沙漠研究 22-1, 291 -294 (2012 )"ICAL 1 / DT X"

Journal of Arid Land Studies－Refereed Paper－Detecting Soil Characteristics in Arid Land by Using Landsat ETM+:
Case Study of Beni-Swif, Egypt
Kunihiko YOSHINO*1), Khishigsuren NYAMSAMBUU2), Yudi SETIAWAN2) and Abeer ELWAN2)
Abstract: Remote sensing can discriminate deposits of target minerals. The objectives of this research are to characterize minerals
in the soil of Beni-Swif area in Egypt and to map the distribution of soil classes based on their mineral contents estimated from Landsat
ETM+ data. Eight typical minerals were identified based on spectral reflectance database in the module of ERDAS Imagine
software for Landsat ETM+ images. Eight minerals were identified according to the visual analysis; they are: Albite, Calcite,Dolomite, Gypsum, Halloysite, Kaolinite, Muscovite and Montmorillonite. According to the contents of each mineral estimatedfrom the soil surface reflectance of the Landsat ETM+ image, seventeen soil classes were classified and their distributions weremapped. The accuracy of the map was validated with 45 soil samples in the study site. This research shows remote sensing iseffective to characterize soil in the arid and semi arid regions.
Key Words: Landsat ETM+, Minerals, Soil characteristics, Soil spectral reflectance

2. Methods
1. Introduction
Mineral composition of the soil surface mainly affects on
soil behavior because it consists about 97% of the soil volume
in the arid regions (Schulze, 2002). Moreover, Kruse et al.
(1990) suggested that many minerals could be identified andcharacterized based on their reflected-light spectral
characteristics. Their types and concentrations determine
important properties such as texture and cation exchange
capacity. These properties may in turn have significant
effects on many other soil properties such as nutrient
availability for plant uptake, available potassium which
depends on its released amount from the weathering primaryminerals.
Many image processing techniques are used to extract
ground surface information such as soil mineral composition
from remote sensing spectral data. Information on mineralcomposition of soils can be derived from their spectralreflectance (Kruse et al., 1990). Datasets of soil spectralreflectance have been successfully used for prediction ofnumerous soil properties (Taylor et al., 2002; Gregory et al.,2006; Chun-Yu et al., 2009). Soil maps are very helpful for
better agricultural land management in arid lands. Therefore,
the objectives of this research are 1) to characterize minerals inthe soil of Beni-Swif area in Egypt and 2) to map thedistribution of soil classes based on their mineral contents
estimated from Landsat ETM+ data.

2.1. Study area
The study area is located in the arid region in the middle ofEgypt (Fig. 1). It is characterized by complexly folded andfaulted Cretaceous, Pleistocene and Eocene age rocks that hosta wide verity of mineral deposits (Ghorab et al., 1970). The
study area includes two main soil formations; one is thealluvial flood plain and another is westerns desert deposits.
Since they have been formed in various geological ages, theirmineral compositions are different among those soil properties.
2.2. Measurement of spectral reflectance of soil samples
In order to characterize minerals in the soil of Beni-Swifarea in Egypt from spectral reflectance of soil, some
topographic and geological maps were analyzed with the fieldwork. Then, forty five soil samples were collected in thestudy site. After that, they were air-dried, ground gently, andthen sieved through a 2 mm sieve. Figure 1 shows the study
area and the locations of soil samples.
The spectral reflectance of these sieved soil samples weremeasured with Shimadzu UV-3600 spectro-photometer andUVProbe software. The measuring wavelength ranged from220nm to 2600 nm. A white plate of BaSO4 was used as areflectance standard. The reflectance was measured every 1.0nm, so each soil sample had totally 2380 data points. Thesereflectance data were clustered by using the Matlab R2010
software, and classified into some groups of soil. Specificmineral components were detected by comparing absorption* Corresponding Author: sky@sk.tsukuba.ac.jp
1-1-1 Ten-noudai, Tsukuba, Ibaraki 305-8573
1) Faculty of Engineering, Information and Systems, University of Tsukuba2) Graduate school of life and environmental sciences, University of Tsukuba

Landsat

Soil

Atmosphericcorrection

Spectral reflectancemeasurement(220-2600nm)
USGS
Spectral
Libraryusing 9minerals

Subset thestudy area

Spectral analysisusing target detection
analysis and OSP(orthogonal subspace
j ti )
Tree Classification -4
classes for eachmineral map (high,mod, low, and poor)
Fig. 1. The study area (Beni-Swif) and soil sample points.

