GIGeoscientific Instrumentation, Methods and Data SystemsGIGeosci. Instrum. Method. Data Syst.2193-0864Copernicus PublicationsGöttingen, Germany10.5194/gi-6-149-2017Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniquesElhagMohamedmelhag@kau.edu.sahttps://orcid.org/0000-0001-9048-0084BahrawiJarbou A.https://orcid.org/0000-0001-6824-670XDepartment of Hydrology and Water Resources Management, Faculty of
Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University, Jeddah 21589, Saudi ArabiaMohamed Elhag (melhag@kau.edu.sa)15March20176114915819November20168December201614February20171March2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://gi.copernicus.org/articles/6/149/2017/gi-6-149-2017.htmlThe full text article is available as a PDF file from https://gi.copernicus.org/articles/6/149/2017/gi-6-149-2017.pdf
Vegetation indices are mostly described as crop water derivatives.
The normalized difference vegetation index (NDVI) is one of the oldest remote
sensing applications that is widely used to evaluate crop vigor directly and
crop water relationships indirectly. Recently, several NDVI derivatives were
exclusively used to assess crop water relationships. Four hydrological
drought indices are examined in the current research study. The water supply
vegetation index (WSVI), the soil-adjusted vegetation index (SAVI), the moisture
stress index (MSI) and the normalized difference infrared index (NDII) are
implemented in the current study as an indirect tool to map the effect of
different soil salinity levels on crop water stress in arid environments. In
arid environments, such as Saudi Arabia, water resources are under pressure,
especially groundwater levels. Groundwater wells are rapidly depleted due to
the heavy abstraction of the reserved water. Heavy abstractions of
groundwater, which exceed crop water requirements in most of the cases, are
powered by high evaporation rates in the designated study area because of
the long days of extremely hot summer. Landsat 8 OLI data were extensively
used in the current research to obtain several vegetation indices in
response to soil salinity in Wadi ad-Dawasir. Principal component analyses
(PCA)
and artificial neural network (ANN) analyses are complementary tools used to understand
the regression pattern of the hydrological drought indices in the designated
study area.
Introduction
Remote sensing data are considered to be a convenient source to perform
several vegetation indices in either simple or complicated band
ratio
combinations. Satellite images offer a large amount of data that could be
analyzed, processed and stored to better understand several vegetation
indices based on the type of the satellite sensor used (Govaerts et al.,
1999; Pinty et al., 2009). Hypothetical backgrounds have been implemented to
improve and enhance the optimization of particular satellite sensors to
support certain vegetation indices (Verstraete et al., 1996; Gobron et al.,
2000; Psilovikos and Elhag, 2013).
Spectral vegetation indices are mathematical combinations of different
spectral bands mostly in the visible and near-infrared regions of the
electromagnetic spectrum. Vegetation activities can be measured
comprehensively through semi-analytical methods of spectral band ratios that
have been extensively used to detect not only seasonal variability of the
vegetation cover but also local scale spatial variability (Broge and
Mortensen, 2002; Xiao et al., 2002).
The generic principle of utilizing vegetation indices is to improve the
interpretation of the spectral data reflected from a vegetation cover.
Spectral reflectance variabilities tend to differentiate between different
vegetation characteristics, based on crop water
relationships and other
surrounding features of soil components, and atmosphere, based on the
maximization of vegetation characteristics over the surrounding environments
(Moulin and Guerif, 1999; Boegh et al., 2002). Color, roughness and water
content are the main soil components that affect soil spectral reflectance
(Curran, 1983a, b; Bouman and Tuong, 2001).
Soil parameter variation tends to draw a line on a plenary scattergram.
Nevertheless, this line, used as a reference point and known as a “soil line”
in vegetation studies, involved both red and infrared spectral bands (Colombo
et al., 2003; Elhag, 2014a, b). The utilization of vegetation indices has always been
challenged by a major difficulty, which is the minimization of soil
component interferences and sensitivity maximization of atmospheric
variations (Qi et al., 1994; Leprieur et al., 2000). The atmospherically
resistant vegetation index (ARVI), developed by Kaufman and Tanré (1992),
and the global environmental monitoring index (GEMI), developed by Pinty and
Verstraete (1992), are the less sensitive vegetation indices to
atmospheric variation. Additionally, Qi et al. (1994) reported that the
GEMI is soil noise sensitive. The higher noise sensitivity of GEMI has
completely disabled the index and classified it as inadequate for arid regions.
