Accuracy of coarse-scale land quality maps as a function of the upscaling procedure used for soil...

Accuracy of coarse-scale land quality maps as a

function of the upscaling procedure used for soil data

Volker Hennings

Federal Institute for Geosciences and Natural Resources, Stilleweg 2, 30655 Hannover, Germany

Received 3 April 2000; accepted 24 September 2001

Abstract

A thoroughly investigated study area in Northern Germany was used to determine the accuracy of

coarse-scale land quality maps (1:200,000 and 1:1,000,000), and to evaluate possible improvements

by applying different upscaling procedures on fine-scale soil data (1:5000). The land quality chosen

for this investigation was the mean annual groundwater recharge rate, obtained by a deterministic

simulation model of the soil water balance. Three versions of soil maps were compared and evaluated:

the existing soil maps versus new soil maps, created by selecting region-specific soil profiles without

changing the boundaries of the existing map units or created by direct aggregation of map units. In the

study area, characterized mainly by glacial deposits, the taxonomic criterion of the upscaling proce-

dure is irrelevant for the accuracy of land quality maps at the 1:1,000,000 scale. The results can be

used to divide the total error into two parts: at the 1:200,000 and 1:1,000,000 scale, about 75% of the

error is caused by soil spatial variability and cannot be avoided at these scales; the remaining 25%

indicate the degree of uncertainty that is caused by the mapping or upscaling procedure and can be

eliminated to produce ‘‘best-possible maps’’. D 2002 Elsevier Science B.V. All rights reserved.

Keywords: Aggregation; Soil maps; Spatial variability; Upscaling

1. Introduction

1.1. Translating information on soil characteristics and qualities across different spatial

scales

Each of the soil-forming factors acts over a certain spatial scale, and within each soil-

forming factor there can be many spatial scales of interaction. Burrough (1983a) was one

0016-7061/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved.

PII: S0016 -7061 (01 )00148 -3

E-mail address: [email protected] (V. Hennings).

www.elsevier.com/locate/geoderma

Geoderma 107 (2002) 177–196

of the first soil scientists to use general models to describe the superimposed effects of

spatial variations in soils on the basis of published data. He compared fragments of soil

maps over several scales whereby each area drawn at a finer scale is nested within those

drawn at the coarser scales. One of his conclusions (Burrough, 1983b) was that ‘‘the detail

resolved is partly due to the scale of variation present, and partly to the resolving power of

the map at the given scale’’. Besides these comparisons of maps showing basic, easily

mappable soil characteristics, similar studies have focussed on more complex soil pro-

perties or land qualities obtained by simulation models: Bregt (1989) evaluated the

reliability of soil-moisture-deficit predictions at different scales. Wilson et al. (1996) used

various sets of soil and climate data to evaluate the impact of the resolution of the input

data from maps on model predictions of pesticide leaching. Variogram models at regional,

county and field scales, using soil salinity as an example, have been presented by Odeh

et al. (1998). However, there is little data and few geostatistical studies on soil and land

attributes over many scales. Only a few authors have observed nested behaviour when two

hierarchical scales are included (McBratney, 1992). McBratney (1998) concluded that the

sill variances seen on most variograms are simply a function of the geometric support and

the finite size of the region sampled.

1.2. Compilation of coarse-scale land quality maps in Germany

Soil protection or conservation decisions cannot be made without maps of land qualities,

such as retention capacity for heavy metals, vulnerability to erosion by water, and ground-

water recharge. The relevant decision-makers are more interested in the accuracy of these

land qualitymaps than of the corresponding soil maps. For this reason, when the reliability of

the results of a soil survey is evaluated, the priority interest is not on the purity of the soil map

units, but on errors in the land quality assessment based on existing soil maps. In this paper,

‘‘scale’’ is used depending on the degree of spatial resolution; ‘‘coarse scale’’ means ‘‘upper

scale’’ (1:1,000,000) and refers to a map covering a large area while ‘‘fine scale’’ means

‘‘lower scale’’ and refers to a map at the field or farm level.

The Federal Institute for Geosciences and Natural Resources (BGR) is preparing

nationwide land quality maps e.g., for the new Hydrological Atlas of Germany (HAD),

commissioned by the Ministry for the Environment as a tool to support its soil protection

policies (Bundesministerium fur Umwelt, 2000). For this reason BGR is involved in sev-

eral projects designed to assess the reliability of maps of this kind. On a national scale, the

main objective of such maps is not to provide a highly accurate, site-specific assessment of

the functions or vulnerabilities of the soil but to provide an estimate of the proportion of

the country affected by soil degradation or endowed with land qualities such as high re-

tention capacity for pollutants or a high agricultural yield potential. Current examples are

maps for the HAD project showing the plant-available water capacity in the root zone or

mean annual groundwater recharge.

