IJERD – International Journal of Environmental and Rural Development (2015) 6-1

Research article
erdApplicability of Estimation Method for Soil Moisture in
Mongolian Grasslands using a Pattern Decomposition
MethodAYAKO SEKIYAMA*
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Email: a3sekiya@nodai.ac.jp

SAWAHIKO SHIMADA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, JapanMICHINARI YOKOHAMA
Faculty of Bioindustry, Tokyo University of Agriculture, Abashiri, Japan

HIROMICHI TOYODA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Received 1 January 2015

Accepted 1 April 2015(*Corresponding Author)

Abstract The objective of this research is to develop a method that will allow remotelysensed data to be used for estimating soil moisture and distinguishing vegetated and bareground areas in Mongolia. The study was conducted in central Mongolia, where climate
conditions and human impacts are resulting in degradation of grassland and damage to
grazing stock. The method employed was a pattern decomposition method using flat patternmodel for estimation soil moisture, which is developed by authors. A total of ten survey
plots were established in grasslands, and a spectroradiometer was used to measure spectral
characteristics of the soil in the visible and near-infrared wavelengths. Spectral reflectanceof the soil was measured, and used to derive the pattern decomposition coefficient andestimate soil moisture. These results were then compared to actual field measurements ofsoil moisture to verify the accuracy of the method. In addition, photographic images wereused to estimate the vegetation coverage for each survey plot, and the effects on vegetationon the accuracy of soil moisture were examined. The results showed a strong negative
correlation between water content in soil and the pattern decomposition coefficient for flatmodel, indicating that this method is capable of accurately estimating soil moistureregardless of soil type. A strong correlation was also found between the vegetation
coverage and the pattern decomposition coefficient for vegetation, suggesting that themethod can be used to estimate vegetation coverage. A high level of accuracy, however,
was achieved only for bare ground, indicating that the vegetation reflectance influences the
spectral reflectance of the soil, and the current method is thus not applicable to vegetatedareas.
Keywords pattern decomposition method, spectroradiometry, visible and near infrared
wavelength

INTRODUCTION
In recent years, a winter disaster known as Dzud, characterized by heavy snow and low
temperatures, has caused widespread freezing and starving damage to livestock in Mongolia. In
addition, it has also been suggested that sparse summer precipitation and resultant lack of soil
moisture has depressed vegetation growth (Kondoh et al., 2005). This lack of soil moisture leads tograssland degradation, which weakens livestock and makes them more susceptible to damage fromthe Dzud. Therefore it is important that the continuous monitoring for spatial distribution of soil
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moisture in the summer, and in addition, a method for estimating both soil moisture as well as
distinguishing between vegetated and bare areas, is needed for conserving grassland and predicting
and preventing livestock damage from the Dzud.
Satellite remote sensing is useful for retrieving information regarding surface conditions
iteratively and over a wide area. There are many remote sensing studies dealing with vegetated area,such as predicting plant diversity by vegetation condition from MODIS (Ranjeet et al., 2008), theestimation of biomass production using AVHRR-based vegetation health indices (Kogan et al.,2004), and the evaluation of the vegetation phonological pattern using NDVI (Lee et al., 2002).
Few studies, however, deal with soil moisture.
For bare soil, many studies estimate soil moisture using microwave remote sensing. This is a
useful wavelength that can be employed either day or night, and is not affected by clouds. In recent
years, a synthetic aperture radar (SAR) active sensor has been frequently used with spaceresolution enhancement (Zribi et al., 2010). In conservation of Mongolian grasslands, however, it isalso important to distinguish between bare and vegetated areas. Some preliminary research using
the microwave method for this purpose has been implemented, but at present the system is still inthe developmental stage.
Some studies have also achieved high accuracy in estimating soil moisture using water
absorption in the near and mid infrared wavelengths (Ishiyama et al., 1992; Liu et al., 2003). Water
absorption bands, however, occur in a narrow width of the near and mid infrared wavelengths, andmany satellites do not have sensors that operate in these wavelengths.
To develop a method for estimating soil moisture that is both widely applicable to remotelysensed data and capable of distinguishing between vegetated and bare areas, Sekiyama et al. (2010)
conducted an open-air experiment employing a spectroradiometer, and achieved highly accurate
estimates of soil moisture using spectral characteristics of the soil in the visible and near-infrared
bands. The reflectance in each band was normalized by the sum of the reflectance in four bands:
blue, green, red, and near infrared, and an obtained curve of the band reflectances (band reflectancepattern) was utilized. By applying a pattern decomposition method for analyzing mixel
decomposition proposed by Fujiwara et al. (1996), Sekiyama et al. (2010) developed a method forusing the pattern decomposition coefficient for flat model calculated from the band reflectancepattern as the index of soil moisture. This method is described in detail in the next section.
In this study, we applied the method of estimating soil moisture developed by Sekiyama et al.
(2010) to field data acquired by spectrometer in the Mongolian grasslands. We attempted thismethod’s ability to accurately estimate soil moisture and to distinguish between bare and vegetatedareas. Furthermore, we verified the upper limit of vegetation coverage at which soil moisture canbe accurately measured by this method.
METHODOLOGY
Estimation of Soil Moisture by the Pattern Decomposition Method
The pattern decomposition method is an un-mixed analysis, in which the measured band
reflectance pattern is expressed as the linear sum of three typical band reflectance patterns which
are water, vegetation and soil. Several studies have applied this method to estimation of vegetation
coverage (Zhang et al., 2007a; Muramatsu et al., 2007) and to classification of land cover and land
cover changes (Muramatsu et al., 2000). Zhang et al. (2007b) have also applied the method todatasets of various satellite sensors. Until recently, however, there have been no studies that use
this method to estimate the soil moisture condition.
Sekiyama et al. (2010) first applied the pattern decomposition method to the soil spectralreflectance obtained by spectroradiometer in open-air experiments. The soil samples were collectedfrom 8 sampling sites in Japan, Mongolia and Malaysia. Reflectance measurements were taken foreach sample at 4-8 different values of water content in soil (WC), ranging from 0%-22.9% (N= 53).
Four multi-spectral bands of blue (band 1: 470–502 nm), green (band 2: 539–580 nm), red (637–668 nm) and near-infrared (band 4: 802–870 nm) were calculated from the measured hyper-spectralⒸ ISERD

