Soil mapping approach in GIS using Landsat satellite imagery and DEM data
ABSTRACT The objective of this study was to create base soil survey maps of the studied lands using Landsat satellite imagery and Digital Elevation Model (DEM) data in a GIS framework. Specific goals were to generate soil maps and to test the usage probability of slope class map overlies colour composite images as a preliminary map for soil survey in a hilly terrain. Surrogate soil-landscape data layers were derived from Landsat satellite imagery and a 10 m DEM. The data were also used to produce 3D-view with slope class boundaries superimposed Landsat image and relief shaded map as a colour map in order to select possible site of soil profile pits and to define physiographic units. Six soil series formed on two different physiographic units were determined, described and sampled. Soil profiles have been classified according to Soil Taxonomy and FAO-Unesco soil map of the World legend classification systems. The methodology was adequate for soil survey and mapping of some types of soils.
- Soil Science Society of America Journal - SSSAJ. 01/1995; 59(2).
- [Show abstract] [Hide abstract]
ABSTRACT: demonstrated the successful implementation of quanti- tative soil-landscape modeling on a broad landscape Soil-landscape patterns result from the integration of short- and scale useful for soil resource inventory and as a frame- long-term pedogeomorphic processes. A 2-ha hillslope catena in Cali- work for understanding soil-landscape function. Moore fornia shows short-distance variation in A horizon depth from 8 to 80 cm and in soil depth from 8 to .450 cm in convex to concave et al. (1993) and Gessler et al. (1995) showed strong positions. Similar variations in net primary productivity (NPP) and correlation and predictive utility between a digital ter- soil C represent significant information often not captured by soil rain index, the compound topographic index (CTI), and survey maps. Strong correlations between these measured soil- several soil properties. The CTI, often referred to as landscape variables and explanatory digital terrain attributes are used the steady-state wetness index, is a quantification of to develop quantitative soil-landscape models. We were able to ac- catenary landscape position. It integrates both landform count for between 52 and 88% of soil property variance using easily position and context through an index defined as computed terrain variables such as slope and flow accumulation. Spa- tial implementation of the models suggest lateral redistribution pro- CTI 5 ln(As/tan b) (1) cesses resulting in differential accumulation of C and soil mass in where A s is the specific catchment area (area (m 2 ) per convergent and divergent landscape positions. The models are explicit unit width orthogonal to the flow direction) and b is and quantitative, which enables their use for testing hypotheses about the slope angle. Small values of CTI generally depict the spatial distribution of fine-scale landscape and ecosystem pro- cesses and for parameterizing spatially distributed hydrological and upper catenary positions and large values lower cate- ecosystem simulation models. nary positions with an overall range typically from 2 to 12 for zero-order upland areas. The purpose here was to continue development ofSoil Science Society of America Journal - SSSAJ. 01/2000; 64(6).
- [Show abstract] [Hide abstract]
ABSTRACT: Cited By (since 1996): 7, Export Date: 11 January 2013, Source: Scopus, CODEN: CIJPD, doi: 10.1016/j.catena.2008.12.004, Language of Original Document: English, Correspondence Address: Castrignanò, A.; C.R.A.-SCA, Via Celsio Ulpiani n.4, 70125 Bari, Italy; email: email@example.com, References: Barca, E., Castrignanò, A., Masciale, R., Passarella, G., Sedda, L., Uncertainty assessment of groundwater quality index using sequential indicator simulation (2006) Final Proc. Int. Conf. Spatial Data Methods for Environmental and Ecological Process. Baia delle Zagare (FG), Italy;Catena. 01/2009; 77(1):28-38.
African Journal of Agricultural Research Vol. 4 (11), pp. 1295-1302, November, 2009
Available online at http://www.academicjournals.org/AJAR
ISSN 1991-637X © 2009 Academic Journals
Full Length Research Paper
Soil mapping approach in GIS using Landsat satellite
imagery and DEM data
Ertu?rul Aksoy, Gökhan Özsoy* and M. Sabri Dirim
Uludag University, Faculty of Agriculture, Department of Soil Science, 16059 Görükle-Bursa, Turkey.
