Brus, D.J.

Heuvelink, G.B.M.

Kempen, B.

Knotters, M.

Vasat, R.

Vries, F., de

Walvoort, D.J.J.

English

Name not available

Implications of digital soil mapping for soil information systems

de Vries, F.

NARCIS 

Wageningen Yield 

 1 
IMPLICATIONS OF DIGITAL SOIL MAPPING FOR SOIL INFORMATION 
SYSTEMS 
 
G.B.M. Heuvelink1, D.J. Brus1, F. De Vries1, R. Vašát2, D.J.J. Walvoort1, B. Kempen1 and 
M. Knotters1 
1Alterra, Wageningen University and Research Centre, Wageningen, The Netherlands, 
gerard.heuvelink@wur.nl 
2Department of Soil Science and Soil Protection, Czech University of Life Sciences in Prague, 
Czech Republic 
 
 
Abstract 
Soil information systems (SIS) as we know them store information about the soil as point 
observations and maps. This may seem obvious and sensible, but in fact in the present time it is a 
suboptimal way of storing soil information. Nowadays soil maps are often derived using digital 
soil mapping models and this offers the possibility to store the models used to derive the maps, 
instead of the maps themselves. This short paper lists the advantages of storing models instead of 
maps and illustrates the approach with examples from SIS+, a prototype developed for the 
Netherlands. 
 
Keywords: Soil Information System, geostatistics, database, accuracy, R 
  
1. Introduction 
Soil information systems (SIS) were introduced in the 1970s and have proven very instrumental 
for the disclosure of soil data and soil information. These systems stimulated the transformation 
of analogue soil data to digital form and facilitated their storage. The database functionality of a 
professional SIS provides flexible query capabilities such that users can easily search and extract 
point data and maps from the database. Nowadays, many of the national and institutional SI 
systems around the globe can also be accessed from the internet. Current SI systems serve a clear 
goal and have a large group of satisfied users. However, they also have limitations. Some of 
these limitations can be overcome by making use of the fact that nowadays, many soil maps are 
produced using digital soil mapping techniques. 
 
2. SIS+: storing models instead of maps 
The rapid development and application of digital soil mapping techniques during the past decade 
calls for a next generation of SI systems. Such a system, named SIS+ for short, stores 
(pedometric) models instead of maps. For instance, rather than storing the result of a kriging 
interpolation, it is more sensible to store the source data and kriging parameters, such that a map 
can be delivered on demand. Thus, SIS+ still needs the source data (i.e. observations) and 
explanatory variables, such as a legacy soil type map, a DEM or remote sensing imagery, all of 
which can be read from a conventional SIS or geodatabase, but it no longer contains resultant 
maps of soil type and soil properties. 
 
Storing models instead of maps has several important advantages: 
1. It gives much more flexibility in terms of the spatial and temporal extent, resolution and 
support of the requested map. Users can log on to SIS+, submit their specific request and 
 2 
a customized map will be produced on the fly, provided that a model and sufficient data 
are available. Note that flexibility with respect to spatial extent, resolution and support 
applies both to the horizontal as well as the vertical dimension. 
2. It saves storage capacity, because the number of different maps that can be produced (i.e. 
different soil property, resolution, extent, support, depth or time period) is extremely 
large and it is practically impossible to store all of these in a conventional SIS. 
3. Many digital soil mapping approaches make use of (geo)statistical models that not only 
produce a map but also quantify the associated uncertainty, which is indispensable 
information in today’s environmental policy. Hence SIS+ also includes methods for 
stochastic simulation of hundreds or thousands ‘possible realities’, such as required for 
Monte Carlo uncertainty propagation analyses (e.g. Goovaerts 2001). Again, it would be 
very difficult to store all of the simulated realities in a conventional SIS. There is also no 
need for that, because storing the geostatistical model and the seed of the pseudo-random 
number generator is sufficient to be able to reproduce the realizations when needed. 
4. It enables easy updating with new data. When new data are added to the database, all that 
needs to be done to update an existing map is to rerun the model, which draws on all 
relevant data (old and new) stored in the SIS. 
5. It automatically archives the way in which a map is made. The model code tells precisely 
how a map was obtained, which point data and covariates were used and in what way. 
Note that this also allows using a model that was built for a particular soil property as 
starting point for a model of another soil property. Thus, model building need not 
commence from scratch each time a new model is built. 
6. It can help solve data sharing problems because it need only store models that can be 
applied to data that are stored elsewhere. For instance, a data owner may not be willing or 
allowed to transfer datasets to others. However, perhaps it is allowed that others make use 
of the data to create a map. In such a case, using a web-based approach,  the SIS+ model 
can be submitted to and run on the computer of the data owner, returning only the 
resulting map. In other words, there is no need to physically export data. Note that web-
based implementation of SIS+ also facilitates its use from all around the world. Anyone 
with permission rights can connect to the SIS+ host and load and run the SIS+ models on 
their own data, stored in their local SIS. 
 