In Germany, the state soil surveys are responsible for soil mapping and for publishing

soil maps at the 1:25,000 and 1:50,000 scale. All nationwide thematic maps are based on

the 1:1,000,000 Soil Map of Germany (BUK 1000) (Hartwich et al., 1995), which uses 72

map units. Since more detailed soil maps cover only about 35% of the country (Zitzmann,

1994), the 1:1,000,000 map could not be compiled by generalization of fine-scale map

V. Hennings / Geoderma 107 (2002) 177–196178

units and is not the result of an inductive upscaling procedure. Instead of using data from

soil survey institutions, this map is based mostly on expert knowledge about soil-forming

factors. The 1:1,000,000 Soil Map of Germany was compiled using a more or less de-

ductive procedure in which the available geological maps are interpreted. In this pro-

cedure, many soil map units were created by simply relabelling geological map units.

Because methodology and scale of a routine soil survey differ from one state to another,

this status has practical consequences for the 1:1,000,000 soil map:

� The accuracy or reliability of this map is unknown.� The potential for further improvements differs depending on the availability of fine-

scale maps.� When more fine-scale soil maps are completed during the next several years, BGR

will have to check and improve the master 1:1,000,000 soil map by upscaling the

available soil data. This demonstrates the need for an effective upscaling approach

and rule-based algorithms that are suitable for computerised map compilation.

1.3. Objectives of this study

The map compilation methodology of the master 1:1,000,000 soil map of Germany

(BUK 1000) described above and the user’s request for quantitative information on the

reliability of a soil survey institution’s products, require investigations on the accuracy of

coarse-scale land quality maps in selected areas. For that reason a study was carried out

with the aim of covering the following issues.

bThe accuracy of coarse-scale land quality maps, based on BUK 1000, had to be

examined for an area where detailed soil mapping results of full coverage are available

over several scales.

bBecause groundwater recharge is the theme of one of the next HAD maps, this land

quality was chosen for this study.

bSoil data at the 1:5000 scale serve as reference, regardless of any unknown internal

variability of the mapping units referred to. Based on these assumptions, soil-mapping

units at the 1:5000 scale are regarded as completely homogeneous and spatial variability of

soil hydraulic parameters below the field or plot level is neglected.

Four questions were formulated for the study presented here:

� What degree of accuracy is obtained at a given scale if land-quality maps are

derived directly from existing soil maps (1:25,000, 1:200,000, and 1:1,000,000)

without any modification of their boundaries and ‘‘typical profiles’’?� Is it possible to improve the accuracy of these land quality maps if they are based on

alternative soil maps created by selecting region-specific soil profiles without

changing the boundaries of the existing map units?� Is it possible to improve the accuracy of these land quality maps if they are based on

alternative soil maps created by direct aggregation of finer-scale map units?� What degree of uncertainty is caused by soil spatial variability and has to be accepted

as a scale-specific error, and what degree of uncertainty is caused by the mapping or

upscaling procedure and can be eliminated to produce ‘‘best-possible maps’’?

V. Hennings / Geoderma 107 (2002) 177–196 179

2. Materials and methods

2.1. Study area, available soil maps and groundwater recharge determination

This investigation covers the following aspects:

� comparison of soil maps at four scales: 1:5000 (DGK 5 Bo), 1:25,000 (BK 25),

1:200,000 (BSK 200) and 1:1,000,000 (BUK 1000);� a thorough investigation of a selected area, the ‘‘Vechta’’ sheet of the 1:25,000 Soil

Map of Lower Saxony (Benne, 1997), which covers 100 km2;� the land quality under consideration is the mean annual groundwater recharge,

defined as the mean annual seepage rate from the soil;� the 1:5000 map serves as reference.

The study area is located in western Lower Saxony (Fig. 1), the northwestern federal

state of Germany. The northern part of the area consists mainly of glacial deposits

covered by sandy loess, the southern part consists mainly of fluvial deposits and soils

with a high water table occur locally. This area was chosen because its soils cover a broad

range of parent materials, the water table varies in depth, and, because the 1:25,000

Vechta sheet was recently mapped, a digital database is available for all scales. Local soils

were classified as Podzoluvisols, Spodo-Stagnic Cambisols, Haplic Podzols and Fluvi-

sols.