Accepted 22 October, 2009
The objective of this study was to create base soil survey maps of the studied lands using Landsat
satellite imagery and Digital Elevation Model (DEM) data in a GIS framework. Specific goals were to
generate soil maps and to test the usage probability of slope class map overlies colour composite images as a
preliminary map for soil survey in a hilly terrain. Surrogate soil-landscape data layers were derived from
Landsat satellite imagery and a 10 m DEM. The data were also used to produce 3D-view with slope class
boundaries superimposed Landsat image and relief shaded map as a colour map in order to select possible site
of soil profile pits and to define physiographic units. Six soil series formed on two different physiographic
units were determined, described and sampled. Soil profiles have been classified according to Soil
Taxonomy and FAO-Unesco soil map of the World legend classification systems. The methodology was
adequate for soil survey and mapping of some types of soils.
Key words: Soil survey, soil mapping, soil classification, GIS, DEM, satellite data.
Soil is a valuable non-renewable resource and exists
throughout the World in a broad diversity. Different types
of soil exhibit diverse behaviour and physical properties.
It provides essential support to ecosystems and to human
life and society. Therefore, it is imperative to maintain soil
functions and qualities to sustain the ecosystem and the
human being (Blum, 1993; De Groot et al., 2002;
European Commission, 2006). This alarmed authorities
to plan and assess suitable parameters for land uses. It
has been recognized that the quality of land suitability
assessment and the reliability of land use decisions
depend largely on the quality of soil information used to
derive them (Mermut and Eswaran, 2001; Bogaert and
D’Or, 2002; Salehi et al., 2003; Ziadat, 2007).
Soil surveys are the main information source for
sustainable agriculture and land use management. Soil
survey mapping units are defined by the soil properties
that affect management practices, such as drainage,
erosion control, tillage and nutrition, and they involve the
whole soil profile (Soil Survey Division Staff, 1993). In
*Corresponding author. E-mail: firstname.lastname@example.org. Tel:
+90 224 2941538. Fax: +90 224 2941402.
Turkey, soil surveys are available only at a small scaled
(1:100,000) for most of the country and just a few small
part of it has detailed soil maps because of funding
limitations and governmental policies, as it is in most of
other developing countries.
The traditional methods are expensive and time con-
suming due to large number of observations. However,
advances in computer and information technology have
introduced new group of tools, methods, instruments and
systems. Rapid developments in new technologies such
as Remote Sensing (RS) and Geographic Information
System (GIS) provide new approaches to meet the
demand of resource related modelling (Mermut and
Eswaran, 2001; Salehi et al., 2003).
In recent years thematic mapping has undergone a
revolution as the result of advances in geographic infor-
mation science and remote sensing. For soil mapping
archived data is often sufficient and this is available at
low cost. Green (1992) stated that integration of Remote
Sensing within a GIS database can decrease the cost,
reduce the time and increase the detailed information
gathered for soil survey. Particularly, the use of Digital
Elevation Model (DEM) is important to derive landscape
attributes that are utilized in land forms characterization
(Brough, 1986; Dobos et al., 2000).
1296 Afr. J. Agric. Res.
A DEM is an electronic model of the Earth’s surface that
can be stored and manipulated in a computer (Brough,
1986). It provides greater functionalities than the quail-
tative and nominal characterization of topography. A
DEM can be manipulated to provide many kinds of data
that can assist the soil surveyor in mapping and giving a
quantitative description of landforms and of soil
variabilities. By itself the DEM can yield maps of slopes,
aspects, rate of change of slope, drainage network on
catchments areas (Brough, 1986; Brabyn, 1997).
Information derived from a DEM, such as elevation, slope
and aspect maps can also be used with the images to im-
prove their capabilities for soil mapping (Lee et al., 1988).
A Study by Hammer et al. (1995) indicated that slope
class maps produced from 10 m DEM appear to have
great potential use for soil survey and land use planning.
Moore et al. (1992) stated that with information on
geology and surface deposits a DEM could be used to
predict soil types. Bayramin (2001) tested the use of
DEM, satellite data, digital geological data to improve
mapping efficiency and quality of soil maps and deve-
loped a pre model for soil mapping for countries where
conventional soil surveys have not been completed yet.
Mora-Vallejo et al. (2008) applied digital soil mapping in
a 13,500 km2 study area in South-eastern Kenya with the
main aim to create a reconnaissance soil map to assess
clay and soil organic carbon contents in terraced maize
fields. Soil spatial variability prediction was based on
environmental correlation using the concepts of the soil
forming factors equation. The results were confirmed by
cross-validation and provide a significant improvement
compared to the existing soil survey.
