Opolje were studied. The number of allocated soil map units was 220, 303 and 417, respectively. The areas of the units ranged from 2800 m2 to 1 ha.

Rank-order graph of the logarithms of the areas of the soil maps units of all three sites under consideration to the rank logarithms consisted of several linear segments. The coefficients of determination for each of them were higher than 0.95, provided that even for the shortest segment the sample volume exceeded 30 values. The first line segment corresponded to the soil map units with the largest area. It was a few map units with not eroded gray forest soils and complexes of gullies and ravines soils. In the middle part of the graph gray forest soils and gray forest gleyic and gley soils with little participation of sod-podzolics soils were placed. Gray forest soils with different degrees of erosion appeared more often with the rank increasing. The last graph segment corresponds to the map units of a very small area arising as a result of mapping errors or when digitizing paper map.

**keywords:** Zipf's-Pareto law, Phaeozems Albic, soil cover patterns
Stacked generalization of statistical learners - a case study with
soil iron content in Brazil

Alessandro Samuel-Rosa – Universidade Federal de Santa Maria, Brazil

Ricardo Dalmolin – Universidade Federal de Santa Maria, Brazil

When modeling soil-landscape relationships we generally test a handful of statistical learners. Having limited data, we use cross-validation to select the best performing learner. In this study we evaluate the benefits of combining learners for soil prediction using stacked generalization. It consists of calibrating multiple learners and submitting them to 10-fold cross-validation. Cross-validation predictions are used as covariates in an interceptless linear regression of the target variable. Constrained to be non-negative, the estimated regression coefficients are the stacking weights expressing the importance of each learner. When making predictions, each learner is used in turn and the weights used to optimally combine multiple predictions into a single prediction.

The data was downloaded from the national database maintained by Embrapa. The target variable was the soil iron content (g/kg). Covariates ($p \le 7$) were constructed using soil profile data. The $n \le 22,981$ records remaining after some data cleaning were split into calibration ($n\_cal \le 16,086$) and validation ($n\_val \le 6,895$) sets. Six learners were used: linear regression with stepwise selection (lm), multivariate adaptive regression splines (mars), regression random forest (rf), single-hidden-layer neural network (nnet), weighted k-nearest neighbor regression (knn) and support vector machine with polynomial kernel (svm).

rf and knn severely overfitted the data, while lm, mars and svm were the most stable learners. The first two yielded the lowest absolute and squared errors (rmse < 45 g/kg) and explained more of the variance (ave ~ 0.6). mars, nnet and lm were the least biased learners (me ~ -0.1 g/kg), while svm was the most biased (me ≤ -5.14 g/kg). lm explained the smallest amount of variance (ave ≤ 0.49).

rf received the largest stacking weight ($w \le 0.55$), knn and svm received moderate weights ($w \sim 0.2$) and nnet and mars received the smallest weights ($w < 0.1$) – lm was dropped from the stack ($w \le 0$). Combining learners lowered all absolute and squared errors (rmse ≤ 43.23 g/kg), yielded a considerably small bias (me ≤ 0.53 g/kg), and explained the same amount of variance explained by rf (ave ≤ 0.61).

Staking learners was more beneficial than using the single best performing learner because it reduced generalization errors. The magnitude of the benefits seems to depend upon the diversity of learners (over and underfitting, biased and nonbiased). Besides, by using least squares regression to compute stacking weights we can estimate the prediction error variance of any combination of learners.

keywords: Digital soil mapping, Model combination, Cross-validation
# ‘spup’ – an R package for uncertainty propagation analysis in spatial environmental modelling

Kasia Sawicka – Wageningen University, Netherlands

Gerard Heuvelink – Wageningen University, Netherlands

In many environmental studies, including soil science, variables of interest are spatially dis-
tributed. These variables are often used as input to spatial analyses and spatial models, such as
in land evaluation systems, erosion models and crop growth models. Since mapped variables are
only approximations of the real world uncertainty is introduced, which will propagate through the
analyses and models. Currently, advances in uncertainty propagation and assessment have been
paralleled by a growing number of software tools for uncertainty analysis, but none has gained
recognition for a universal applicability and being able to deal with case studies with spatial mod-
els and spatial model inputs. Due to the growing popularity and applicability of the open source
R programming language we undertook a project to develop an R package that facilitates uncer-
tainty propagation analysis in spatial environmental modelling. In particular, the ‘spup’ package
provides functions for examining the uncertainty propagation starting from input data and model
parameters, via the environmental model onto model outputs. The functions include uncertainty
model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC)
techniques, as well as several uncertainty visualization functions. Uncertain environmental vari-
ables are described by probability distributions. Both numerical and categorical data types are
handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is
also accommodated for. For uncertainty propagation the package has implemented the MC ap-
proach with efficient sampling algorithms, i.e. stratified random sampling and Latin hypercube
sampling. The MC realizations may be used as input to the environmental models called from R,
or externally. Selected visualization methods that are understandable by non-experts with limited
background in statistics can be used to summarize and visualize uncertainty about the measured
input, model parameters and output of the uncertainty propagation. We present the ‘spup’ pack-
age and illustrate it with a simple example of deriving the C:N ratio from maps of organic carbon
and nitrogen for a 33x33km area in Madagascar. We show that even for such a simple example,
the outcomes of the uncertainty analysis yields valuable insight.