Implementations of vegetation indices varied, from a local leaf scale to a
continental vegetation scale. Moreover, certain indices tend to be site
and/or species specific (Clevers, 1989; Elhag, 2014a), and they cannot be
applied to different species or different leaf structures and
canopy geometry (Xiao et al., 2002).
The scholarly work of Kerr and Ostrovsky
(2003), Pettorelli et al. (2005), Huete et al. (2008) and Elhag (2014b)
reported that several vegetation indices were used to estimate different
vegetation parameters extensively, including the leaf area index (LAI), the
fractional vegetation cover (FC), the crop water stress index (CWSI), the drought
severity index (DSI) and the water supply vegetation index (WSVI).
Soil salinization is a dynamic process that basically arises when an excess
of irrigational water is frequently used in the drainage capacity of the
fields (Wardlow and Egbert, 2010). Implementations of remote sensing
techniques in soil salinity mapping achieved comprehensive results on the
regional scale (Montandon and Small, 2008). The brightness index (BI), the
normalized difference salinity index (NDSI) and the salinity index (SI) are
widely distinguished in soil salinity mapping in an arid environment
(Douaoui et al., 2006; Jiapaer et al., 2011). The current research aims to
evaluate the suitability of different vegetation indices for a different
level of remotely sensed soil salinity contrasting to crop water
relationship in Wadi ad-Dawasir.
Materials and methodsStudy area
The study area,the Wadi ad-Dawasir town, is located in the plateau of Najd at
44∘43′ lat and 20∘29′ long, about 300 km south of the capital city, Riyadh.
The study area illustrated in Fig. 1 is comprised of gravelly tableland
disconnected by insignificant sandy oases and isolated mountain bundles.
Across the Arabian Peninsula, as a whole, the tableland slopes toward the
east from an elevation of 1360 m in the west to 750 m at its
easternmost limit. Wadi ad-Dawasir and Wadi al-Rummah, which are the most
important patterns
of the ancient riverbeds, remain in the study area. The Wadi ad-Dawasir
and Najran regions are the major irrigation water abstractors from
the Al-Wajid aquifer. Agriculture in the Wadi ad-Dawasir area consists of
technically highly developed farm enterprises that operate with modern pivot
irrigation systems. The size of center pivot ranges from 30 to 60 ha, with
farms managing hundreds of them with the corresponding number of wells. The
main crop grown in winter is wheat and occasionally potatoes, tomatoes or
melons. All-year fodder consists of alfalfa, which is cut up to 10 times a
year for food. Typical summer crops for fodder are sorghum and Rhodes grass,
which is perennial but dormant in winter. The shallow alluvial aquifers
could not sustain the high groundwater abstraction rates for a long time and
groundwater level declined dramatically in most areas. Meteorological
features of the area are speckled. Five elements of meteorology are
constantly recorded through a fixed weather station located within the study
area. Temperature varies from a minimum of 6 ∘C to a maximum of 43 ∘C.
Relative humidity is mostly stable at 24 %. Solar radiation of average
sunrise duration is generally 11 h day-1. Average wind speed is closer to 13 km h-1
and may reach up to 46 km h-1 in thunderstorm incidents. Finally, mean
annual rainfall is about 37.6 mm (Al-Zahrani and Baig, 2011).
Location of the study area (Elhag, 2016).
Methodological framework
The current research work is based on assessing a regression correlation
between different vegetation indices and their spatial corresponding soil
salinity values conducted from satellite images. The principal component
analysis (PCA) was used to envisage the impacts of soil salinity on
the current vegetation.
Estimation of vegetation indicesWater supply vegetation index (WSVI)
The water supply vegetation index is calculated by
WSVI=NDVI/Ts,
where Ts is the estimated brightness temperature channel or related remote sensing
imagery, and NDVI is the normalized difference vegetation index. The smaller this index is, the more severe the drought
is.
Soil-adjusted vegetation index (SAVI)
The soil-adjusted vegetation index is calculated by
SAVI=NIR-RNIR+R)⋅(1+L,
where NIR is the near-infrared band, R is the red band and
L is the is the soil brightness correction factor, commonly L=0.5
(Huete, 1988).
Moisture stress index (MSI)
The moisture stress index is calculated by
MSI=SWIR1NIR,
where SWIR1 is the short-wave infrared band 1.
Normalized difference infrared index (NDII)
The normalized difference infrared index is calculated by
NDII=NIR-SWIR1NIR+SWIR1.