Debella-Gilo and Etzelmüller (2009) used a DEM of 25
m grid resolution to derive terrain attributes to model the
relationship between WRB-1998 soil groups and terrain
attributes and predict the spatial distribution of soil groups
in Vestfold County of South-eastern Norway. Elevation,
flow length, duration of daily direct solar radiation, slope,
aspect and topographic wetness index were found to be
the most significant terrain attributes correlating with the
spatial distribution of the soil groups.
Moreover, many researches indicated optimistic results
on using digital data for soil surveys (Moore et al., 1993;
Odeh et al., 1994; Boer et al., 1996; Dobos et al., 2000;
Gessler et al., 2000; Wilson and Gallant, 2000; Bishop
and McBratney, 2001; Park et al., 2001; Zhu et al., 2001;
Florinsky et al., 2002; Park and Burt, 2002; Ziadat et al.,
2003; Ziadat, 2005; Bishop et al., 2006; Liu et al., 2006;
Castrignano et al., 2009), and numerous complements
related to the satellite data enriched with topographic
information for mapping natural resources have been
reported by many researchers (Frazier and Cheng, 1989;
Bhatti et al., 1991; Dinç et al., 1992; Dobos et al., 2000;
Odeh and McBratney, 2000; Ryan et al., 2000;
McBratney et al., 2003; Ziadat et al., 2003; Dobos et al.,
2006; Lagacherie et al., 2007; Hartemink et al., 2008;
Liberti et al., 2009).
The main goal of this research was to use digital
elevation model (DEM) and Landsat TM imagery for a
detailed soil survey work in a hilly terrain, as an alter-
native and improved method for mapping soil patterns. A
3D view of the landscape is generated to visualize the
soil and landform relationships. The final soil map of this
study was intended to analyze agricultural productivity
and to prepare the land capability and irrigation suitability
classification maps of the studied area.
MATERIALS AND METHODS
The study area is located on between 28° 12’ 30” and 28° 15’ 00” E, and 40°
14’ 00” and 40° 17’ 30” N in the Northwest part of Bursa province, Turkey
(Figure 1) and covers an area about 850 ha. The area has a Mediterranean
type climate with annual precipitation around 700 mm (Anonymous, 2006),
most of which occurs from December to May and possesses mesic soil
temperature and xeric soil moisture regime according to Soil Taxonomy
(Soil Survey Staff, 1999). The mean annual temperature is about 14.50°C
(Anonymous, 2006). Elevation in the study area varies from 230 to 30 m
above sea level and generally decreases from South to North. Agriculture is
the main land use in the area and sparse forest lands and orchards are the
other land cover types. The major agricultural crops are wheat, maize,
sunflower, pea and watermelon.
Integrated land and water information system (ILWIS 3.2) was
used to develop a GIS framework for the spatial analysis and image
processing software (ERDAS Imagine 8.2) was used for image ana-
lysis. Topographic maps scaled at 1:25,000, Landsat TM satellite
data (August 1998) and soil map of the Bursa province, scaled at
1:100,000, produced by General Directorate of Rural Service of
Turkey in 1995, were used for this study. The selection of the scene
was based on the minimization of the vegetation cover and low
cost. Thus, the selected scene had less vegetation cover, minimal
effect of surface roughness and the very low soil moisture content.
The remotely sensed data and soil maps were geometrically
rectified to a common Universal Transverse Mercator (UTM) coor-
dinate system optimally enhanced and histogram matched to be
comparable during the visual interpretation through ERDAS soft-
ware. The root mean square error (RMSE) for the rectified image
was < 0.5 pixel.