The 1:25,000 soil map (BK 25) is the product of the routine soil survey of Lower

Saxony. This map is not the result of conventional mapping techniques, characterized by

about 3000–4000 auger drillings per map sheet or 30–40 drillings per km2. During the

early 1980s, new methods were developed in order to minimize fieldwork and to facilitate

direct interpretation of the soil type from digital data on soil-forming factors such as

geology, relief and land use over the last few centuries (spatial models for prediction of

soil types). The data with the greatest potential for soil type interpretation are the soil

assessment data at 1:5000 that constitute a ‘‘soil digital database’’, containing 150–300

profile descriptions for each sheet from the 1:5000 map. Before they can be used for

further interpretations, the soil assessment data have to be translated into the soil science

terminology by a special computer program. After these data and information from other

sources (e.g., geology, relief and historical land use) have been incorporated into a central

database and GIS system, the spatial prediction model can be run. The result is a

‘‘conceptional soil map’’ that can be used by the soil scientist as basic information at

the beginning of fieldwork. To draw boundaries at the 1:25,000 scale, he needs only 15–

30 additional auger drillings. When the local sheet of the 1:25,000 soil map is finished, the

profile descriptions at 1:5000 are checked to eliminate translation and interpretation

failures of the soil assessment data. At the end there is a new and retrospectively corrected

‘‘soil digital database’’ containing the most detailed information of the Lower Saxony Soil

Information System. These data act as reference values for all further validations of the

aggregation procedures applied for this study.

The 1:200,000 soil map of Lower Saxony (BSK 200) (Luders and Oelkers, 1975) was

compiled during the seventies and is mostly a pedological interpretation of the available


Fig. 1. Location of the 1:25,000 Vechta sheet in Lower Saxony.

V.Hennings/Geoderm

a107(2002)177–196

181

geological maps. Additionally, information about agroclimatic conditions was used to

subdivide Lower Saxony into soil landscapes.

The degree of spatial resolution at the different scales is illustrated by Table 1. Notice

that the 1:25,000, 1:200,000 and 1:1,000,000 soil maps were compiled at different times

and by different people completely independently of each other. The most detailed

information is given by the 1:5000 map, which is represented by almost 4500 profile

descriptions (polygons), while at the 1:1,000,000 scale only eight map units appear.

The mean annual groundwater recharge, defined as the mean annual seepage rate from

the soil, was obtained using a deterministic simulation model (‘‘GWNEU’’; Wessolek,

1989). Following the classification scheme from Wagenet et al. (1991), GWNEU is a non-

mechanistic or functional model of the soil water balance. The soil profile is represented

by two layers, the effective root zone and the subsoil. Soil hydraulic parameters were

estimated via pedo-transfer functions, which have been validated using a database of

nationwide soil data (Krahmer et al., 1995). All calculations are based on the same climatic

data, and the same crop rotation was assumed for the whole map sheet. At the 1:25,000

and the 1:200,000 scale, the groundwater recharge was calculated individually for the

dominant and the associated soils of each map unit. The result was weighted according to

the spatial percentages of each soil within a map unit. Minimum values near 0 mm are

associated with Histosols and Fluvisols with a shallow water table, maximum values near

300 mm are associated with Haplic Podzols from eolian sands. Modal values around 220–

230 mm correspond to Spodo-Stagnic Cambisols from sandy loess.

2.2. Formal approach to aggregation of soil map units and statistical criterion for

evaluation

According to the scale hierarchy and knowledge type diagram of Hoosbeek and Bryant

(1992), the knowledge type used for this investigation can be described as ‘‘quantitative-

empirical’’. As can be seen in Fig. 2, the same empirical model and the same realization of

pedo-transfer functions is used at all scales. When this approach is applied, the potentiality

for mechanistic understanding is limited.