The principle of storing models instead of maps is not new. For instance, the INTAMAP project 
(Pebesma et al. 2010) developed a web-based interpolation service for real-time automatic 
interpolation of environmental variables. The service can be approached from any place around 
the world, data can be submitted together with an interpolation request, after which an 
interpolated map and associated accuracy map are produced in real time in a fully automated 
fashion (see http://www.intamap.org/tryIntamap.php). 
 
The use of SIS+ can vary with the expertise of the user. Roughly, three categories of users are 
envisaged: 
1. Plain users that only work with ready-made models (recipes) for which only a few 
parameters need to be set (e.g. which soil property, extent, resolution and support). 
2. More advanced users that in addition can work with the system in an interactive way and 
can slightly modify existing models. For instance, based on the results of an exploratory 
data analysis tool contained in SIS+ these users might decide to transform the data or 
 3 
covariates and run the model on the transformed data. These users are expected to be able 
to adjust the model code accordingly. 
3. Fellow model developers that help extend the library of recipes stored in SIS+ with 
entirely new functions. For instance, these users might wish to implement new statistical 
models published in the scientific literature. 
 
Many more issues and opportunities will turn up once the development of SIS+ progresses and 
matures. Indeed, perhaps the best way to discover these opportunities is to simply build an 
operational SIS+ and learn from that. In the Netherlands, we have explored the possibilities of 
building a prototype SIS+ since 2007 (Brus and Heuvelink 2007). More recently, we have 
implemented a first version of SIS+ that as yet can only create maps of a limited number of 
continuous soil properties (Brus et al. 2010). The project is ongoing and aims to have a 
comprehensive prototype ready by the end of 2014. The next section briefly describes the current 
version of the Dutch SIS+ and illustrates its functionality with a few examples. 
 
3. Examples from the Dutch prototype SIS+ 
The Dutch prototype SIS+ is implemented in the R language for statistical computing 
(http://www.r-project.org/). It automatically loads data from the existing Dutch SIS 
(www.bodemdata.nl) and stores geostatistical models that map soil properties from data and 
covariates as R functions. An important feature of the prototype SIS+ is that it also quantifies the 
accuracy of the resulting maps. It is composed of six stages: 
1. Importing data from the Dutch Soil Information System. A function was developed that 
reads data from the SIS without requiring that users know the SQL language. Information 
such as soil property, time frame, extent and depth are provided as function parameter 
values. 
2. Data preprocessing. Among others, this stage converts observations from soil horizons at 
locations to values of the soil property for an arbitrary soil layer (with arbitrary top and 
bottom values chosen by the user). For soil properties such as organic matter and clay 
content, information on horizon bulk density is employed to calculate the weights with 
which soil horizon values contribute to the average of the soil layer, because this type of 
soil properties are not related to soil volume but to soil mass. 
3. Exploratory data analysis. Basic statistics such as the mean, variance, minimum, 
maximum and median can be computed to obtain insight in the distribution of the soil 
property. Histograms and Q-Q plots can be constructed to evaluate whether the data 
depart from the normal distribution. These plots can also be used to evaluate the effect of 
data transformation. Furthermore, a set of boxplots describing how the soil property 
distribution varies with soil type may be useful when taking a decision about the structure 
of the geostatistical model. If appropriate, outliers can also be removed from the data set 
during this stage. 
4. Building models of spatial variation. The current version of the Dutch SIS+ is restricted 
to geostatistical models, which include simple, ordinary, regression and cokriging 
models. Change of support can be defined using block kriging. The most important 
covariate currently used is a generalized soil map of the Netherlands, which distinguishes 
21 soil types (Wösten et al. 1988). 
5. Geostatistical (co)prediction and (co)simulation. Standard R-libraries such as gstat 
(Pebesma 2004) for geostatistics and maptools for GIS operations are used. 
 4 
6. Exporting resulting maps. Resulting raster maps can be exported in a variety of formats, 
such as graphical formats, GIS layers, ASCII files and database tables. 
 