A DEM was generated with 10 m spatial resolution based on the
topographic maps and this data was used to generate a slope map
of the study area. The DEM data and the slope class map of the
study area were shown on the Figures 2 and 3 respectively. After
eliminating the speckle effects by smooth filtering a vector map of
the slope classes was produced by screen digitizing. The produced
vector format slope class map was overlaid to colour composite
Landsat image of the studied area to delineate soil boundaries and
other land features by visual interpretation. A 3D perspective view
map and a hill shade relief map were generated using the DEM. A
3D presentation of the landscape is required to visualize the soil
and landform relationships. Thus, a colour hill shade relief map with
slope classes was produced by overlying the final maps in order to
select possible site of soil profile pits and to define physiographic
units (Figures 4 and 5). After extensive fieldwork and sampling the soil
profiles, 27 mapping units were determined. The soil series and their
important phases were slope, texture, depth and stoniness which were
considered as basic mapping units. Henceforth, final soil map, scaled at
1:25,000 was produced after the final field checking and so the preliminary
soil map (scaled at 1:100,000) was corrected. Soil profiles were described
and sampled according to Soil Taxonomy (Soil Survey Staff, 1999, 2006)
and Schoeneberger et al. (2002). Necessary analysis for classifying and
Aksoy et al. 1297
Figure 1. Location map of the study area and false colour composite of Landsat TM image (band 543 as RGB).
1298 Afr. J. Agric. Res.
Figure 2. Digital elevation model (DEM) of the study area and
determining physical and chemical properties were done according to Burt
(2004). On the basis of morphological and physicochemical characteristics,
the soil profiles classified according to Soil Taxonomy (Soil Survey Staff,
1999, 2006) and FAO-Unesco soil map of the World legend (FAO-
Unesco, 1974, 1990) classification systems.
RESULTS AND DISCUSSION
With the detailed soil survey and mapping works at the
study area, six soil series formed on two different physio-
graphic units were identified and mapped in 27 mapping
units. Mainly the studied soils were formed on neogene
clay lime deposits at the eroded upland physiographic units
and the others formed on holocene colluvial deposits at the
lowland physiographic units. Most of the soils are shallow and
a few were very deep, with textures ranging from SCL to C.
Limitations such as salinity, sodicity and surface fragments
(rocks and stones) were not determined in the study area.
However, agricultural potential of the soils was restricted by the
steep slope, shallow soil depth and high amount of CaCO3
content of the sub-surface horizons. Organic matter contents
were generally low and decreased with the depth, but it was
high or moderate level in lands newly deforested for agricultural
The CEC was generally found high because of high clay
contents and clay type. The base saturation percentage was
high and often close to 100 % with the Ca+2+Mg+2 occupying
more than 95% of the exchange site. Average soil properties
for the upper 30 cm of the main soil types are given in Tables 1
and 2. Soil profiles investigated in the area have ochric
Aksoy et al. 1299
Figure 3. Slope class map of the study area.
Figure 4. 3D view of the study area and Landsat imagery as a colour map.
1300 Afr. J. Agric. Res.
Figure 5. Hillshade relief map of the study area and 3D perspective view.
Table 1. Some important chemical properties for the main soil groups in the study area (average values for the upper 30 cm).
Water soluble total
Typic Xerorthents 7.82 0.05
Pachic Calcixerolls 7.74 0.08
Typic Xerochrepts 7.25 0.05
Vertic Xerochrepts 6.40 0.09
Typic Calcixererts 7.40 0.10
Chromic Haploxererts 7.92 0.08
Table 2. Some important physical properties for the main soil groups in the study area (average values for the upper 30 cm).
Particle size distribution (%)
Soil classification pH
Exchangeable cation (cmol kg-1)
Sand Silt Clay
(cm3 water cm-3 soil)
and mollic surface horizons and some of them have
cambic horizon as a sub-surface horrizon. Based on
morphological properties and physicochemical analysis, soils
were classified as Entisol, Mollisol, Inceptisol and Vertisol
according to Soil Taxonomy (Soil Survey Staff, 1999, 2006)
and as Eutric Leptosol, Haplic Calcisol, Calcaric Cam-
bisol, Eutric Vertisol, Calcic Vertisol according to FAO-
Unesco soil map of the World legend (FAO-Unesco,
1974, 1990) classification systems (Table 3).
In the Entisols, only Ochric epipedon existence was
identified. The clay contents of the Vertisols in the area
were generally close to 50%. They are especially rich in
smectitic clay minerals reason to form cracks in summer
time. Ochric epipedon with cambic horizon was found in
Aksoy et al. 1301
Table 3. The classification of the soils according to Soil Taxonomy (Soil Survey Staff, 1999, 2006) and FAO-
Unesco (1974, 1990) classification systems.