Table 1

Soil databases for the different scales

Digital database

of the soil

Soil map of

Lower Saxony

Soil map of

Lower Saxony

Soil map

of Germany

information system

DGK 5 Bo

BK 25 BSK 200 BUK 1000

Scale 1:5000 1:25,000 1:200,000 1:1,000,000

Number of polygons 4496 436 40 10

Number of legend units 4496 soil profiles 37 10 8

Concept for

describing map units

1 soil profile per

polygon = 1 soil

profile per map unit

dominant and

associated soils

per map unit

dominant and

associated soils

per map unit

dominant soil

per map unit


In this paper, ‘‘upscaling’’ of soil information is done only by spatial aggregation of soil

map units. Upscaling in this sense can be done by various approaches, applying any of the

following criteria for aggregating spatial units: (i) a single property from typical profiles,

such as soil texture class or available water capacity; (ii) results of the allocation to a

classification system, i.e., soil groups or series; (iii) land qualities derived from basic

pedological data via pedo-transfer functions or models; and (iv) soil-forming factors

(morphographic units from digital terrain models, . . .). During aggregation, adjacent

polygons at the lower scale are pooled according to spatial neighbourhood, correspond-

ence in one of the above given criteria and a cartographically defined minimum size of the

new polygon. A minimum distance between points, vertices and arcs was also taken into

Fig. 2. Classification of the knowledge type of this investigation (after Hoosbeek and Bryant, 1992).


account. As a result, most of the boundaries from the lower scale are omitted while a few

remaining boundaries are emphasized to delimit the new mapping units at the upper scale.

In this study, only methods based on the second approach (ii) were compared and

evaluated, since it was important to use only information that is always available as

aggregation criterion and can be used for routinely applicable algorithms.

During upscaling, all map units at the lower scale corresponding to a given map unit at

the upper scale were identified and grouped according to the aggregation criterion (e.g.,

soil type, phase, . . .) and the spatial proportion. As a result, proportions of the areas

occupied by all dominant and associated soils within a given map unit at the upper scale

are known. Later results showed that the inaccuracy of the new map is not significantly

reduced by using complete soil associations (see Table 2). For that reason, in this in-

vestigation new map units at the upper scale are only described by the dominant soil type.

The average groundwater recharge rate was then calculated from the values for all sites

with the same soil type. These mean values are then used for the groundwater recharge

map at the upper scale.

As an initial step, finer-scale soil data were used to check the dominant soil type of

every map unit at the upper scales. In this case, all boundaries at the upper scales remained

unchanged and only the map legends were modified by new soil profile descriptions,

regarded as statistically representative for the individual soil association (‘‘first-stage

aggregation’’). As a second step, not only the map legends, but also the boundaries bet-

ween map units were modified while finer-scale soil data were used to create a completely

new map design (‘‘second-stage aggregation’’).

No matter whether ‘‘first’’ or ‘‘second-stage aggregation’’ was applied, for every scale

the new soil profiles, regarded as representative for the belonging map units, were selected

on the basis of soil information at the nearest lower scale. The database for the 1:25,000

scale are the 1:5000 data, the database for the 1:200,000 scale are the 1:25,000 data, etc.

The most detailed information at 1:5000 generally acts as reference data to quantify the

accuracy of the aggregation procedure’s result.

A measure of the deviation between two scales is the root of the mean squared difference

(RMSD) between values at the scale under consideration and the reference scale, calculated

for each 1:5000 map unit (polygon) and the corresponding area of the upper scale. These

errors were weighted for the whole map sheet as a function of the proportion of the total

Table 2

Errors in groundwater recharge maps at several scales when the existing soil maps and different concepts for

describing map units by representative profiles are used

Scale and map RMSD [mm]

Case 1:

map units are described

only by the dominant soil

Case 2:

map units are described by all

dominant and associated soils

1:25,000; BK 25 62.0 59.6

1:200,000; BSK 200 82.1 78.6

1:1,000,000; BUK 1000 83.7 –

RMSD: root of the mean squared difference between the scale under consideration and the 1:5000 reference scale.


area occupied by the map unit and averaged. When the deviation between two scales is

measured for every 1:5000 polygon, the huge number of single results can also be used for

more detailed interpretations, e.g., to calculate mean errors as a function of soil type, parent

material, etc. (Table 3). Because the main objective of this study were comparisons of dif-

ferent upscaling techniques, the RMSD measure as a global criterion was favoured for

obtaining one averaged result for the whole map sheet.