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reflectance data, with a view to application in satellite imagery. The measured spectral radiance
was calibrated using the spectral radiance of a white calibration board to convert into spectralreflectance. The soil spectral reflectance in each band was normalized by dividing by the sum of
the four bands (Ono et al., 2002), thus defining the normalized band reflectance for soil Si (bandnumber: i = 1 to 4), which has the property that S1 + S2 + S3+ S4 = 1.
Generally, the spectral reflectance of soil in bands 1–3 (visible) and band 4 (near infrared)
decreases with increasing soil moisture creating a spectral reflectance that increases with increasing
wavelength. The normalized band reflectance for soil, however, decreases slightly in the visible
bands with increasing soil moisture, but increases in the near infrared band. The change ofreflectance in the near infrared band with increasing soil moisture is large compared to that in thevisible bands, and the slope of the band reflectance (i.e., the reflectance pattern) between bands 3
and 4 may thus be used to estimate soil moisture. The slope between bands 3 and 4 is steep when
the soil moisture is high, and flattens when the soil moisture is low. In the pattern decomposition
method, the flat model pattern Pfi (Pfi = 0.25) is defined as the pattern that corresponds tocompletely dried soil, and each normalized band reflectance for soil Si is the linear sum of two
patterns (flat model and soil). Explicitly,
Si = Csf Pfi + CssPsi,(1)where Csf and Css are the pattern decomposition coefficients, which are the relative weights ofthe two patterns. The decomposition coefficients are calculated from Si using the least squares
method, the flat model pattern Pfi, and the typical soil pattern Psi.
In Sekiyama et al. (2010), a negative correlation (r2= 0.56) was found between WC and Csf(Fig. 1), suggesting that Csf is an effective index for estimating soil moisture.