Order Suborder Great group Subgroup
Entisols Orthents Xerorthents Typic Xerorthents
Mollisols Xerolls Calcixerolls Pachic Calcixerolls
Inceptisols Ochrepts Xerochrepts Vertic Xerochrepts
Vertisols Xererts Haploxererts Chromic Haploxererts
Calcixererts Typic Calcixererts
the Inceptisols. Moreover, there is no other horizon
definition except mollic epipedon formed on the surface
of the Mollisols. It is also suggested that all soil profiles
are still in developing phase. Over all we found the soils
are very high in clay and CaCO3 contents, but very low in
organic matter. They also have very weak structure to be
used for agricultural purposes. Hence, we strongly re-
commend that close attention should be paid for the soil
cultivation, irrigation system and time regarding the soil
The major photo-interpretation elements such as land
forms, relief, slope etc. are the cornerstones of the both
monoscopic and stereoscopic interpretation of satellite
images as well as aerial photographs for delineation of
soil boundaries. The disadvantages caused by the
absence of stereovision of the Landsat images during the
image interpretation for soil survey were eliminated by
using slope classes map and shaded relief map derived
from 10 m DEM. Viewing the topographic and satellite
data together provided an opportunity to look at the same
soil mapping units in both formats at the same time.
The results showed that, the slope classes map from
10 m DEM overlie Landsat images can easily be used for
soil survey with extensive ground truth where there are
proven close relationships between soils and topography
and soils are situated hilly terrain. But in flat areas the
contour lines alone did not enable easy interpretation of
soil variations. 3D view with slope classes boundaries
overlaid Landsat images and shaded relief map as a
colour map, can be used to define physiographic units, to
select possible site of soil profile pits and to distinguish
distribution of the soils. A 3D viewing of the landscape
helps the visual interpretation of images and the under-
standing of relationships between landscape elements.
Even though topography is a crucial factor in the spatial
distribution of soils, cannot explain everything itself.
Most importantly, close attention must be given to land
surveys and profile descriptions as well. Therefore, the
prediction could be improved if information regarding the
other soil forming factors is included with extensive field
works. Besides, the soil survey efficiency can be
increased by using large scaled geological map, high
resolution satellite data or black and white aerial photo
graphs and others. It further showed that digital terrain
analysis plays a strong role in digital soil mapping and
provides a high level of topographic detail. Landsat TM
satellite imagery (bands 5, 7) has a good potential for
responding to differences in soil properties and hence the
separation of soil types.
For long term productivity, soils must have a good and
right soil management. Depending on this, soil survey
works become more important spontaneously (Özsoy
and Aksoy, 2007). The results proved that using RS and
GIS technologies and integrating DEM, satellite data and
ancillary data are very powerful tool for soil survey and
the GIS based softwares are user friendly and can easily
be support necessary procedures for soil survey and
Anonymous (2006). Multi year and monthly precipitation and temperature
data reports of Bursa, Mustafakemalpa?a and Balıkesir Meteorol. Sta-
tions (in Turkish). Turkish State Meteorol. Service, unpublished, 32p.
Bayramin I (2001). Using geographic information system and remote
sensing techniques in making pre-soil survey. In proceedings book, 15th
Int. symposium on desertification ISD. Soil Sci. Society of Turkey, 13-
17 June 2000, Konya, Turkey.
Bhatti AU, Mulla DJ, Frazier BE (1991). Estimation of soil properties and
wheat yields on complex eroded hills using geostatistics and thematic
mapper images. Remote Sensing of Environ. 37:181-191.
Blum WEH (1993). Soil protection concept of the council of Europe and
integrated soil research. In: Eijsackers HAP, Hamers T, editors. Inte-
grated Soil and Sediment Research: A Basis for proper protection.
Kluwer Academic Publishers pp. 37-47,
Bishop TFA, McBratney AB (2001). A comparison of prediction methods
for the creation of field-extent soil property maps. Geoderma
Bishop TFA, Minasny B, McBratney AB (2006). Uncertainty analysis for
soil terrain models. Int. J. Geogr. Info. Sci. 20: 117-134.
Boer M, Barrio GD, Puigdefabres J (1996). Mapping soil depth classes
in dry Mediterranean areas using terrain attributes derived from a
digital elevation model. Geoderma 72: 99-118.
Bogaert P, D’Or D (2002). Estimating soil properties from thematic soil
maps. The Bayesian maximum entropy approach. Soil Sci. Soc. Am.