keywords: R language, uncertainty analysis, uncertainty propagation, spatial models, Monte Carlo
Reflectance spectra and Land Surface Temperature integration
obtained from Landsat on the soil granulometric quantification

Veridiana Maria Sayão – Luiz de Queiroz College of Agriculture, Brazil

Jose Dematte – Luiz de Queiroz College of Agriculture, Brazil

Luis Gustavo Bedin – Luiz de Queiroz College of Agriculture, Brazil

Rodnei Rizzo – Center of Nuclear Energy in Agriculture, Brazil

Luiz Gonzaga Medeiros Neto – University of Sao Paulo, Brazil

Marcos Rafael Nanni – State University of Maringa, Brazil

Soil attributes have direct influence on surface temperature. Despite studies with soil spectra obtained from satellites, soil evaluation through Land Surface Temperature (LST) is still scarce. The broad availability of satellite thermal data and the development of algorithms to retrieve LST facilitated its use in soil studies. In this study, an image of Landsat 5 was used for extracting LST using the inversion of Planck's function in band 6 (10.4 – 12.5 µm), and land surface emissivity was estimated using Normalized Difference Vegetation Index threshold method. We also extracted reflectance values from bands 1, 2, 3, 4, 5 and 7. Most of the area was with bare soil and plowed. The study area (198 ha) is located in Sao Paulo state, Brazil. Soil texture varies from loamy fine sand to clayey. Soil samples were collected in a regular grid of 100 x 100 m (depths: 0-0.2 and 0.8-1.0 m); soil granulometry was determined by laboratory analysis. Models for soil texture determination were performed using Linear Regression (LR), with soil samples from 62 auger points distributed in 14 toposequences. Simple LR was applied for generating prediction models for clay and sand based only on LST and also only on elevation data extracted from a Digital Elevation Model (DEM). Multiple LR was applied in order to generate models for the same variables but using atmospherically corrected spectral reflectance from Visible, Near-infrared and Shortwave infrared (Vis-NIR-SWIR) as predictors. Multiple LR was also applied for the prediction of clay and sand using simultaneously Vis-NIR-SWIR, LST and elevation. Predictive performance of models was assessed based on adjusted R² and Root Mean Squared Error (RMSE, g kg⁻¹) obtained in validation. Using as co-variables only LST we had R²adj ≤ 0.35 and RMSE ≤ 153.83 for clay and R²adj ≤ 0.36 and RMSE ≤ 172.51 for sand; using only elevation, we had R²adj ≤ 0.14 and RMSE ≤ 169.15 for clay and R²adj ≤ 0.15 and RMSE ≤ 187.03 for sand; using Vis-NIR-SWIR bands, R²adj ≤ 0.67 and RMSE ≤ 100.86 for clay and R²adj ≤ 0.55 and RMSE ≤ 131.20 for sand. The best model was obtained using all co-variables for clay, reaching R²adj ≤ 0.73 and RMSE ≤ 89.84 and Vis-NIR-SWIR plus LST for sand, with R²adj ≤ 0.64 and RMSE ≤ 121.72, enhancing the importance of considering not only spectral data for generating soil texture maps, but also LST information, which improves soil attributes mapping.

keywords: Land Surface Temperature, Soil Attributes Mapping, Thermal Infrared, Satellite Im-
agery Analysis
Visualizing Soil Landscapes

Darrell Schulze – Purdue University, USA

Alfred Hartemink – University of Wisconsin, USA

James Thompson – West Virginia University, USA

NA Isee Network – Isee Network, USA

The ready availability of high quality digital soils data, particularly in the U.S., makes it
possible to visualize soil landscapes in new ways. The Integrating Spatial Educational Experiences
(Isee) project utilizes the most detailed US soils information from the USDA SSURGO soil survey
dataset, the best available digital elevation models from the National Elevation Data Set, and other
data sources to prepare informative maps of a variety of soil properties. Maps for seven U.S. states
(Indiana, Illinois, Kentucky, Ohio, Texas, West Virginia, and Wisconsin) are currently online. Maps
for Dominant Soil Parent Materials, Natural Soil Drainage Classes, and Soil Orders are available
for all the states, and some states have additional maps. These maps can be studied using the free
Isee app for the Apple iPad (https://appsto.re/us/nbdy7.i) and the new SoilExplorer.net website.
Details of soil landscapes can be explored by zooming to a scale of 1:18,000, while popups provide
additional information about each map unit. The project is a collaborative effort of the Isee
Network (http://isee.network).

keywords: soil landscape visualization, soil parent materials, soil property maps
# Detection of soil microbial dynamics with high spatial and temporal resolution using infrared thermography and radiocarbon imaging

Katharina Schwarz – Bergische Universität Wuppertal, Germany

Jannis Heil – Bergische Universität Wuppertal, Germany

Julian Heitkötter – Ruhr-Universität Bochum, Germany

Bernd Marschner – Ruhr-Universität Bochum, Germany

Britta Stumpe – Bergische Universität Wuppertal, Germany

Soil microbial activity is highly important in the context of soil fertility, soil carbon loss, and soil contamination. However, up to now, high resolution spatial and temporal analysis of the microbial activity in soils has not been possible due to methodological, instrumental, and analytical challenges. Thus, new techniques, such as active and passive infrared thermography in combination with radiocarbon imaging, are used to develop a method to monitor spatial microbial dynamics in undisturbed soil samples. The infrared thermography technique is used because microbial activity produces heat due to respiration.