Fig. 1 Relation between water content in soil (WC) and pattern decomposition coefficient forflat model (Csf) (Sekiyama et al., 2010)
Field Survey
Survey area: In this study, ten 40 m × 40 m plots, including pasture and semi-desert areas in threeprefectures around the city of Ulan Bator in central Mongolia were investigated (Fig. 2a). The fieldsurvey was performed from 9:00 at 12:00 (local time) on August 9 to 16, 2006 and August 8 to 14,
2007, on clear days (Table 1) with sun elevation ranging from 40 to 50 degrees.
The vegetation of Mongolia is predominately grassland. Annual precipitation is below 400
mm, 70% to 80% of which occurs from June to August in central Mongolia. Kondoh et al. (2005)
have reported that the growth of plants depends heavily on summer precipitation. Although
grassland degradation due to overgrazing and the expansion of cultivated land has become aproblem in recent years near Ulan Bator (Yoshihara et al., 2008).
Each plot was taped off into a grid with 10 m intervals (Fig. 2b). At each grid point wemeasured the spectral radiance of the surface, photographed the surface, and collected soil samples.
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The surfaces of all the plots were covered with herbaceous plants. Therefore, the spectral radianceof vegetation and soil both contributed to the measured spectral radiance.

a

b

40m

Sampling grid point(n, n), n =1,..,5
ca. 30 cm φ

Fig. 2 Map of Mongolia showing the surveyed district (a) and 10 lettered survey plots (b)Table 1 Location of survey plot and land use

Plot
E
F
G
H
L
M
N
R
S
X

Latitude (N)48°01’ 04.2”
48°00’ 09.7”
46°36’ 28.8”
46°40’ 39.2”
47°40’ 59.9”
47°46’ 59.2”
47°54’ 55.0”
48°18’ 41.3”
48°28’ 42.3”
48°05’ 16.4”

Longitude (E)109°06’ 53.7”
109°01’ 30.4”
105°53’ 57.8”
105°54’ 51.2”
106°38’ 46.6”
109°14’ 51.7”
109°26’ 09.6”
106°46’ 16.7”
106°45’ 58.0”
109°20’ 16.8”

Land use
Pasture land
Pasture land
Pasture land
Pasture land
Pasture land
Pasture land
Pasture land
Abandoned farmland
Pasture land
Pasture land

Year
2007
2007
2006
2006
2006
2007
2007
2006
2006
2007

Spectral Radiance Measurement
The spectral radiance of the surface for each grid point (25 samples per plot) was measured using aspectroradiometer (Eko Instruments Co., Ltd, MS-720, with a wavelength range of 350– 1050 nm).
The field of view (FOV) of the sensor is 25 degrees, and the measurement was taken at a height of
70 cm above the ground, so the measured area was a 30 cm diameter circle. At each grid point, thespectral radiance was measured three times and the average value was used as the datum for thegrid point. The measured spectral radiance was converted into spectral reflectance dividing by thespectral radiance of a white calibration board, and the normalized band reflectance was calculated
from the spectral reflectance. The reflectance from a mixed surface of vegetation and soil at eachgrid point was defined as the normalized mixed-band reflectance Mi. In addition, the sensor FOV
was changed to 10 degrees to measure the pure spectral radiance from the bare soil and from thedominant plants (e.g., Poaceae, Rosaceae, Fabaceae, and Asteraceae) in each plot. The reflectancevalues of the bare soil and the vegetation (i.e., the dominant plants) were defined as the normalizedband reflectance for soil Si and the normalized band reflectance for vegetation Vi, respectively, foreach plot.
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Calculation of Vegetation Coverage and Soil Sampling
Photographs of each plot were taken using a digital camera set at almost the same position as thatof the spectral radiance measurement described in the preceding paragraph. The vegetation
coverage VC was calculated by extracting pixels of vegetation from the photographic image (N=250).
In 2006, 50 cm3 of surface soil were taken at each grid point for four plots (25 samples perplot). In 2007, soil samples of the same volume and depth were taken from 15 grid points (column
numbers of the grid are 1, 3, and 5) in four different plots. No soil samples were taken in plot E orplot R (Fig. 2). The total number of sampled plots was N= 160. The WC for each sample using theoven-dry method, the soil was kept under dry conditions (WC = 0 %).
Calculation of Pattern Decomposition Coefficient
The normalized band reflectances for soil Si and the normalized mixed-band reflectance Mi of allplots were analyzed using the pattern decomposition method. Si was decomposed using the flatmodel pattern Pfi (= 0.25) and the typical soil pattern Psi via Eq. (1), where soil pattern Psi is theaverage value of the Si (band number: i= 1 to 4) from all plots (Ps1, Ps2, Ps3, Ps4: 0.13, 0.19, 0.27,
0.41).
Similarly to Si, Mi was decomposed using the flat model pattern and the soil pattern. Explicitly,
Mi = CmfPfi + CmsPsi,(2)where Cmf is the pattern decomposition coefficient for flat model and Cms is the patterndecomposition coefficient for soil.
RESULTS AND DISCUSSION
Spectral ReflectanceFig. 3 Soil reflectance pattern (Si) in each plot (a); and measured reflectance pattern (b)Reflectance pattern (Vi) of soil and vegetation are average values of all plots,
mixed reflectance pattern (Mi) is an average value of all grid points in plot N