J. 66: 1492-1500.
Brabyn L (1997). Classification of macro landforms using GIS. ITC J.
Brough PA (1986). Principle Of Geographical Information Systems For
Land Resources Assessment. Oxford University Press, 194p.
Burt R (2004). (Editor in) Soil survey laboratory methods manual. Soil
survey investigations report no: 42, version 4,0. USDA-NRCS. US
1302 Afr. J. Agric. Res.
Government Printing Office, Washington, DC, USA.700p.
Castrignano A, Costantini EAC, Barbetti R, Sollitto D (2009). Accounting
for extensive topographic and pedologic secondary information to
improve soil mapping. Catena 77: 28-38.
De Groot RS, Wilson MA, Boumans RMJ (2002). A typology for the
classification, description and valuation of ecosystem functions,
goods and services. Ecol. Econ 41: 393-408.
Debella-Gilo M, Etzelmüller B (2009). Spatial prediction of soil classes
using digital terrain analysis and multinomial logistic regression
modeling integrated in GIS: Examples from Vestfold County, Norway.
Catena 77: 8-18.
Dinç U, Sayın M, Ye?igil ?, Güzel N, Kaya Z, Derici MR, Öztürk N, Aydın
M, Çullu MA, Gülüt KY, Tuli A, Pe?temalcı V, ?enol M, ?enol S, Kapur
S, Ye?ilsoy M?, Gök M, Çolak AK, Özbek H, Irmak S, Dinç AO, Aksoy
E, Karaman C, Ortaç ?, Kandırmaz HM (1992). Determination of
distribution and basic charasteristics of soil resources of the Mardin-
Ceylanpınar plains and preparing ideal land use plans. Prime Min. GAP
(Northeastern Anatolia Project) Regional Dev. Administration of the
Turkish Republic. GAP Pub. No: 52, Adana, Turkey.
Dobos E, Micheli E, Baumgardner MF, Biehl L, Helt T (2000). Use of
combined digital elevation model and satellite radiometric data for
regional soil mapping. Geoderma. 97: 367-391.
Dobos E, Carre F, Hengl T, Reuter H, Toth G (2006). (Editors in) Digital
soil mapping as a support to production of functional maps. Office for
official publications of the European Communities, Luxemborg. EUR
22123 EN, 68p.
European Commission (2006). Proposal for a directive of the European
Parliament and of the council establishing a framework for the soil
protection of soil and amending directive 2004/35/EC. Brussels
22/09/2006 Com(2006)232 final, 30p.
FAO-Unesco (1974). Soil map of the World legend, 1:5.000.000.
Unesco, Paris. 1: 59p.
FAO-Unesco (1990). FAO-Unesco Soil map of the World, revised
legend. World soil resources report No: 60, FAO, Rome. 119p.
Florinsky IV, Eilers RG, Manning GR, Fuller LG (2002). Prediction of
soil properties by digital terrain modelling. Environ. Model. Software
Frazier BE, Cheng Y (1989). Remote sensing of soils in the eastern
Palouse region with Landsat thematic mapper. Remote Sensing
Environ. 28: 317-325.
Gessler PE, Chadwick OA, Chamran F, Althouse L, Holmes K (2000).
Modelling soil landscape and ecosystem properties using terrain
attributes. Soil Sci. Soc. Am. J. 64: 2046-2056.
Green K (1992).Spatial imagery and GIS: integrated data for natural
resource management. J. For. 90: 32-36.
Hammer RD, Young NC, Wolenhaupt TL, Barney TL, Haithcoate TW
(1995). Slope class maps form soil survey and digital elevation models.
Soil Sci. Soc. Am. J. 59: 509-519.
Hartemink AE, McBratney AB, Santos MMI (2008). (Editors in) Digital
soil mapping with limited data. Springer, the Netherlands, 445p.
Lagacherie P, McBratney AB, Voltz M (2007). (Editors in) Digital soil
mapping: An introductory perspective. Developments in Soil Sci.
Elsevier, Amsterdam, 31:600p
Lee KS, Lee GB, Tyler EJ (1988). Thematic mapper and digital elevation
modelling of soil characteristics in hilly terrain. Soil Sci. Soc. Am. J. 52:
Liberti M, Simoniello T, Carone MT, Coppola R, D’Emilio M, Macchiato
M (2009). Mapping badland areas using Landsat TM/ETM satellite
imagery and morphological data. Geomorphol. 106: 333-343.