In this study, three agricultural and one forest soil sample, varying in soil biological properties, have been used. For pre-incubation, the soils were re-wetted to 50% water holding capacity and placed in microplates in an airproof-glove box, with an adjusted relative humidity about 92%, for one week. After pre-incubation, 14C-glucose was applied to the soil samples in droplet form or laminary with a robotic spraying system to increase the microbial activity to a detectable limit. After the glucose application, the spatial microbial activity in the soil samples was monitored, scanning the surface temperature dynamics with an infrared thermography (IRT) camera. The IRT camera was installed above the samples and recorded the surface temperatures of each sample for every minute over a period of one week. Additionally, radiocarbon images of the soil samples were continuously taken during incubation, to detect the spatial 14C-glucose mineralization process as well as to relate the mineralization rates to the spatial microbial dynamics. Since soil moisture dynamics are potentially influencing the surface temperature dynamics, temporal and spatial changes of the soil water content have been continuously monitored with active thermography techniques as well.

In general, all soil samples revealed a significant increase of the surface temperature two days after 14C-glucose application. Furthermore, significant differences in surface temperature dynamics have been detected between the four soil samples. The spatial surface temperature dynamics correspond well with the spatial 14C-mineralization detected by radiocarbon imaging. Thus, the application of glucose to soil samples enhances microbial activity up to a level which is detectable with passive thermography techniques. Water content calibration against active thermography parameter shows that active thermography has a high reliability in predicting soil sample moisture with a high spatial and temporal resolution. The differences in surface temperature dynamics between the soil samples can be partly explained by different soil water contents.

**keywords:** infrared thermography, radiocarbon imaging, undisturbed soil, spatio-temporal analysis, active thermography, passive thermography, microbial activity
Exploring extrapolation risks of spatial prediction models at global,
continental and regional scales

Wei Shangguan – Sun Yat-sen University, China

Gerard Heuvelink – ISRIC — World Soil Information, Netherlands

Tomislav Hengl – ISRIC — World Soil Information, Netherlands

Yongjiu Dai – Sun Yat-sen University, China

Shupeng Zhang – Sun Yat-sen University, China

Statistical spatial prediction models are frequently used for spatial interpolation. However, when the calibration data do not completely cover the feature or geographic space then interpolation may turn into extrapolation. This is not without risk, because extrapolation is more difficult than interpolation, and can produce maps that are highly inaccurate for parts of the area of interest. However, the extrapolation risk is often overlooked in the literature. In this paper, we investigated the extrapolation risk of global depth to bedrock mapping using random forest regression. To evaluate the extrapolation risk, we proposed a method called “cross-validation by region”, which involves partitioning a sample of data into subsets by regions, calibrating the spatial prediction model using one subset of a region, and validating the model using the other subsets. Then, we examined the relationship between the model performance and the spatial distance (and the feature space similarity). Results show that extrapolation gives less extreme values and is much less accurate than interpolation. The extrapolation risk was reduced when the calibration area was changed from continent to region. Even if a model yielded a high accuracy in interpolation, it did not necessarily give a high accuracy in extrapolation. The overall accuracy of extrapolation for regional models had a negative correlation (R≤-0.39) with spatial distance and a positive correlation (R≤0.26) with feature space similarity. The extrapolation risk depends on the degree of spatial autocorrelation, the degree of feature space similarity, and numbers of situations that were not covered by the calibration data set occur in the extrapolation area. We emphasize that the risk of extrapolation may be high even if the feature space (i.e. environmental conditions) is covered well by the training data set, indicating that the relationship between the dependent and independent variables do not carry from one region or continent to another. Cross-validation by regions was recommended to evaluate the extrapolation risk.

keywords: Extrapolation, Interpolation, Spatial prediction model, Mapping, Depth to Bedrock,
Machine Learning, Cross Validation
Proximal soil sensing: new tools for pedometricians