3. Evaluation of small-scale land quality maps

3.1. Results using existing soil maps

The first question formulated for this study was how reliable can a user expect a map to

be if all land quality maps are derived directly from the existing soil maps without any

modification of their map unit boundaries and the profiles of their legend and what degree

of accuracy is obtained at a given scale. A qualitative answer can be given by comparing

the resulting groundwater recharge maps (Fig. 3). Unfortunately, the 1:5000 reference

scale is not included in this figure because the more than 4000 polygons cannot be plotted

in this format. Notice that these three thematic maps do not reflect any upscaling

procedure. A preliminary qualitative interpretation would indicate that at the 1:200,000

scale the differentiation between till plateaus and valleys and the groundwater recharge in

some places corresponds well with those of the 1:25,000 scale, but at the 1:1,000,000 scale

the spatial pattern of the boundaries as well as the corresponding values appear to be

random.

A quantitative answer to the first question is given by Table 2 and illustrated by a

simple diagram (Fig. 4). In Table 2, two cases are distinguished: The values on the map (i)

were derived only from the dominant soils or (ii) were calculated according to the

proportions of the areas occupied by all dominant and associated soils. For the 1:1,000,000

Soil Map of Germany the second set of information is not available because all map units

Table 3

RMSD measures of the groundwater recharge for the map units of the 1:1,000,000 Soil Map of Germany

(BUK 1000)

Legend

unit no.

Main soil types

and parent material

Spatial

proportion [%]

RMSD

[mm]

6 Eutric Histosols 11.0 68.1

7 Dystric Histosols 2.7 95.4

10 Fluvisols/Gleysols from sandy to loamy fluviatile sediments 4.9 133.9

17 Haplic Podzols/Cambic Podzols from sandy fluviatile sediments 10.0 131.5

28 Spodo-Stagnic Cambisols/Stagnic Podzoluvisols

from loamy to sandy deposits over boulder clay

44.6 69.9

31 Cambic Podzols/Spodic Arenosols

from dry dystrophic sand deposits

2.5 90.9

33 Haplic Podzols/Dystric Regosols from dry dystrophic sand deposits 4.0 96.1

46 Haplic Luvisols/Eutric Podzoluvisols/Eutric Cambisols

from sandy loess over sand or loam

20.3 66.7


are only described by the dominant soil (Table 1). Associated soils are mentioned, but only

by soil types (for example, ‘‘Haplic Podzols, Cambic Podzols, . . .’’), while complete

descriptions of representative soil profiles including information about texture class per

Fig. 3. Mean annual groundwater recharge in the ‘‘Vechta’’ area derived from existing soil maps at several scales.


horizon, etc., are missing. The two concepts, compared in Table 2, lead to similar results.

The error is not significantly reduced by using complete soil associations to describe map

units and is therefore not worthwhile. A initial explanation for this fact has to stress that all

members of a complete soil association differ only in soil type or details of a genetically

based classification system. In general, proportions of the areas occupied by soils with a

shallow water table are underestimated at upper scales. A second possible explanation will

be given in Section 4.

Table 3 lists RMSD values which were calculated grouping the deviations of ground-

water recharge by the legend unit of the 1:1,000,000 soil map. Compared to the RMSD for

the whole map sheet (Table 2), results range from 66.7 mm (legend unit no. 46) to 133.9

mm (legend unit no. 10). Table 3 shows that the 1:1,000,000 groundwater recharge map

provides the most reliable information for soils developed from boulder clay or glacial

deposits covered by sandy loess. Its accuracy is deteriorating in areas characterized by

fluviatile sediments and changing water tables. But even the lowest RMSD result is close

to the mean value for the whole investigation area. This indicates that at the 1:1,000,000

scale, the accuracy of a land quality map is mainly dependent on internal soil spatial

variability within soil map units.

In Fig. 4, for every scale the upper line gives the error obtained when the existing soil

maps are used, the lower line indicates the best possible result if all detailed information at

the 1:5000 scale is used to calculate mean values within the map units of the existing soil

maps. The ‘‘corridor’’ or space between both lines indicates the potential for further map

Fig. 4. Accuracy of groundwater recharge maps at different scales when the existing soil maps are used without

any modification of their boundaries and ‘‘typical profiles’’.


improvements by alternative compilation strategies. Four conclusions can be derived from

this figure:

� The root of the mean squared difference of the groundwater recharge data relative to

the detailed data at the 1:5000 scale increases from 62 mm at 1:25,000 to 84 mm at

1:1,000,000.� The accuracy of a groundwater recharge map at the 1:25,000 scale cannot be

improved significantly by alternative concepts.� Both maps at the upper scales seem to be equivalent with regard to their accuracy.� The results can be used to divide the total error into two parts: At the 1:1,000,000

scale about 75% of the error (63/84 mm) is caused by soil spatial variability; the

remaining 25% indicate the degree of uncertainty that is caused by the map

compilation procedure and can be eliminated to produce ‘‘best-possible maps’’.