Estimation of soil salinity index
Soil salinity indices are principally adjusted to detect salt mineral in
soils based on the different responses of salty soils to various spectral
bands. The following equation to map soil salinity was used following Elhag
(2016).
SI=(G×R)/B,
where B is the blue band,
G is the green band and
R is the red band.
Regression analyses
The purpose of the regression analyses is to envisage the regression
potentials between the soil salinity index from one side and the rest of the
hydrological drought indices from the other side. The principal component
analyses and artificial neural network (ANN) analyses were the implemented
approaches. The PCA is used to transform a set of likely correlated with unlikely
correlated variables. The principal components number is less than or equal to the
variables' original number. Following Lorenz (1956), the PCA fundamental
equations are described as follows.
First, vector W(1) has to be calculated
as follows:
w1=argmaxw=1∑it1i2=argmaxw=1∑ixi⋅w2.
The matrix form of the above equation gives the following:
w1=argmaxw=1Xw2=argmaxw=1wTXTXw.W(1) has to be calculated as follows:
w1=argmaxwTXTXwwTw.
The resulting w(1) suggests that the first component of a data vector,
x(i), can then be expressed as a score of t1(i)=x(i)⋅w(1) in the transformed co-ordinates or as the corresponding
vector in the original variables, {x(i)⋅w(1)}w(1).
The neural network regression model is written as
Y=α+∑hwhϕhαh+∑i=1pwihXi,
where
Y=E(Y|X).
This neural network model has one hidden
layer, but it is possible to have additional hidden layers.
The ϕz function used is hyperbolic tangent activation
function. It is used for logistic activation for the hidden layers.
ϕz=tanhz=1-e-2z1+e-2z.
Significantly, the final output should be stochastically linear, with no prediction limitations being between 0 and 1.
A simple diagram of a skip-layer neural
network is illustrated in Fig. 2. The equation for the skip-layer neural
network for regression is shown below.
Y=α+∑i=1pβiXi+∑hwhϕhαh+∑i=1pwihXi.
It should be clear that these models are highly parameterized and, thus, will
tend to overfit the training data. Cross-validation is, therefore, critical
to making sure that the predictive performance of the neural network model is
adequate.
The determination of the adequate performance of the ANN model is a must. Five
different criteria are used: the Pearson correlation coefficient (R), the
root mean square error (RMSE), the mean absolute deviation (MAD), the
negative log likelihood and the error sum of squares (SSE).
Basically, RMSE is the examined parameter for comparability reasons. RMSE
can be computed as follows:
RMSE=1T0∑t=1T0y1-ý12,
where t is the time index and y^t and yt are the simulated and
measured values. Principally, the higher value of R and smaller values of
RMSE ensure the better performance of the model.
Results and discussion
The realization of the hydrological drought indices was exercised after a
comprehensive remote sensing data correction. Basically, atmospheric
correction and spatial enhancement were practiced utilizing Landsat 8 OLI data
acquired over the designated study area. The four hydrological drought
indices were shown in Figs. 3 to 6. Stochastic algorithms of WSVI and SAVI
mapping (Figs. 3 and 4) showed spatial coherence with higher drought
indices' values within the agricultural area rather than the surrounding area
(Ceccato et al., 2001; Daughtry et al., 2004).
Artificial neural network scheme with one hidden layer and three nodes.
On the contrary, MSI functioned as a deterministic drought index, it was
nearly unaffected by changing water content. Conducted results showed two
classes of stresses: stressed and no stress. The no stress class was located
within the agricultural area, and the stressed area was represented along the
agricultural peripheral areas (Fig. 5), where higher values indicate greater
water stress and less water content. This could be explained rationally by
the presence of irrigational sprinkles (Hunt Jr. and Rock, 1989; Ceccato et al.,
2001). NDII is also a stochastic algorithm and was used in the current
research due to the higher sensitivity of the infrared band to detect changes in
water content of plant canopies (Hardisky et al., 1983). The spatial
distribution of NDII (Fig. 6) was mapped accordingly with WSVI and SAVI
indices, in which higher NDII values meant higher water content (Jackson et
al., 2004). There are several algorithms to map soil salinity based on
utilization of different remote sensing data and different ecological
systems. An adequate NDSI algorithm was carried out according to Elhag
(2016) findings in arid ecosystems. In Fig. 7, NDSI showed spatial variation,
especially within the new agricultural expansion at the southwestern part
of the study area. The sprinkle movement drove the salt accumulation to be
located at the peripherally of the agricultural areas (Lunetta et al., 2006;
Konukcu et al., 2006).
