Mapping and Modeling of Soil Salinity Using
WorldView-2 Data and EM38-KM2 in an Arid
Region of the Keriya River, China
Nijat Kasim, Tashpolat Tiyip, Abdugheni Abliz, Ilyas Nurmemet, Rukeya Sawut, and Balati Maihemuti
Abstract
Soil salinity is one of the common factors leading to land
degradation problems on earth, especially in arid and semi-
arid regions. There is an urgent need for rapid, accurate and
cost-effective monitoring and assessment of soil salinization.
Remote Sensing (
RS
) and Geographical Information Systems
(
GIS
) are employed as viable technologies for detecting, moni-
toring, and predicting spatial-temporal patterns of soil salini-
zation. The purpose of this study is to establish partial least
squares regression (
PLSR
) models that are based on remotely
sensed data and field measured electrical conductivity (
ECa
)
and to retrieve soil salinity estimates by constructing an opti-
mal model. First, the soil adjusted vegetation index (
SAVI
) was
calculated based on WorldView-2 images. Second, a statisti-
cal regression method was applied to analyze the correlation
between ECa and
SAVI
under different parameters. The
SAVI
that was measured as the most stable parameter was an op-
timum index. Finally, a
PLSR
prediction model of soil salinity
was established based on the sensitivity bands, the optimum
index and ECa. The results of this study are the following: (a)
According to the adjusted parameter (L = 100), the
SAVI
index
illustrated the best correlation with ECa, and ECa was also
significantly related to the bands ((Red Edge) Band6, (Near-
IR1) Band7 and (Near-IR2) Band8) derived from a World-
view-2 image. (b) The results of the
PLSR
predictive model
calibration showed that the model-D performed best through
the sensitivity bands and optimal index, with the highest
coefficient of determination (R
2
= 0.67) and the smallest root
mean square error (
RMSE
) of 1.19 dS·m
-1
. The results indicated
that the model-D that is constructed and applied in this paper
could provide quantitative information for detecting and
monitoring soil salinization in the Keriya Oasis and could
also supply examples for the study of soil salinization in arid
and semiarid regions with similar environmental conditions.
Introduction
Soil salinization is one of the most damaging environmental
problems worldwide, especially in arid and semi-arid regions
(FAO, 2010). Approximately 1.128 billion hectares of global
land are salt-affected (Wicke
et al
., 2011), and in China,
salinized areas account for approximately 9 percent of the na-
tional land resources (Liu and Diamond, 2005). Salinization
occurs in areas that have higher groundwater tables and have
parent materials that are rich in soluble salts, such as in the
agricultural areas of arid and semi-arid regions (Schofield and
Kirkby, 2003; Amezketa, 2006; Zhou
et al
., 2013). Saliniza-
tion leads to severe deterioration in soil quality that hinders
crop growth and regional agricultural production (Abbas
et
al
., 2013). Soil salinization is worsening due to anthropogenic
environmental impacts and climatic change, and as such, is
receiving worldwide attention (Wang
et al
., 2008; Bui, 2013).
Periodic identification, surveying, and detailed assessment
of the extent and severity of soil salinity are therefore of vital
importance to preventing and mitigating further salinization
(Metternicht and Zinck, 2003; Masoud, 2014; Sidike et
al
.,
2014). Detecting and monitoring soil salinization also became
an imperative for planning future agricultural development
and promoting regional ecological sustainability (Ding and
Yu, 2014). To this end, a rapid, accurate and economical ap-
proach is greatly needed. Remote sensing and
GIS
are feasible
technologies for detecting, monitoring and predicting spatio-
temporal patterns of soil salinization.
Saline soils contain evident spectral features in the visible
and near-infrared spectrum, and as such, salt contents of a
soil can be quantitatively analyzed by those absorption fea-
tures (Weng
et al
., 2008; Mulder
et al
., 2011). Microwave data
have also been observed to be effective for salinity informa-
tion extraction because their dielectric properties are sensitive
to the soil salinity, which is also a key element of electrical
conductivity (Aly
et al
., 2007). Therefore, both optical and
microwave data with different spectral and spatiotemporal
resolutions were widely used to characterize the severity of
soil salinization over the last two decades (Dwivedi
et al
.,
1992 and 1999; Metternicht and Zinck, 1996; Taylor
et al
.,
1996; Bell
et al
., 2001; Metternicht, 2003; Dehaan and Taylor,
2003; Douaoui
et al
., 2006; Fernandez-Buces
et al
., 2006;
Farifteh
et al
., 2007; Aly
et al
., 2007; Eldeiry and Garcia, 2008;
Ding and Yu, 2014; Masoud, 2014; Sidike
et al
., 2014; Aldabaa
et al
., 2015). Approaches have also been improved from early
methods of visual interpretation and quantitative analysis to
relatively complex quantitative inversion models (Dehaan and
Taylor, 2003; Masoud, 2014). However, monitoring of soil sa-
linity by remote sensing is constrained by the spatiotemporal
and vertical variability of salt concentration in a soil profile
(Mulder
et al
., 2011). Moreover, satellite remote sensing data
only provides information about land surfaces because there
is limited penetration depth into the soil profile. Accordingly,
electromagnetic induction (
EMI
) is widely considered to be an
effective technique that enables quantification of the spatial
variability of soil electrical conductivity and salinity (Brevik
et al
., 2003; Corwin, 2008; Doolittle
et al
., 2014; Miller, 2012).
EMI
sensors measure the apparent electrical conductivity (
ECa
)
of a soil at a certain depth and provides users timely, reliable
and quantitative geo-referenced measurements of soil salinity
Key Laboratory of Oasis Ecology, Ministry of Education,
Xinjiang University, Urumqi 830046, China, and the
College of Resources and Environmental Sciences, Xinjiang
University, Urumqi 830046, China, Sheng Li Road No.14, Tian
Shan Region, Urumqi, Xinjiang, China (
).
Photogrammetric Engineering & Remote Sensing
Vol. 84, No. 1, January 2018, pp. 43–52.
0099-1112/17/43–52
© 2017 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.84.1.43
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
January 2018
43