The soil reflectance patterns were upward-sloping and linear, and the slopes of the reflectancepatterns differed among the plots (Fig. 3a). To indicate the tendency of each reflectance pattern, thesoil and vegetation reflectance pattern averaged over all the plots is shown in Fig. 3b. Although
vegetation was strongly reflected in bands 2 and 4, absorption in bands 1 and 3 showed thecharacteristic reflectance pattern of vegetation. Mi was the reflectance from mixed soil andvegetation and was located near the mean value of the reflectance of soil and vegetation in bands 3
and 4. The reflectance patterns for plot N are shown in Fig. 3b, but the same tendencies wereobserved in the other plots as well.
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Vegetation Coverage and Soil Moisture
The average value of vegetation coverage (VC) and water content in soil (WC) in each plot isshown in Table 2. The vegetation coverage was comparatively high for surfaces covered with richvegetation. In plot M, for example, the surface was bare soil sparsely dotted with Poaceae, while inplot X the surface was densely covered by Poaceae and Asteraceae.
Table 2 Vegetation coverage (VC), water content in soil (WC) (The maximum, minimum andaverage value) and dry density in each survey plot
Plot
E
F
G
H
L
M
N
R
S
X

Min
18.5
2.0
13.6
15.2
37.2
6.6
5.0
28.5
52.2
2.1

VC (%)Max
Average
72.5
52.2
11.4
6.6
47.0
32.3
69.2
42.0
94.0
54.5
51.2
19.4
66.3
38.1
85.0
54.7
93.4
81.6
32.5
13.9

Min
－
1.6
1.5
1.0
0.9
5.3
5.8
－
4.5
1.0

WC (%)Max
Average
－
－
3.3
2.6
3.1
2.3
1.9
1.3
4.4
2.9
15.8
9.7
10.8
8.0
－
－
20.8
13.4
23.4
1.5

Dry density (g/cm3)
－
－
1.4
1.4
1.1
－
－
－
0.7
－

Estimation of Soil Moisture and Effect of Vegetation on Estimation Accuracy
Soil moisture estimation from the pattern decomposition coefficient for flat model: Many
studies have been done with visible and near-infrared wavelengths to estimate soil moisture using asoil line. In the scatter diagram of soil spectral reflectance values, where red is plotted on thehorizontal axis and the near-infrared on the vertical axis, the data form a straight line (the soil line;
Baret et al., 1993). Points on the scatter diagram that correspond to low water content in soil (WC)are located near the origin. However, the soil type must be classified in order to accurately estimate
the soil moisture using this method because the slope of the soil line depends on the soil type. The
soil line method is thus difficult to apply over a wide area utilizing remotely sensed data.
In contrast, the pattern decomposition method using flat model pattern depends less on thephysical characteristics of soils. In our study, the regression analysis between WC and Csf indicateda negative correlation (WC = −26.6 Csf + 6.73, r2 = 0.89) across various soil types (Fig. 4). These
results show that the Csf derived from field survey data is also an effective index for soil moisture
estimation, and high accuracy can be obtained without needing to classify the soil type.
Relationship between pattern decomposition coefficient and soil moisture: The patterndecomposition coefficient for flat model Cmf was calculated from Mi using Eq. (2), and theregression analysis performed using and WC and Cmf. No relationship between WC and Cmf (r2 =0.27) was found. In order to verify whether WC information is included in other coefficients, thefollowing additional pattern decompositions were conducted using patterns of flat model, soil, andvegetation:
Mi = Cms3Psi + Cmv3Pvi,(3)Mi = Cmf4Pfi + Cmv4Pvi,(4)Mi = Cmf5Pfi + Cms5Psi + Cmv5Pvi,(5)where the typical vegetation pattern Pvi is the average over all the plots of Vi (band number: i = 1 to4) from all plots data (Pv1, Pv2, Pv3, Pv4: 0.10, 0.16, 0.14, 0.60).
No significant correlation between WC and the pattern decomposition coefficients was found.
A strong correlation, however, was found between VC and the pattern decomposition coefficientfor vegetation (Cmv3, Cmv4, and Cmv5) (Fig. 5a). Although a correlation between VC and theⒸ ISERD