Liu TL, Juang KW, Lee DY (2006). Interpolating soil properties using
kriging combined with categorical information of soil maps. Soil Sci.
Society Am. J. 70(4): 1200-1209.
Mermut AR, Eswaran H (2001). Some major developments in soil
science since the mid 1960s. Geoderma. 100:403-426.
McBratney AB, Santos MMI, Minasny B (2003). On digital soil mapping.
Geoderma. 117: 3-52.
Moore ID, Gessler PD, Nielsen GA, Peterson GA (1992). Terrain analysis
for soil-specific crop management. In proceedings book, A workshop on
research and development issues, Minnesota Extension Service, 12-16
Moore ID, Gessler PE, Nielson GA (1993). Soil attribute prediction using
terrain analysis. Soil Sci. Soc. Am. J. 57: 443-452.
Mora-Vallejo A, Claessens L, Stoorvogel J, Heuvelink GBM (2008).
Small scale digital soil mapping in southeastern Kenya. Catena 76:
Odeh IOA, McBratney AB, Chittleborough DJ (1994). Spatial prediction
of soil properties from landform attributes derived from a digital
elevation model. Geoderma 63: 197-214.
Odeh IOA, McBratney AB (2000). Using AVHRR images for spatial pre-
diction of clay content in the lower Namoi valley of eastern Australia.
Geoderma. 97: 237-254.
Özsoy G, Aksoy E (2007). Characterization, classification and agricul-
tural usage of vertisols developed on neogene aged calcareous marl
parent materials. J. Biol. Environ. Sci. 1(1): 5-10.
Park SJ, McSweeney K, Lowery B (2001). Identification of the spatial
distribution of soils using a process-based terrain characterization.
Geoderma. 103: 249-272.
Park SJ, Burt TP (2002). Identification and characterization of pedogeo-
morphological processes on a hillslope. Soil Sci. Soc. Am. J. 66:
Ryan PJ, McKenzie NJ, O’Connell D, Loughhead AN, Leppert PM,
Jacquier D, Ashton L (2000). Integrated forest soils information
across scales: spatial prediction of soil properties under Australian
forests. For. Ecol. Manage. 138: 139-157.
Salehi MH, Eghbal MK, Khademi H (2003). Comparison of soil varia-
bility in a detailed and a reconnaissance soil map in central Iran.
Geoderma 111: 45-56.
Schoeneberger PJ, Wysocki DA, Benham EC, Broderson WD (2002).
(Editors in) Field book for describing and sampling soils, version 2.0.
National Soil Survey Center, Lincoln. NE.USDA-NRCS. 189p.
Soil Survey Division Staff (1993). Soil Survey Manual. Agricultural
Handbook No: 18. USDA-NRCS. US Government Printing Office,
Washington, DC, USA. 438p.
Soil Survey Staff (1999). Soil Taxonomy: A basic system of soil
classification for making and interpreting soil surveys, second edition.
USDA-NRCS Agric. Handbook No: 436. US Government Printing
Office, Washington, DC, USA. 871p+maps.
Soil Survey Staff (2006). Keys to soil taxonomy, 10th edition. USDA-
NRCS. US Government Printing Office, Washington, DC, USA. 332p.
Wilson DJ, Gallant JC (2000). Digital terrain analysis. In: Wilson DJ,
Gallant JC (Eds.) Terrain analysis: principles and applications. John
Wiley and Sons, New York pp.1-27.
Zhu AX, Hudson B, Burt J, Lubich K, Simonson D (2001). Soil mapping
using GIS, expert knowledge, and fuzzy logic. Soil Sci. Soc. Am. J.
Ziadat FM, Taylor JC, Brewer TR (2003). Merging Landsat TM imagery
with topographic data to aid soil mapping in the Badia region of
Jordan. J. Arid Environ. 54: 527-541.
Ziadat FM (2005). Analyzing digital terrain attributes to predict soil
attributes for a relatively large area. Soil Sci. Soc. Am. J. 69: 1590-
Ziadat FM (2007). Land suitability classification using different sources
of information: Soil maps and predicted soil attributes in Jordan.
Geoderma 140: 73-80.