Figures 1 to 3 show results of the six stages for mapping the soil pH at point support at depth 
0−25 cm, using a regression kriging model. Figure 4 shows final results of mapping the clay 
content at 0−25 cm with regression kriging for the entire Netherlands and for two subareas. In all 
three cases exactly the same model and data were used, only the extent differed. Figure 5 shows 
maps of the organic matter content of the topsoil, using either a kriging or cokriging model. 
These examples are merely shown for illustration, details are given in Brus et al. (2010). 
However, the important message is that SIS+ stores the geostatistical models with which these 
maps are made. These examples show the ease with which a given model can be extended to 
other depths or soil properties. 
 
 
Figure 1. Snapshot of R code used to extract pH observations from the Dutch SIS. 
 
 
Figure 2. Spatial distribution and histograms of soil pH observations (n=1621) for 0−25 cm 
depth. Bimodality is caused by the large difference between soil pH in the poor sandy 
soils (low values) and the loess, clay and calcaric sandy soils (high values). 
Histogram of pH residuals shows that the generalized Dutch soil map explains  much 
of the spatial variation. 
 
 5 
 
Figure 3. Maps of the predicted (left), prediction error standard deviation (centre) and example 
realization (right) of the soil pH for the Netherlands at 0−25 cm depth. 
 
 
 
Figure 4. Kriging maps of the clay content (mass %) at 0−25 cm depth for the Netherlands 
(left), at 0−50 cm depth for a coastal subarea (centre) and at 0−25 cm depth for a 
subarea in the east of the Netherlands (right). 
 
 
  
Figure 5. Maps of the organic matter content (mass %) for the Netherlands at 0−25 cm depth 
obtained with regression kriging (left) and regression cokriging (right), taking 
correlation with organic matter content at 25−50 cm and 50−100 cm into account. 
 6 
 
4. Conclusions 
• The development of a SIS+ that stores pedometric models instead of maps has many 
important advantages that can help overcome the limitations of conventional SIS. 
• SIS+ does not replace conventional SIS but needs it for delivery of point soil data and 
basic soil maps. It also needs other geodatabases for delivery of covariates. In turn, maps 
produced by SIS+ that are frequently used can be transferred to and stored in the 
conventional SIS. 
• In the long term, SIS and SIS+ may be integrated into one system, but during the 
development stage it is better that these are separate systems that communicate through 
data exchange. 
• The development of SIS+ can make use of current developments in automated mapping, 
particularly when it concerns web-based implementations. 
• Experiences so far with the development of a prototype Dutch SIS+ are very positive. 
The current implementation can already automatically download data from the Dutch SIS 
and map multiple continuous soil properties for arbitrary depths, extents, resolutions and 
supports. 
 
5. References 
Brus, D.J. and G.B.M. Heuvelink, 2007. Towards a Soil Information System with quantified 
accuracy. Three approaches for stochastic simulation of soil maps. WOt report 58, 
Wageningen, The Netherlands. 
Brus, D.J., R. Vašát, G.B.M. Heuvelink, M. Knotters, F. de Vries and D.J.J. Walvoort, 2010. 
Towards a Soil Information System with quantified accuracy. A prototype for mapping 
continuous soil properties. WOt report, Wageningen, The Netherlands (in press). 
Goovaerts, P., 2001. Geostatistical modelling of uncertainty in soil science. Geoderma 103, 
3−26. 
Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers and 
Geosciences 30: 683−691. 
Pebesma, E.J., D. Cornford, G. Dubois, G.B.M. Heuvelink, D. Hristopoulos, J. Pilz, U. Stöhlker 
and J.O. Skøien, 2010. INTAMAP: the design and implementation of  an interoperable 
automated interpolation web service. Computers & Geosciences (in press). 
Wösten, J.H.M., F. de Vries and J. Denneboom, 1988. Generalisatie en bodemfysische vertaling 
van de bodemkaart van Nederland, 1 : 250.000, ten behoeve van de PAWN-studie. Technical 
Report 2055, Stichting voor Bodemkartering, Wageningen, the Netherlands (in Dutch). 


Generalisatie en bodemfysische vertaling van de bodemkaart van Nederland, 1 : 250.000, ten behoeve van de PAWN-studie.

Geostatistical modelling of uncertainty in soil science.

INTAMAP: the design and implementation of an interoperable automated interpolation web service.

Multivariable geostatistics in S: the gstat package.

Towards a Soil Information System with quantified accuracy. A prototype for mapping continuous soil properties. WOt report,

Towards a Soil Information System with quantified accuracy. Three approaches for stochastic simulation of soil maps. WOt report 58,

Implications of digital soil mapping for soil information systems

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