Relevance of GEMAS for
soil property mapping
Rainer Baritz1, Dietmar Zirlewagen2, Vibeke Ernstsen3
1)
Federal Institute for Geosciences and Natural Resources (BGR)
2)
INTERRA
3)
Geological Survey of Denmark and Greenland (GEUS)
1
Introduction
GEMAS samples were taken from agricultural surface-close
soil layers (Ap 0-20 cm, Grazing land 0-10 cm);
Parameters also include TOC, pH, P, CEC;
“Standard results“ are provided as geostatistical maps;
The main objective of GEMAS is to collect information about
the spatial distribution pattern of trace elements in the rooted
zone of soil;
Soil organic matter and acidity are important to interpret the
potential of soil to store and release heavy metals.
2
Questions
Technical questions:
 What is the possible contribution of GEMAS to soil
monitoring?
 How can GEMAS information be integrated into different soil
inventories?
 How can the representativity from GEMAS be assessed?
Criteria?
 Are there alternative upscaling methods?
3
Upscaling method
4
Upscaling method
Spatial Regression
Stepwise multiple linear regression combined with
geostatistics/kriging;
Covariates as possible impact factors on the target variables
(TOC, pH, P);
Stratification is important to optimise upscaling models.
5
Upscaling method
Database
Biogeographical regions, soil regions, N deposition data
(EMEP);
DEM 90m/Relief parameters (aspect, slope, curvature,
topographic wetness index, potential direct radiation, etc.);
Parent material (ESDB, 2004);
Land cover CORINE 2000 and 2006, and at GEMAS points
LUCAS 2003 crop types (CEC, 2003);
Climate (WORLDCLIM).
6
Upscaling method
Stratification
Stratum 1: Sweden Norway
and Finland (‘Boreal’)
Stratum 2: United Kingdom
and Ireland
Stratum 3: Eco-Regions
(DMEER) with CodeNumbers 56, 27, 10 (‘High
Mountainous’)
Stratum 4: the remaining
European target area
(‘European continent’)
7
Total organic carbon
8
TOC [%] agricultural soil
(From Baritz et al., 2014, Fig. 6.4B, p.123)
9
Step
Influence of
predictors
[All_TOC]
Variable
Entered
Number
Vars In
Partial
Model F Value
R-Square R-Square
Pr > F
1
TMAX5
1
0.2015
0.2015
462.11 <.0001
2
Ap
2
0.0892
0.2907
230.09 <.0001
3
PREC11
3
0.0139
0.3046
36.58 <.0001
4
Histosol
4
0.0128
0.3174
34.31 <.0001
5
TMAX4
5
0.0059
0.3234
16.05 <.0001
6
Corine_pasture
6
0.0066
0.3299
17.91 <.0001
7
BIOCLIM_3
7
0.0041
0.3340
11.12
0.0009
8
LForm_4
8
0.0040
0.3380
11.04
0.0009
9
Corine_scrub
9
0.0037
0.3417
10.25
0.0014
10 BIOCLIM_14
10
0.0030
0.3447
8.45
0.0037
11 BIOCLIM_17
11
0.0073
0.3520
12 TMIN7
12
0.0035
0.3555
9.78
0.0018
13 Luvisol
13
0.0033
0.3587
9.25
0.0024
14 Leptosol
14
0.0023
0.3610
6.55
0.0106
15 Podzol
15
0.0028
0.3638
7.98
0.0048
16 TEXTSRF1
16
0.0049
0.3687
13.98
0.0002
17 ECO_CODE_11
17
0.0026
0.3713
7.58
0.0060
18 climagroup4
18
0.0021
0.3734
6.09
0.0137
19 PREC8
19
0.0023
0.3758
6.82
20.44 <.0001
10
0.0091
TOC
TOC and crop types
Stratum 1: Sweden, Norway and Finland (‘Boreal’)
Stratum 2: British Isles and Ireland
Stratum 3: Eco-Regions (‘High Mountainous’)
Stratum 4: remaining Europe ‘(European continent’)
11
(From Baritz et al., 2014, Fig. 6.5, p.124)
LUCAS 2003 and soil texture (ESDB, 2004)
coarse
Rye
medium
medium fine
fine
very fine
Potatoes
Olive groves
Shrubland
Barley
Maize
Sunflower
Grassland
Durum Wheat
Rape seeds
Common wheat
Dry pulses
Sugar beet
Other non permanent
industrial crops
Cotton,
Other fibre and oleaginous
Oranges,
crops
Vineyards,
Nuts
12
(From Baritz et al., 2014, Table 6.3, p.127)
Soil acidity
pH (CaCl2)
13
pH (CaCl2)
agricultural soil
14
Validation and uncertainties
15
Validation
Method
 Despite low sampling density (1 sample site/2500 km2), the
sample size was large enough to separate a training, and a
validation set both representing well the predictive
population;
 Split of the data; random split inside large-scale squares
stratified biogeographical region;
 Regional models are derived from the training data;
 Prediction error is then compared to the results from running
the training set-based models with the validation data.