Zhou Shi - Zhejiang University, China

Craig Lobsey - CSIRO, Australia

Raphael Viscarra-Rossel - CSIRO, Australia

This presentation will provide a historical perspective of proximal soil sensing (PSS) and describe some of the early to current day proximal soil sensors, as well as current research, and the challenges for PSS into the future. Proximal soil sensing has gained prominence in the soil and environmental sciences over the past 35 years, and specially in the past 15 years, due to advances in sensing technologies, data analytics, modelling and computation capabilities. PSS provides good quality soil information more efficiently compared to traditional laboratory methods of soil analysis, which can be complex, expensive and time consuming. The rationale for PSS is that many less precise spatial measurements that are more easily taken are better and more informative than a few discrete measurements that are very precise. Interest in PSS is now more widespread, currently there is much research and development and a wide range of technologies are commercially available. In fact, by its own merit, PSS is now a new discipline and is a topic of considerable interest in the soil, agricultural and environmental sciences, and engineering. Importantly, PSS can provide precise and quantitative data at fine spatial and/or temporal resolutions, to help Pedometricians better understand the soil as a phenomenon that varies over different scales in space and time. Currently, there is much research on the development of proximal multi-soil sensor systems to simultaneously measure many physico-chemical soil properties in the field, and to develop sensor systems to measure soil biota. Proximal soil sensors produce big data, which can be analyzed using modern statistical, geostatistical and machine-learning methods. As such, there is research also on sensor data analytics, modelling and sensor data fusion. Proximal soil sensing is also enabling sensor-based digital soil mapping at different scales, from field to continental scales. The challenges for PSS are to: (i) continue to develop new integrated sensing systems that combine the sensing technologies with robust data analytics, modelling and easy-to-use user interfaces, (ii) improve the accuracy, robustness and application of existing techniques, (iii) focus on the assimilation of proximal and remote sensing because these technologies are largely complementary, (iv) explore new applications and (v) maintain the enthusiasm and support for the working group on proximal soil sensing (WG-PSS) from soil scientists and pedometricians, which provides a mechanism for continued engagement, collaboration and support for our community. Continued use and development of proximal soil sensing is imperative if we are to improve our understanding of soil processes, and assist soil science in the development of solutions to our current environmental dilemmas.
Spatial variability of peat soil properties for different sampling
scales

Valeriia Sidorova – Institute of Biology, Karelian Research Centre, Russia

The objective of this research was to determine the spatial variability of pH values and soil organic carbon content at different scales using both traditional statistics and geostatistics. The investigation was carried out at Korzinskaja lowland in Southern Karelia (North-West of Russia). The dominant soils at the plots included Agric Podzols, Histic Gleysols, Sapric and Hemic Histosols. The field have been drained in 1963. Actually it is used as a hayfield.

In a detailed scale the plot was a square 100 100 m. A regular sampling grid scheme with the lag 10m was used. For the study in a middle large scale soil samples were collected at 78 locations on a 1 km² field. Sampling lag was about 100 m. In a large scale the plot represented the southern and middle part of the lowland with an area of approximately 12 km². A locally-regular scheme with randomly-regular arrangement with the lag 250m was used.

Such parameters as the scale of the values, variance and coefficient of variation were almost twice higher in a large scale than in a detailed scale. This is due to the fact that increasing of the surveyed area increases soil diversity. In addition, the large scale investigation includes fields with different land-use practices.

Spatial variability of soil properties was examined by variography. Semi-variograms were ap-
proximated by nested models. Spatial coherence was evaluated as medium. Spatial structure of soil
properties showed anisotropy in the N-S (along the drain) and E-W (across the drain) directions.

Generally, the E-W direction was regarded as the main direction of spatial variability. We distinguished the following levels of heterogeneity of soil properties. The first level was nugget variation at distances less than 10 m. The second level was a periodical change with a period of 30 m, which might be ascribed to the influence of the drains. The dimensions of the third level of heterogeneity were about 300 m, and the last level of heterogeneity also represented periodical changes with a period of 500-600 m. Periodical component reflected the influence of two different drainage systems.

Variograms in N-S direction regularly increased for the all scales. Thus, the range in this direction exceeded 1000 m. As the drainage system was constructed along a slope, we hypothesized that there was a natural trend along a slope.

The research was supported by the grant of Russian Science Foundation, project No. 17-17-01293.

keywords: peat soil, spatial variability, sampling scale, anisotropy
# Statistical analyses of landscape controls and vertical variability of soil organic carbon in permafrost-affected soils

Matthias Siewert – Stockholm Univeristy, Sweden

Birgit Heim – Alefred-Wegener-Institut, Germany

Gustaf Hugelius – Stockholm University, Sweden

Samuel Faucherre – Center for Permafrost (CENPERM), University of Copenhagen, Denmark

The circumpolar permafrost region occupies around 24 % of the global land area and stores around half the global soil organic carbon (SOC). Yet, very little pedometric research exists on permafrost affected soils and SOC stored in these regions. This is partly due to the remoteness and inaccessibility, but also due to unique thermal and morphological properties of permafrost affected soils. In our research, we document this extreme spatial and vertical variability caused by periglacial landforms such ice-wedge polygons and non-sorted circles. Permafrost affected soils can be seen in the most simple way as a two layer system, including the seasonally unfrozen active layer and the permafrost layer. Yet, due cryoturbation of entire soil horizons caused by freeze and thaw cycles, there is a strong vertical variability and enrichment of SOC at depth. This variability requires standardization for sampling and intensive data harmonization.

We present results from analyzing landscape controls and the vertical variability of SOC and key soil properties from the Arctic Lena River Delta in NE Siberia (Siewert et al., 2016). These key properties are C%, N%, C/N, bulk density, visible ice and water content. We sampled, classified and harmonized a total of 50 soil pedons representing one of the largest and most coherent soil pedon datasets for a remote study area in permafrost regions. The pedon data was subdivided into 1 mm increments down to 100 cm and then aggregated, compared and visualized. We used statistical testing to compare landscape groupings of soil pedons. We compared different vertical subdivisions according to permafrost properties, soil horizons and metrical depth increments. High vertical resolution plots document the high variability with depth and help to understand soil development. This allows clear recommendations for sampling and analyzing these soils with depth. Major geomorphological units of the Lena River Delta control SOC storage and explain more soil variability than soil taxonomy or vegetation cover. The landscape mean SOC storage is estimated to 19.2 ± 2.0 kg C m⁻² for the top meter highlighting the large amounts of SOC stored in permafrost regions. We further discuss implications of this variability for digital soil mapping.