Mineralsvisualdetection –using thespecific2.2. Spectral information derived from satellite remotesensing imagery
In order to map the distribution of soil classes based on
their mineral contents estimated from satellite remote sensing
data., one Landsat 7 ETM+ image (path/row: 177/40)
acquired on May 3, 2003 was analyzed. The image wasgeometrically corrected and coordinate system was geographic
coordinate systems on datum World Geodetic System of 1984(WGS-84). Then, an atmospheric correction was done using
the COST method (Chavez, 1996), which considers a specificbias, gain value, solar angle, minimum and maximum value ofeach band. As a result, an atmospherically standardized
image was obtained.
The spectral analysis was performed using ERDASIMAGINE 2010 with the spectral library data. The target
detection module and orthogonal subspace projection (OSP)
method was applied for mapping mineral distribution. The
OSP algorithm attempts to eliminate all unwanted or undesired
spectral signatures within a pixel. Then, a matched filter
algorithm was used to extract the desired spectral signature
which exists in that pixel (Harsanyi and Chang, 1994). In thisstudy, this algorithm was applied to assess the contents oftarget minerals in pixels. Then, a map of the distribution ofsoil classes based on their mineral contents estimated fromLandsat ETM+ data was generated. Finally, using 45 soil

220 – 1350
nm
UV - VIS

Clusteringusing
MATLABsoftwareClassification themineral classes
according to the strong
absorptions

Accuracy

wavelengths in the minerals reflectance library (Clark et al.,2003).
As the results of in-situ experiments, differences of thespectral reflectance curves were clearly found between soil
samples depending on soil classes. Consequently, types ofsoil in the study site could be discriminated by using differenceof spectral reflectance of soil surface.

1350 –2600 nm
NIR

8 minerals

17 Different
Soil classes

Final soilminerals
map
Fig. 2. Flowchart of data analysis.

samples, the accuracy of the map was validated (Fig. 1).
2.3. Procedure of data analysis
Data analysis in this study includes several steps following
in-situ experiments, mineral spectral analysis, identification of
typical minerals in the study site, estimate minerals’ contents
and soil class mapping (Fig. 2).

3. Results and Discussions
3.1. Soil classification with spectral reflectance
Measured spectral reflectance of soil samples in the studysite were classified into seventeen soil classes by clustering.
Each soil class shows significantly different characteristics
such as soil color and texture (sandy or clayey). Soil in thedesert area has coarse texture and light color. Soil in the Nilevalley has finer texture and dark color. So, these seventeen
soil classes, which correspond to soil groups, have different
physical and chemical characteristics, and mineral contents.
Eight soil minerals were identified from soil samples according
to the visual analysis. They are Albite, Calcite, Dolomite,Gypsum,Halloysite,Kaolinite,Muscovite
andMontmorillonite.
As suggested by Clark (1999) and Hunt (1997), soil classes
in the study site can be discriminated using Landsat ETM+
image, information of mineral contents in the soil can beobtained, because each mineral has specific absorption
wavelength. After conducting OSP method to Landsat

25%).
Finally, according to the contents of each mineral estimatedfrom the soil reflectance, seventeen soil classes were classified.