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normalized difference vegetation index: [NDVI, (band 4 − band 3) / (band 3 + band 4)] was noted,(Fig. 5b), the determination coefficients of NDVI were almost the same as that of Cmv. These
results suggest that Cmv is also a valuable index for estimating VC.
20
y = -26.6x + 6.73
R = 0.89

WC (%)

15
10

5
0
-0.4

-0.2

0
Csf

0.2

0.4

Fig. 4 Relation between water content in soil (WC) and pattern decomposition coefficient forflat model Csf

Fig. 5 Relation between vegetation coverage (VC) and pattern decomposition coefficient forvegetation Cmv (a)Three combinations, Cmv3, Cmv4, and Cmv5, are calculated by the pattern decomposition method using soil andvegetation pattern, flat model and vegetation pattern, flat model and soil and vegetation pattern, respectively

Effect of Vegetation on Estimation Accuracy
The upper limit of vegetation coverage at which the pattern decomposition method using flat model
pattern is viable was determined. Sekiyama et al. (2010) reported that estimation of soil moistureby this method was not possible when VC was over 50%. In this research, estimations wereobtained for VC ranging from 0-50% (Table 3), and regression analysis was conducted using the
WC and Cmf for each VC class. The results show that accuracy was high in the no-vegetation class(VC = 0%), but lower among the vegetated classes (VC > 0%). This leads to the conclusion that thepattern decomposition method using flat model pattern as developed by Sekiyama et al. (2010) canbe effectively used only on bare soil with no vegetation. As a cause of no effectively method formixed surface of vegetation and soil, it is supposed that physical and chemical characteristics ofvegetation to spectrum is too strong compared with soil, and there is also a possibility that thiscoefficient is not able to estimate the water content because water content information of vegetationand soil is mixed. Thus, this coefficient is an inadequate indicator for water content when samples
are contaminated by vegetation signals. Furthermore this method may not have been able to detectsoil water content due to slight difference of soil water content in each plot. In fact, soil waterⒸ ISERD

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content was 3 % or less in almost plots but soil samples were controlled various water content from
0 % to 20% in experiment for developing estimation method of soil content
For VC > 0%, the decomposition of Mi into the flat model pattern was affected by the highreflectance of vegetation in band 4 and the low reflectance in band 3. As a future refinement of thismethod, a separation (pre-decomposition) of the vegetation reflectance from the mixed data, which
may be achieved using the relationship between VC and Cmv, is required. Such a refinement would
allow the method to accurately extract WC from areas of mixed soil and vegetation.
Table 3 Relation between water content in soil (WC) and the pattern decomposition
coefficient for flat model (Csf) at each vegetation coverage (VC)
VC (%)
0
0-5
5-10
10-20
20-30
30-50

Estimate accuracy of WC (r2)
0.89
0.10
0.29
0.02
0.01
0.006

P value
P < 0.01
P > 0.05
P > 0.05
P > 0.05
P > 0.05
P > 0.05

CONCLUSIONSIn this study, a method for estimating soil moisture developed by Sekiyama et al. (2010) wasapplied to field data acquired in the Mongolian grasslands. The following conclusions wereobtaind:・The bare soil of the Mongolian grasslands exhibits a negative correlation between water content
in soil (WC) and the pattern decomposition coefficient for flat model Csf, which was calculatedusing the pattern decomposition method modified by Sekiyama et al. (2010). This indicates that Csf
is a useful indicator of WC in bare soil.
・A strong correlation was found between the vegetation coverage and the pattern decomposition
coefficient for vegetation Cmv. This indicates that Cmv be used not only to extract bare soil
information, but to estimate the vegetation coverage and NDVI as well.
No significant relationship between WC and the pattern decomposition coefficient for flatmodel Cmf was found in the decompositions of the reflectance patterns from vegetated surfaces.
This indicates that the method as proposed by Sekiyama et al. (2010) is influenced by vegetation,and is thus capable of accurately estimating soil moisture only on bare soil.
ACKNOWLEDGEMENTS
This research work was supported partly by the Japanese Ministry of Education, Culture, Sports
and Science (MEXT) under the Academic Frontier Joint Research Program for Studies on Natural
Environment and Culture in Asia of Tokyo University of Information Sciences.
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