Siewert, M.B., Hugelius, G., Heim, B., Faucherre, S., 2016. Landscape controls and vertical variability of soil organic carbon storage in permafrost-affected soils of the Lena River Delta. CATENA 147, 725–741. doi:10.1016/j.catena.2016.07.048

**keywords:** Soil organic carbon, permafrost, sampling, Statistical analysis
Mapping Soil Properties for achieving Soil Functions

Iolanda Simo - Smart Soil & Land, Spain

Rogier P.O Schulte - Farm Systems Ecology, Wageningen University, Netherlands

David Wall - Teagasc, Environment Soils and Land-use Department, Ireland

Gemma Torres - Teagasc, Environment Soils and Land-use Department, Ireland

Andrea Richter - Teagasc, Environment Soils and Land-use Department, Ireland

Daire O'huallachain - Teagasc, Environment Soils and Land-use Department, Ireland

Rachel E. Creamer - Soil Biology and Biological Soil Quality, Wageningen University, Netherlands

Soils play an important role in ecosystems and provide important services for humans known as “soil functions”: i) primary production, ii) carbon sequestration, iii) water provision and purification, iv) habitats for biodiversity and v) nutrient cycling. Under changing conditions, different soils are better in performing some soil functions than others.

There are few studies that have linked soil properties to ecosystem services while most studies lack completely the soil component for evaluating ecosystem services. Most of the mapping and modelling exercise use proxies to infer soil information, often in the form of land use and land cover data to produce spatially distributed biophysical parameter values needed for production function models. Soil property maps however provide quantified spatial information, which could be utilized more versatility for the spatial inference of soil functions and services. This study utilises a large soil survey conducted in Ireland between 2009 and 2014. This survey provided detailed soil descriptions, a range of soil properties down to 1 m depth and identification of diagnostic features for 228 soil profiles. This has allowed detailed analyses relating to several soil functions, which are applied to this soils database. In Ireland, four soil function maps have been derived including; potential soil carbon sequestration map, soil water infiltration and drainage map, soil microbial diversity map and potential N mineralisation map.

1) Soil carbon sequestration map has been derived from detailed soil fractionation studies of 45 grassland sites down to 1 m depth for a range of soil types in Ireland.

2) Soil infiltration and drainage maps are based upon soil water characteristic curves which have been created for 564 profiles (from legacy and recent sources) throughout the country, utilising the RETC programme.

3) Soil microbial diversity maps indicate microbial abundance (microbial biomass) and functional capacity (respiration of multiple substrates) measured at 156 soil profiles across Ireland.

4) Potential N mineralisation was measured at 93 grassland sites across Ireland. The soil samples covered a broad range of soil types and physiochemical properties. They were taken for all surface horizons, which varied in depth, but generally were 0-25 cm depth.

These soil function maps are derived in conjunction with the main soil survey map to allow extrapolation using key diagnostic or modelled with profile soil properties to predict the general capabilities of soils. Soil property maps have been compiled with proper DSM techniques to combine soil-site datasets from legacy and recent sources and expert pedological knowledge.

**keywords:** Soil properties, Soil functions, Soil Carbon sequestration map, Soil infiltration map, Soil drainage map, Soil microbial diversity map, Potential N mineralisation map, DSM techniques
Refinement of soil maps of forested areas with help of geological
maps, digital elevation model and remote sensing of vegetation

István Sisák – University of Pannonia Georgikon Faculty, Hungary

Piroska Kassai – University of Pannonia Georgikon Faculty, Hungary

Judit Bódis – University of Pannonia Georgikon Faculty, Hungary

Spatial forest databases contain data on tree species and soil types in Hungary. Average unit in the database of Bakonyerdő Ltd. has a size of 3.8 hectare with a range between 0.01 and 36 hectare. However, only 55 % of the units have data on soils and even more interestingly, only 69 % of the units have data on tree species although space images and aerial photographs clearly show dense forest stands in those areas. Further, soil type map for forested areas is more an intelligent guesswork rather than result of thorough investigation. So, soil maps of forested areas in Hungary are incomplete and less reliable thus there is a need for more precise assessments to properly manage forests. Map of surface geology is publicly available in Hungary at 1:100,000 scale and it has much better spatial resolution and accuracy than soil spatial databases. Digital elevation model is available at 10x10 meter ground resolution. Space images and aerial photographs are widely accessible with a few meters ground pixel sizes. Our hypothesis was that we can establish strong relationships between these data and soil types then the relationships can be used to assess a complete, detailed and accurate soil map. First, we developed a stepwise method to semi- automatically delineate homogenous vegetation patches by using space images from four different times of the vegetation cycle between spring to autumn. We have found that early and late stage images in the annual cycle are much more suitable to tell apart canopies of different species, ages and conditions. We have used tree stand data from the company database where only a few species were present to establish signal library for the individual species and we used this core data to assess presence of tree canopies for the remaining area where tree species were not known or many species were present in mixtures or in small patches. Then, we have established relationships between vegetation polygons, elevation, geological units and the individual soil types by using two step classification method. The assessed final soil map was more detailed than the original rough database. The estimated soil map included Rendzic Leptosols, Chromic Cambisols, Haplic Luvisols, Haplic Cambisols, Nudilitic Leptosols and Eutric Arenosols among others. Assessment on vegetation and soil types were tested by visiting 40 test points within an 8 squere kilometre test area and the accuracy was above 80 %.