Fig. 3. Soil class map based on mineral content.
Table 1. Mineral content s in each soil classClass of soil

Mineral contentsclassClass 1

Kaolinite (high), dolomite (poor), gypsum, muscovite(mod)Class 2Kaolinite (high), albite, calcite (low)Class 3Gypsum (poor), kaolinite (mod), hallosite (mod)Class 4

Kaolinite (high), gypsum (poor)

Class 5

Montmorilonite (high), muscovite (high), albite (high)Class 6

Hallosite (high), kaolinite (high), albite (high)Class 7

Hallosite (high), albite (high)Class 8

Albite (mod), kaolinite (mod)Class 9Kaolinite (high), gypsum (poor)

Class 10

Kaolinite (high), muscovite (high), montmorilonit (high),

3.2. Mapping of soil classes
Based on the above-mentioned results, a soil mineral map
was generated (Fig. 3). The map shows that the clay minerals(Halloysite, Kaolinite, Muscovite, Montmorillonite) areconsiderably concentrated in the alluvial plain, and the primaryminerals (Albite, Calcite, Dolomite, Gypsum) are quite
concentrated in the fringes of desert. Detailed information ofmineral contents in each soil class is given in Table 1.
The accuracy of the mineral maps was assessed using the40 soil samples in the study site. The overall accuracy of the(Albite, Calcite, Dolomite, Gypsum, Hallosite, Kaolinite,Muscovite, Montmorillonite) were 51.11%, 73.33%, 71.11%,15.56%, 68.89%, 71.11%, 48.89%, and 68.89%, respectively.
And, kappa-statistics for these minerals were 0.29, 0.60, 0.57,0.03, 0.53, 0.53, 0.21, 0.43, respectively. These classification
accuracies might be insufficient, but the estimated contents ofminerals potentially indicate soil characteristics regarding tomineral composition.
Many spots of greenish color (Class 10, 11 and 12) arefound in this map. These are spots where Muscovite is highlyconcentrated. As one of essential elements of Muscovite ispotassium, which is rich in the fertilizer for agriculture, thesespots might be agricultural lands near villages. According to
the local knowledge of this region, locations of these spots arecorresponding to villages.
These results show that Landsat ETM+ with a spatial
resolution of 30 m is useful for mapping soil characteristics,
especially in arid lands, because vegetation covers are less thanother climatic regions. These kinds of maps of soil
characteristics are very useful for agricultural land planningand land use management in wide areas.

4. Conclusion

albite (high)Class 11

Muscovite (mod), gypsum (poor)

Class 12

Albite (high), gypsum (high), muscovite (high)Class 13Gypsum (high), hallosite (poor)

Class 14

Kaolinite (low), Albite, calcite (mod)Class 15

Gypsum (high), calcite (mod)Class 16

Kaolinite (low), montmorilonit (poor)

Class 17Gypsum (high), albite, calcite (mod)ETM+ data, eight maps of each mineral content weredeveloped according to the level of contents of target minerals;
high content (more than 75%), moderate content (50% to75%), low content (25%-50%) and poor content (less thanMinerals in soil could be recognized using the spectralreflectance in the effective spectral range of 1.0 μm to 2.5 μm.
As a result of in-situ experiment, eight minerals wereidentified in the soil samples in the study site. They are Albite,Calcite, Dolomite, Gypsum, Hallosite, Kaolinite, Muscovite,and Montmorillonite.
Furthermore, a soil map was generated in taking into
account mineral composition in soil surface by using Landsat
ETM+ in this study. The results of accuracy assessment of
this map indicate that additional ground surveys are needed toevaluate accurately. However, some soil characteristic suchas uptake nutrient availability, potassium, Cation Exchange

Capacity, ventilation, permeability, bulk density, wind erosion
impact and the water saturation could be estimated from soil
mineral maps in the further studies.
Considering
meteorological, social and economical data, soil maps areuseful tools for the land evaluation, land management and land
resource planning.

Acknowledgment
Authors thank to Mrs. Abeer Elwan for her cooperation tothis research work while she was a research student at theUniversity of Tsukuba in 2009-2010.
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