3.2. Results obtained using alternative soil maps created by selecting region-specific soil

profiles

The second question was whether it is possible to improve the accuracy of coarse-scale

land quality maps if they are based on alternative soil maps created by selecting soil profiles

typical of the region without changing the boundaries of the existing map units and which

criterion of profile selection is the best one. For this ‘‘first-stage aggregation’’ legends of a

coarse-scale map aremodified on the basis of soil profile data at the nearest lower scale while

Fig. 5. Criteria for soil profile selection and aggregation of map units during the upscaling process.


boundaries of the map units remain unchanged. Up to five variants of aggregation proce-

dures were applied using different taxonomic criteria for soil profile selection (Fig. 5).

During aggregation, according to ‘‘similar parent materials,’’ genetic aspects were neglected

and correspondence in the soil texture class was emphasized, e.g., eolian sands, fluvioglacial

sands, etc., were combined into one group. The heavy lines indicate the recommendation in

the German Soil Mapping Guidelines, which acts only as a first proposal and does not offer

rule-based algorithms to be used for decision-support systems (Domburg, 1994).

The second question can be answered by the diagram shown in Fig. 6. Each symbol

indicates one variant of profile selection given in Fig. 5. Beside a more detailed interpre-

tation, two general conclusions can be drawn from Fig. 6: For the 1:25,000 scale, integrating

information about the parent material leads to better results than procedures based only on

genetic information, but at the upper scales there are no significant differences. It would

appear as if a simple replacement of the profiles of the existing map legend by soil profiles

typical of the region lead to considerable improvement and errors that are close to those of

the best possible map if the mean values of all profiles at the 1:5000 scale are calculated for

each of the map units at the scale that is being considered.

3.3. Results obtained using alternative soil maps created by direct aggregation of map

units

The third question was if it is possible to improve the accuracy of coarse-scale land

quality maps if they are based on alternative soil maps created by direct aggregation of

Fig. 6. Accuracy of groundwater recharge maps at different scales produced on the basis of new soil maps

compiled using region-specific soil profiles without changing the boundaries of the existing map units.


Fig. 8. Potential to improve the accuracy of groundwater recharge maps at different scales by other soil maps than

the existing ones.

Fig. 7. Accuracy of groundwater recharge maps at different scales: using alternative soil maps created by direct

aggregation of map units.


finer-scale map units (‘‘second-stage aggregation’’) and which criterion of aggregation is

the best-suited one. Complete aggregation of map units was carried out only for the upper

scales because the detailed information of the reference scale was already used to generate

the boundaries of the 1:25,000 map (see Section 2.1). To aggregate map units, three

requirements had to be fulfilled: correspondence to the criterion used for that variant,

spatial neighbourhood, and a minimum size of the resulting polygon for cartographic

reasons.

The third question can be answered again by a diagram (Fig. 7) as already done in

Section 3.1. Two interpretations can be concluded from Fig. 7: To all appearances, the best

result is obtained when information about soil type and parent material is combined. But

even if the best variant is considered, the error does not fall short of the error of the best-

possible result if all detailed information at the 1:5000 scale is used to calculate mean

values within the map units of the existing soil maps. This result can also be illustrated by

a figure that is restricted to only the best variant of Sections 3.2 and 3.3 (Fig. 8). Again, it

looks quite clear that the two strategies to improve the accuracy of the existing coarse-

scale soil maps compared in this study lead to similar results. From the point of view of a

soil survey institution, this means that the more time-consuming and costly second-stage

aggregation procedure of map units is not worthwhile.

4. Summary and discussion

The validity of all this paper’s conclusions is limited to a certain land quality and a

study area characterized mainly by glacial and fluvioglacial sediments. A similar

investigation in another soil landscape or one focused on land qualities dependent on

soil chemical properties, may show completely different results.

A systematic overview of the scale-dependency of processes and the theoretical

constructs or models used to describe them has been given by Wagenet (1998). This

author also classified approaches to scale translation ‘‘up’’ and principal scale translation

failures. Bouma et al. (1998) presented a synopsis of upscaling procedures from similar

studies by various authors. Following their classification, the upscaling procedure applied

in this study (Fig. 2) belongs to group ‘‘2’’ (‘‘changing grid sizes at different scales’’).