keywords: incomplete soil map of forests, space images, soil-tree caniopy relationships, two step clustering
Analysing spatial patterns of soil pollution profiles in floodplain
exposed to historical environmental load using correlation of pro-
portional similarity matrices with spatial matrices

Jan Skála - Research Institute for Soil and Water Conservation, Czech Republic

The Czech Republic belongs to the countries with long-term industrial history, especially along the Czech – Saxony border, where the industrial rapid growth was led by coal processing. High TEs contents may be attributed to a spatial interaction of various pollution sources (long-term airborne pollution due to combustion of fossil fuel and chemical industry, geochemical anomalies of metal- logenic zones). High environmental load caused by various sources interference may be reported in the adjacent watersheds of the Ohře River. A regional analysis of soil contamination in floodplain soils (100-year flood inundation areas) was conducted by trace elements (Be, Cd, Co, Cr, Cu, Hg, Pb, Ni, V, Zn) assessment with the objectives of revealing both the spatial patterns of pollution magnitude as well as pollutants’ composition. There were compared trace elements contents (2M nitric acid extract) with national background values and geoaccumulation indices were calculated for each sample. Proportional similarities (cosine-theta matrix) between all the samples were cal- culated after the relative contributions of each element to the overall index had been calculated and transformed (range transformation, constant-row sum normalization). We correlated the pro- portional similarities matrix (cosine-theta matrix) with various spatial matrices (simple matrix of Euclidean distance derived from spatial coordinates, river network spatial distance) by the Mantel correlation test. There was found statistically significant Mantel correlation between proportional similarity and both spatial matrices. A multivariate Mantel correlogram was then computed to analyse spatial correlation changes of proportional similarities among the samples in relation to distance binding (a normalized Mantel statistic between a similarity matrix among sites and a matrix where pairs of sites belonging to the same distance class receive value 0 and the other pairs, value 1). There was proved that the proportional similarity of pollution profiles of soil samples diminished with the sample distance increase (from the distance of 20 km the Mantel correlation became insignificant and continued to approximate zero). The used methodology seems to be a promising tool to supplement geostatistical analyses of pollution magnitude and may bring a new angle to environmental gradients in riverine landscape because the spatial relations of proportional similarity may reflect various aspects of soil pollution - geochemical variation, pollution sources incidence, soil/environmental conditions changes along the watercourse.

**keywords:** spatial analysis of floodplains' pollution, proportional similarity of soil pollution profiles, correlation between similarity and spatial matrices
Near infrared index to assess soil texture and carbon content ef-
fects on soil hydrodynamic properties.

Ines Soltani – UMR SAS AGROCAMPUS-OUEST-INRA, France

Youssef Fouad – UMR SAS AGROCAMPUS-OUEST-INRA, France

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

Pascale Breger – UMR SAS AGROCAMPUS-OUEST-INRA, France

Remy Dubois – UMR SAS AGROCAMPUS-OUEST-INRA, France

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

Christophe Cudennec – UMR SAS AGROCAMPUS-OUEST-INRA, France

Characterization of soil hydraulic properties is important for assessing soil water regime in agricultural fields. In the laboratory, measurements of soil hydrodynamic properties are costly and time consuming. Numerous studies recently demonstrated that reflectance spectroscopy can give a rapid estimation of several soil properties including those related with soil water content. The main objective of this study was to evaluate the ability of near-infrared spectroscopy (NIRS) to predict the effects of soil texture and soil organic carbon (SOC) content on soil hydrodynamic properties.

The study was carried out on A-horizon soil samples collected from the Armorican Massif (Brittany). In order to cover the spatial variability of the regional range of soil types, fifteen undisturbed soil blocks were sampled in 2014 from various sites.

Our soilsamples display a wide range of soil textural classes, from sandy loam to clay loam, and of SOC contents, from 8 g/kg to 62 g/kg, conforming with the typical textures and SOC content levels observed within Brittany.From each soil block, four aggregates of at least 3-4 cm width and 5-6 cm height were extracted.

The whole aggregates were set at different moisture contents (10 matric potentials), from satu-
ration up to permanent wilting point (pF≤4.2). Atgiven pressure head, soil samples were scanned
in triplicate to acquire reflectance spectra between 400nm and 2500 nm using a handheld spectro-
radiometer. Reflectance spectra were converted to continuum removal to highlight the absorption
features of soil samples. Then, we have focused on absorption band around 1920nm, which is
linked to combination vibrations of water. We defined a new index based on the full width at half
maximum (FWHM) of this band. Our results showed a linear relationship between this NIR index
and volumetric water content (R²>0.9), whatever soil aggregate. Moreover, it appears from these
results that the slope of the line was well correlated with clay content and parent material. Overall,
our findings indicate that the parameters of this linear relationship offer a new way to study the
hydrodynamic properties of soils in relation to their physical properties (texture, structure, etc.).