Water supply vegetation index (WSVI) thematic map over the study
area.
Further statistical analyses were carried out to construe the
correspondences between salted soils and different horological drought
indices. The regression analysis demonstrated in Fig. 8 showed that salinity
increases with lower WSVI and SAVI (Fig. 8a, b), which is explained by
the salt accumulation in soils in parts per million (ppm). Under salinity
stress conditions, there is not enough available water in soils for proper
vegetation growth (Lunetta et al., 2006; Yang et al., 2011).
Generally, MSI values (Fig. 8c) are high in the study area because of the
excess irrigation regime adopted to overcome the high evaporation rates
(Elhag and Bahrawi, 2014; Elhag, 2016). Excess irrigation regimes in poor-drain
soils lead to waterlogging problems and salts accumulation (Elhag,
2016).
Soil-adjusted vegetation index (SAVI) thematic map over the study
area.
Moisture stress index (MSI) thematic map over the study area.
Normalized difference infrared index (NDII) thematic map over the
study area.
Normalized difference salinity index (NDSI) thematic map over the
study area.
Due to NDII's higher sensitivity to water, NDII values increase with higher
NDSI values. Salts accumulation caused by excessive irrigation is the
driving force behind the proportional increment of NDII values in
conjunction with NDSI values as demonstrated in Fig. 8d (Jackson et al.,
2004; Shi et al., 2015).
Note that * is significant, ** is highly significant and NS is non-significant.
Figure 9 demonstrated the principal component analysis along with the Factor
Analysis. Moreover, eigenvalue decomposition was also demonstrated. WSVI and
SAVI were grouped together. Additionally, NDII and MSI were
individually plotted against the former indices.
RSquare = root square error and RMSE = root mean square error.
Similar results conducted from the scatterplot matrix and the
accompanying
correlation matrix are shown in Fig. 10 and Table 1. A high correlation is
distinguished between WSVI and SAVI, while a negative correlation is noted
between WSVI and SAVI from one side and MSI and NDII from the other side.
In Table 2, NDSI regression analysis shows that NDII is the proper fit based
on different regression parameters (Rodgers and Nicewander, 1988). The
spearman's correlation demonstrated in Table 3 supports the PCA results.
Hydrological drought indices were classified into two categories: MSI and
NDII in one category and WSVI and SAVI in the other one. The elements of
each category are positively correlated. MSI and NDII were significantly
correlated, and WSVI and SAVI were highly correlated. Moreover, any other
combinations of the four hydrological drought indices were not correlated.
The ANN analysis was carried out under one hidden layer, three nodes, and
hyperbolic tangent activation function conditions. These conditions were
carefully exercised to prevent the algorithm from overfitting; the ANN analysis is
demonstrated in Table 4. NDII expressed the highest RMSE, which indicates
that NDSI and NDII are statistically the best fit (Jiang, 2013). SAVI is
at the second best fit, followed by WSVI. MSI failed to fit NDSI values
comprehensively, like the former hydrological drought indices (Jones and
Marshall, 1992; Jiapaer et al., 2011).
Conclusions
The findings of the current research emphasize the importance of the
hydrological drought indices to envisage the adverse effects of salts
accumulation in poorly drained soils similar to the study area under
investigation. The soils of Wadi ad-Dawasir are poorly drained and still
under heavy pressure of heavy irrigation schemes to overcome the high
evaporation rates. Therefore, the implemented irrigation schemes should be
adjusted for better natural resources management. Remote Sensing techniques
were satisfactorily implemented and interpreted in terms of soil salinity
mapping in consort with hydrological drought indices. The normalized difference
infrared index was statistically proven to be the profound normalized difference
salinity index,
followed by soil-adjusted vegetation index and
water supply vegetation index, respectively. The principal component analyses
and artificial neural network analyses are complementary tools used to understand
the regression patterns of the hydrological drought indices in the designated
study area. Further work needs to be considered towards the restrictiveness
of the drastic effect of salts accumulation within the study area.
The data used in this paper are free. The open access online data are available at the earth explorer website.
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the Deanship of Scientific Research (DSR), King
Abdulaziz University, Jeddah, under grant No. 155-36-1437-D. The authors,
therefore, gratefully acknowledge the DSR's technical and financial support.
Edited by: L. Eppelbaum
Reviewed by: S. Boteva and N. Yilmaz
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