According to McBratney (1998), this is one of the ‘‘M1’’ procedures, because inputs to the

model are aggregated and afterwards the model is run at the appropriate scale (Fig. 2).

Such a comparatively simple approach is generally linked to specific scale translation

failures: Wagenet (1998) mentions the interaction of multiple factors to produce unique

phenomena that acts as a major constraint on the ability to translate information over

several scales. This means that the upscaling procedure of this investigation is not as

sophisticated as demanded by McBratney (1998), who regards only variograms over many

scales and realizations of fractal theory as formal approaches to aggregation. Despite these

restrictions, results from Section 3 allow some general conclusions.

Burrough (1983b) was one of the first soil scientists to study superimposed effects of

the spatial variation of soil by comparing nugget variances and estimated fractal di-

mensions of published soil data and fragments of soil maps over several scales. Other

authors evaluated the reliability of land quality predictions at different scales, e.g., Bregt


(1989) for soil-moisture-deficits and de Vries et al. (1998) for soil acidification processes.

While increasing absolute errors with scale are observed, most of the maximum error is

often reached at a regional scale and then asymptotically approximating a final sill. Results

of this study show similar features (Figs. 4 and 6, 7, 8). The accuracy of the existing

groundwater recharge map at the 1:25,000 scale cannot be improved significantly even

when the soil map was compiled by direct upscaling, using detailed soil data at the 1:5000

scale. In contrast to this result, 1:200,000 and 1:1,000,000 land quality maps are

improvable if they are based on new or alternative soil maps created by using soil profiles

typical of the region or by direct aggregation of finer-scale map units.

Several authors tried to describe soil spatial variability as a function of soil-forming

factors by deriving general rules from existing publications. Wilding (1985) formulated the

hypothesis that soil spatial variability is closely allied with the parent material and in-

creases from loess and glacial till over fluviatile deposits to fluvioglacial sediments and

tectonic rocks. According to local geological conditions the amount of soil spatial vari-

ability in the study area, characterized mainly by glacial deposits, can be expected as

‘‘medium (to high)’’. Furthermore, the taxonomic criterion of the upscaling procedure in

the Vechta area does not make any difference.

To improve the accuracy of coarse-scale land quality maps, two strategies to create other

soil maps than the existing ones were compared: The result of replacing the soil profiles that

describe the legend units by region-specific soil profiles is equivalent to a completely new

map design in which all boundaries are generated directly from finer-scale map units.

For decisions at the national scale, the 1:1,000,000 map is the most important part of

the soil information system. Early comparisons of the utilities and the cost-effectiveness of

soil maps produced by different survey procedures and to different scales were already

made during the seventies (Beckett and Burrough, 1971a,b). Brus et al. (1992) evaluated

different strategies like revision and upgrading to endorse future decisions for updating

soil survey information. A similar question was raised about how to bring up to date and

improve the 1:1,000,000 soil map and what is the most effective time and cost reducing

strategy. The results indicate that an improvement of this product does not necessarily

require the completion of the mapping process at the 1:25,000 or the 1:50,000 scale by the

soil surveys of the federal states. Because direct aggregation of fine-scale map units is

negligible for the 1:1,000,000 target scale, a considerably improvement of the nationwide

soil map can be expected from the completion of the 1:200,000 map and a national

database of well documented typical soil profiles.

By explaining the phenomenon of soil spatial variability, from the beginning several

soil scientists tended to group spatial variability into broad categories, systematic and

random (s. Wilding and Drees, 1983). Over the past decades, soil survey institutions have

tried to minimize field mapping and to substitute conventional mapping methods by

modern procedures to facilitate direct interpretation of the soil type from digital data on

geology, relief and historical land use. In terms of the above-mentioned concepts, models

of this kind attempt to comprehend the systematic part of soil variation with information

on the soil-forming factors. In an investigation area north of the Harz Mountains in Lower

Saxony, Germany, Hennings (1991) quantified the degree of reliability that can be ob-

tained by applying such models: predictions based only on theory without control bore-

holes may be expected to have an error of 30–40% of the total area. At the 1:1,000,000


scale, about 75% of the error is caused by the spatial variability of soils and has to be

accepted as a scale-specific error; the remaining 25% indicate the degree of uncertainty

caused by the mapping or upscaling procedure and can be eliminated to produce ‘‘best-

possible maps’’.