keywords: NIR index, hydrodynamic properties, NIRS, soil texture, soil organic carbon
# Mapping subsoil ripening using Bayesian Generalized Linear Modelling

Luc Steinbuch – Wageningen UR; ISRIC, Netherlands

Dick Brus – Wageningen UR, Netherlands

Gerard Heuvelink – Wageningen UR; ISRIC, Netherlands

One of the first soil forming processes in marine and fluviatile clay soils is ripening, the irreversible change of physical and chemical soil properties, especially consistency, under influence of air. We used Bayesian Binomial Logistic Regression (BBLR) to update the map of subsoil ripening for a reclamation area in the west of The Netherlands. Similar to conventional Binomial Logistic Regression (BLR), in BBLR the binary variable (the soil is ripened or unripened) is modelled by a Bernoulli distribution. The logit transform of the ‘probability of success’ parameter of the Bernoulli distribution is modelled as a linear combination of the covariates soil type, water ditch level and ground water table depth. To capture all available knowledge, Bayesian statistics combines two information streams, i.e. pre-observation knowledge summarized in a ‘prior’ probability distribution, and the actual observations. We combined subsets of different size (ranging from 5% to 50% of the original dataset of 676 observation locations) with different priors (based on legacy data) to investigate the effect of sample size and prior distribution of the quality of the map. The posterior parameter distributions, calculated by Markov Chain Monte Carlo simulation, vary in dispersion as well as in central values, especially for the smaller datasets. More informative priors decreased dispersion and pushed posterior central values towards prior central values. Interestingly, the resulting prediction maps were almost similar, probably because of the relative low predictive power of the BLR model. However, the associated prediction uncertainty maps were different: a more informative prior decreased prediction uncertainty. Validation showed that more informative priors resulted in stabilized and slightly better results, as quantified by the measure for separating power Area Under Curve; again this effect is more pronounced for smaller datasets. In our research, Bayesian Binomial Logistic Regression proved to be a flexible mapping tool, that – depending on the strength of the prior – stabilizes estimated parameters as well as prediction maps in case of small datasets.

**keywords:** Bayesian Statistics, Informative priors, Clay soil ripening, Generalised Linear Model, Spatial prediction, Spatial prediction uncertainty
# Investigating the effect of moisture for using field-portable X-ray fluorescence spectrometry for 2.5D high-resolution geochemical mapping

Uta Stockmann – The University of Sydney, Australia

Brendan Philip Malone – The University of Sydney, Australia

Budiman Minasny – The University of Sydney, Australia

Alex. B. McBratney – The University of Sydney, Australia

Portable X-ray fluorescence spectrometers have the capability to be used for in-field measure-
ments of the soil's elemental concentration. In soil science they are therefore predominantly uti-
lized to delineate areas of soil contamination, to infer a range of soil constituents but also recently
to understand areal soil profile development through 2.5D high-resolution geochemical mapping.
However, acquiring pXRF data in the field is affected by various factors including soil moisture
and sample heterogeneity. Soil moisture can either attenuate the X-ray signal or cause increased
scatter of the X-rays.

In this study, we investigated the effect of soil moisture on pXRF measurements, with the aim to establish a function to correct for the influence of soil moisture. We conducted this study on the farm- and paddock-scale at the Sydney Institute of Agriculture's agricultural research station Nowley, near Gunnedah, NSW, Australia. Differing topography and parent materials have given rise to a variety of soil types across the property, ranging from deep and dark-coloured heavy clays to texture-contrast soils with a sandy topsoil and clayey subsoil. This allowed us to also study the influence of soil texture on pXRF measurements. Soil samples were collected based on stratified random sampling. 120 surface soil samples (farm-scale) and 30 soil core samples of 1 m depth (paddock-scale) were measured with an Olympus Innov-X Delta Premium pXRF under field and laboratory conditions. In the laboratory, measurements were conducted on air-dried samples that were ground to pass a 2 mm sieve. Wetness (%) was determined gravimetrically by measuring the amount of water in the soil after oven-drying at 105°C for 24 h.

We found that variable field soil moisture conditions reduced the elemental signal-to-noise
ratio, which resulted in lower concentrations of the target variables in field condition as compared
to laboratory conditions. This, however, did not obscure the spatial pattern. The relationship
between field-moist and air-dried concentrations of the target variables was highly linear which
enabled us to correct for moisture content by establishing an empirical correction function.

keywords: proximal soil sensing, soil geochemical mapping, portable X-ray fluorescence spectrometry
# Desertification status mapping using recent machine language techniques

Dharumarajan Subramanian – Scientist, National Bureau of Soil Survey and Land Use Planning,
Nagpur, India, India