Germany: 357,104 km2 total, 187,291 km2 , agriculture (1 site/600 km2)
Europe: 10.5 million km2; agriculture: 1 site/2333 km2 (parts of Eastern Europe and
Balkans not covered)
16
Results
Modellskala (lognormal)
Response
TOC
TOC
TOC
TOC
TOC
TOC
TOC
TOC
Variante
ALL
ALL_AP_STRATEN
ALL_GR_STRATEN
AP_STRATEN
AP_STRATEN_KRIGING
GR_STRATEN
GR_STRATEN_KRIGING
STRATEN
R²
0,343
0,267
0,29
0,324
0,926
0,35
0,871
0,397
Spatial distribution of the
inaccuracy (standard error)
RMSE
0,43818
0,38471
0,48785
0,36742
0,12247
0,46583
0,21213
0,41833
Training
MSE
0,192
0,148
0,238
0,135
0,015
0,217
0,045
0,175
STD
0,553
0,463
0,59
0,457
0,457
0,587
0,587
0,549
OBS
1865
951
911
949
949
904
904
1853
R²
0,366
0,296
0,316
0,338
0,33
0,352
0,35
0,399
Validierung
RMSE
MSE
0,43932
0,193
0,40988
0,168
0,46904
0,22
0,40866
0,167
0,41833
0,175
0,45935
0,211
0,46797
0,219
0,43359
0,188
STD
0,566
0,502
0,58
0,511
0,511
0,581
0,581
0,57
Spatial distribution of the
residuals
17
OBS
1862
952
910
948
948
907
907
1855
Outlook
18
Outlook





Include N and CEC, include soil texture data;
Re-upscale with the new parent material map;
Condense regionally, then also improve stratification;
Interpret covariates;
Include integrated evaluations (e.g., potential heavy metal
release relative to SOM and acidity).
19
New BGR parent material map
alluvium/colluvium
calcearous rocks
clayey materials
crystalline rocks
detrital formations
European Soil
Database
glaciofluvial materials
loamy/silty
marl
other/organic
sandstone/flysch/molasse
sandy materials
schists
volcanic rocks
(From Günther et al., 2013, Fig. 2, p.299 &
Baritz et al., 2014, Fig. 6.2, p.120)
201 classes
(aggregated from
671 initial classes)
Regional studies
BGR GEMAS: N=310 (completely
sampled and analysed soil profiles)
+
BGR soil profiles: N=1567
(agricultural land)
=
Representative data set for higher
resolution evaluations, 2.5 D
21
Conclusions
 The quality of the GEMAS inventory (analysis,
georeferencing) is high so that satisfactory regression
models can be built (950 plots for the ‘learning’ data set;
stratification is important.
 Option: Integration into a larger soil monitoring and soil
quality assessment scheme (country-level/Europe).
 Added value to facilitate a closer exchange between
geoscientists and soil scientists.
Thank you for your attention!
d.zirlewagen@interra.biz
ve@geus.dk
rainer.baritz@bgr.de
References
23
References
SLIDES 7, 9, 11, 12, 20:
Baritz, R., Ernstsen, V. & Zirlewagen, D., 2014. Carbon concentrations in European agricultural and grazing land soil. Chapter 6
In: C. Reimann, M. Birke, A. Demetriades, P. Filzmoser & P. O’Connor (Editors), Chemistry of Europe's agricultural soils – Part
B: General background information and further analysis of the GEMAS data set. Geologisches Jahrbuch (Reihe B 103),
Schweizerbarth, 117-129.
SLIDE 6:
CEC (Commission of the European Communities), 2003. The LUCAS survey. European statisticians monitor territory. Theme
5: Agriculture and fisheries, Series Office for Official Publications of the European Communities, Luxembourg, 24 pp.
Corine land cover 2000 (CLC2000) seamless vector database. http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2000clc2000-seamless-vector-database
Corine Land Cover 2006 (CLC2006)s eamless vector data. http://www.eea.europa.eu/data-and-maps/data/clc-2006-vector-data-version
ESDB, 2004. The European Soil Database distribution version 2.0. European Commission and the European Soil Bureau Network,
CD-ROM, EUR 19945, http://eusoils.jrc.ec.europa.eu/ESDB_Archive/ESDB_Data_Distribution/ESDB_data.html .
SLIDE 9:
Baritz, R., Ernstsen, V. & Zirlewagen, D., 2014. Carbon concentrations in European agricultural and grazing land soil. Chapter 6 In:
C. Reimann, M. Birke, A. Demetriades, P. Filzmoser & P. O’Connor (Editors), Chemistry of Europe's agricultural soils – Part B:
General background information and further analysis of the GEMAS data set. Geologisches Jahrbuch (Reihe B 103),
Schweizerbarth, 117-129.
SLIDE 17:
Baritz, R., D. Zirlewagen and E. Van Ranst (2006). Methodical standards to detect forest soil carbon stocks and stock changes
related to land use change and forestry – landscape scale effects. Final report Deliverable 3.5-II. Multi-source inventory methods
for quantifying carbon stocks and stock changes in European forests (CarboInvent) EU EVK2-2001-00287.
SLIDE 20:
Günther, A., Van Den Eeckhaut, M., Reichenbach, P., Hervás, J., Malet, J.-P., Foster, C. & Guzzetti, F., 2013. New developments in
harmonized landslide susceptibility mapping over Europe in the framework of the European Soil Thematic Strategy. Proceedings
Second World Landslide Forum, 3-7 October 2011, Rome. In: C. Margottini, P. Canuti, K. Sassa (Editors), Landslide Science and
Practice. Springer-Verlag, Berlin, Vol. 1, 297-301. doi: 10.1007/978-3-642-31325-7_39.