Besides the above detailed conclusions, there is also the following general realization

for the user of a 1:1,000,000 land quality map: At this scale, about 75% of the error is

caused by soil spatial variability and cannot be avoided at this scale, boundaries between

map units do not appear to have a statistical basis, and the user of the existing soil map has

to expect or tolerate that the internal heterogeneity of the map units can differ completely.

A possible explanation for this situation in the Vechta area is given in Fig. 9.

A statistically well-founded boundary at the upper scale requires structures with long-

range variability at the lower scale like in Fig. 9a. More often there are structures with

short-range variability at the lower that are responsible for the randomness of a boundary

at the upper scale. Fig. 9b is typical of glacial till deposits, where old relief forms have

been smoothed by later erosion. Also widespread are fluvial patterns with anisotropic

structures like Fig. 9c. A feature such as that in Fig. 9d might represent small, irregularly

distributed bogs. All these spatial patterns are superimposed in the Vechta area.

As a consequence, a measure of soil spatial variability should be quoted in the legends

of coarse-scale soil maps. Conventional concepts to characterize legend units of soil maps

were already evaluated and criticised by many authors (e.g., Webster, 1994, Burrough et al.,

Fig. 9. Types of spatial patterns in the Vechta area.


1994). Some of them like Brown and Huddleston (1991) or Nordt et al. (1991) recommend

‘‘interpretive purities’’ or confidence intervals of diagnostic properties. Others like Brus

(1993) or Domburg (1994) propose methods like soil map stratification and geostatistical

tools to derive typical semivariograms for widespread mapping units. Burrough et al. (1994)

summarize the most important issues as follows: there is a general need for a systematic

spatial variability analysis of permanent properties in multiple delineations of soil series and

for confidence bands of predictive model results in dependence of the spatial resolution level

of the input data. As a consequence, the convention of describing map units on the basis of

typical soil profiles, which differ only in soil type or details of a genetically based clas-

sification system, is not sufficient. This conclusion is underlined by the results of Table 2

when two concepts for describing map units are compared. It is necessary to use at least

simple statistics, such as frequency distributions or confidence limits of the variance of input

parameter values for simple deterministic simulation models of water and solute transport

(e.g., available water capacity).

5. Outlook

The more general conclusions for a soil survey operating nationwide can be summar-

ized as follows.

bWhen investigations of a similar methodology are repeated in various soil regions,

legend units of a coarse-scale soil map can be grouped or classified according to their

internal spatial variability and priorities can be established for detailed mapping programs

that focus on widespread map units of a finer-scale soil map with nationwide coverage.

bKnowledge of the best suited upscaling procedure can be used by soil surveys to

manage decision-support systems; the development of rule-based aggregation algorithms

allows automated map compilation.

bIf the degree of reliability of the existing soil maps as well as of the ‘‘best-possible

maps’’ can be quantified by statistical measures, the relation between cost and utility of

further work to improve the existing soil maps can be estimated.

Besides the results given above, there are still some objectives for the future:

� testing of other upscaling procedures (e.g., using soil-forming factors as aggregation

criterion), probably not a promising task when interpretations of Fig. 8 are taken

into account;� repetition of this investigation, focussed on land qualities that are dependent on soil

chemical properties;� evaluation of the relative importance of the regionalization of climatic data;� evaluation of the relative importance of the regionalization of land-use data;� identification of the best-suited measures for quantifying soil spatial variability at

the 1:200,000 and 1:1,000,000 scales;� development of rule-based algorithms to be used in decision-support systems.

In general, there is no lack of thoroughly investigated study areas; there is a lack of

generalized rules to predict spatial variation from easily available information. Rules are


needed that can be transferred to other soil associations with similar soil-forming factors.

The need for an ‘‘information system of map quality data’’ was noted already in the

eighties (Marsman and de Gruijter, 1984).

At present, new approaches, such as pooled variograms, are being tested and the initial

results are being published by soil survey institutions in various countries. For example,

Domburg (1994) mentions such a ‘‘National Sampling Map Unit Program’’, conception-

ally designed to collect detailed quantitative information on the spatial variability within

map units of the 1:50,000 Soil Map of the Netherlands. If the initial results of this program

are available, perhaps the best-suited methodology for deriving pooled variograms at this

scale can be applied. But the question as to whether it is possible to guess parameters of a

variogram model from field experience, i.e., without local sampling and statistical anal-

ysis, will still have to be answered.

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