Thomas Bishop – The University of Sydney, Australia

Rajendra Hegde – ICAR-NBSS&LUP, India

S.K Singh – ICAR-NBSS&LUP, India

There is a need for up-to-date assessment of desertification/land degradation maps which are
dynamic in nature at different scales for comprehensive planning and preparation of action plan.
The present paper aims to develop the desertification vulnerability index (DVI) and predict the
different desertification processes operating in Anantapur district, Andhra Pradesh, India based on
recent machine language techniques. Climate, land use, soil and socioeconomic parameters were
used to prepare desertification vulnerability index by multivariate geostatic model. Computed
desertification vulnerability index along with climate, terrain and soil properties were used as ex-
planatory variable to predict the desertification processes by using Random Forest Model. About
14.2 % of area was created as a Training dataset in 9 different places for modeling and remaining
area was tested for prediction of desertification processes. We used desertification status map of
Anantapur district (1: 50 000 scale) prepared by Space application Centre under Desertification
status mapping of India – 2cycle (DSM -2cycle) as a reference dataset and calculation of accuracy
indices. Kappa and Overall classification accuracy index were calculated for training and validation
data sets. We recorded Overall accuracy rate and kappa index of 85.5 % and 75.8 % for training
datasets and 71.0 % and 51.8% for testing datasets. The results of predicted map showed that
19.2, 2.12 and 19.77 % of district is affected by soil erosion, salinisation and vegetal degradation
processes respectively against 19.6, 6.2 and 22.5 % of area actually mapped for soil erosion, salin-
isation and vegetal degradation processes respectively under DSM-2cycle project. The prediction
for salinisation is low due to less number pixels selected for training data and it could be improved
by selecting more pixels and using additional covariates like EC, parent material and drainage
in environmental modeling. The results of variable importance analysis of Random Forest model
showed that Desertification vulnerability index (DVI) was the most important predictor followed
by Evapotranspiration and NDVI (Normalised Difference Vegetation Index) for prediction of de-
sertification processes. The results from the present work give new thought of using the existing
knowledge on prediction of the desertification at unvisited areas and also quick update of DSM
maps.

**keywords:** Desertification, Desertification vulnerability indices, Random Forest model, prediction, variable importance
# Updating digital soil maps with new data: a case study of soil organic matter in Jiangsu, China

Xiaolin Sun - Sun Yat-sen University, China

The rapid developments in the acquisition of data on soil should enable pedologists to update existing soil maps readily. The methods by which that is done must take into account temporal change of soil properties and local differences in spatial variation, and the common mapping techniques will have to be modified to make full use of digital data. We show what can be achieved with a case study of updating maps of soil organic matter (SOM) in Jiangsu Province, China, with three sets of soil data collected in the 1980s, 2000 and 2006. Our results showed that temporal changes in SOM between the three sampling periods occurred in only very small parts of the region, although F and t tests based on global statistics indicated significant change. Models of spatial variation of SOM based on the data collected in the 1980s and 2006 for the whole region differed somewhat, whereas models based on the data collected in the 1980s, 2000 and 2006 for the Taihu region (south Jiangsu) were significantly different. As updating with Bayesian Maximum Entropy continued, prediction accuracy increased and prediction variance decreased. Finally, our study leads us to suggest improved technologies for updating digital soil maps with new data.

**keywords:** Updating, Digital soil mapping, Bayesian maximum entropy, Soil organic matter, Temporal change of soil
Comparative examination of various uncertainty assessment ap-
proaches based on geostatistical approaches and machine learning
algorithms

Gábor Szatmári – Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Hungary

László Pásztor – Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Hungary

Katalin Takács – Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Hungary

Annamária Laborczi – Institute for Soil Science and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Hungary

Predictive soil maps are subject to uncertainty since they are simplified representations of more
complex (and partially unknown) patterns of soil variations. Therefore, the effective quantification
and communication of the inherent uncertainty in digital soil mapping products would be indis-
pensable to stakeholders (e.g. policy makers) as it has already been stressed by GlobalSoilMap.net.
Nowadays, various methods (e.g. geostatistical approaches, machine learning algorithms) are used
to assess uncertainty. Nevertheless, the adequacy of these quantifications are rarely evaluated in
detail. Furthermore, only a few papers deal with the comparison of these methods, as well as their
pros and cons. The aim of our present study was to compare the most commonly used uncertainty
quantification methods and highlight their pros and cons using independent control data set for
their evaluation. Soil organic carbon (SOC) content of the topsoil layer (0-30 cm) was selected as
target variable, due to its stressful relevancy in environmental related decision-making and plan-
ning. For the area of Hungary we tested the following methods: (1) quantile regression forest, (2)
sequential stochastic simulation, (3) prediction error variance of regression kriging (using random
forest as trend estimator), and (4) prediction error variance of regression kriging (using multiple
linear regression as trend estimator). These methods use probabilistic framework to model and
quantify uncertainty, which can be modelled by a probability density function at any location.
For each method we mapped the SOC content, as well as its associated uncertainty using the
upper and lower limits of the 90% prediction interval, respectively. In addition, we derived and
mapped the prediction interval range for each method. Based on an independent control data set
we evaluated the adequacy of the quantified uncertainty using classical indicators (e.g. mean error,
root mean square error, percentage of explained variance), the percentage of prediction interval
coverage, as well as accuracy plots. Furthermore, in the course of the evaluation and comparison,
we highlighted the pros and cons of the applied methods. According to the results of the evaluation
and comparison process, we ranked the applied uncertainty quantification methods. Our poster
will present the results of evaluation and comparison, the rank of the applied methods, their pros
and cons, as well as some of our conclusions drawn from the experiences.

keywords: accuracy assessment, DSM, geostatistics, machine learning, SOC