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- Taylor & Francis
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- © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
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- 2474-9508
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- 10.1080/24749508.2018.1438744
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Abstract
GeoloGy, ecoloGy, and landscapes, 2018 Vol . 2, no . 1, 22–28 https://doi.org/10.1080/24749508.2018.1438744 INWASCON OPEN ACCESS Remote sensing approach to assess salt-affected soils in the north-east part of Tadla plain, Morocco a a b a b Widad Ennaji , Ahmed Barakat , Ismail Karaoui , Mohamed El Baghdadi and Abdelkrim Arioua Georessources and environment laboratory, Faculty of s ciences and Techniques, University sultan Moulay slimane, Béni-Mellal, Morocco; Management and enhancement of Water Resources laboratory, Faculty of s ciences and Techniques, University sultan Moulay slimane, Béni-Mellal, Morocco ABSTRACT ARTICLE HISTORY Received 30 July 2017 Recently, the remote sensing technologies have been used increasingly in various domains in a ccepted 20 o ctober 2017 order to explain or detect different phenomena in a rapid manner and covering large areas. This study aims to use Landsat 8 Oli imagery product to elaborate a map of soil salinity in the north- KEYWORDS east part of Tadla plain, by implication of spectral reflectance and electrical conductivity (EC) Tadla plain; soil salinity; sI; measured in the laboratory. Based on salinity Index (SI), the Normalized Differential Salinity Index ndsI; landsat 8 oli (NDSI), and Landsat bands, we carried out a statistical study via the JMP13 software to determine the most correlated bands with EC measured. The obtained results were very satisfactory with = 71.3% and root mean square error (RMSE) of 0.084. The elaborated map showed that an R the salinity is high near Oum Er Rbia River and the two cities of Beni-Mellal and Ouled Yaich, which is due to saline waters of Oum Er Rbia River and Béni-Moussa-East (Dir) groundwater used for irrigation. These results signify that the combination of remote sensing and laboratory EC measurements would be a suitable method for predicting soil salinity. 1. Introduction 2002). The new remote sensing technologies become a powerful method to provide global and rich information Soil salinization is the accumulation of salt in soils, on the spatiotemporal evolution of surface soil without which is considered as an ecological problem that is oe ft n any direct contact. This ability has been demonstrated attributed to natural influences (80% in salinized lands), in some studies, such as in a Th iland by Shrestha (2006), rather than anthropogenic activities. The high soil salin - Mehrjardi, Mahmoodi, Taze, and Sahebjalal (2008), and ity or “salt-ae ff cted soils” ae ff cting, in particular, the arid Bouaziz, Matschullat, and Gloaguen (2011). Shrestha and semi-arid regions, contribute to the degradation of (2006) conducted an assessment of soil salinity in north- soil quality (Abrol, Yadav, & Massoud, 1988), and conse- east Thailand using soil properties and remote sensing, and quently constitutes a real threat to global food security. developed diff erent salinity prediction models contain- Furthermore, the Food and Agriculture Organization ing the spectral variables, and including the Normalized (Food & Agriculture Organization of the UN, 1989) Difference Vegetation Index (NDVI), Normalized indicates that in 227 million hectares of irrigated lands Difference Salinity Index (NDSI), eight original bands of in the world, 20% are salt-ae ff cted. This salt-ae ff cted soil Landsat ETM+, and soil properties. The results obtained is increasing day aer d ft ay to more than 30% in countries indicated that near-infrared (band 4) and mid-infrared such as Egypt, Iran, and Argentina (Ghassemi, Jakeman, (band 7) had the highest correlation with the measured & Nix, 1995). This situation was similar in Morocco, electrical conductivity (EC). Otherwise, Mehrjardi et al. where 5% of agricultural soils are ae ff cted by saliniza- (2008), working on an assessment of soil salinity map in tion in different degrees (Antipolis, 2003), reducing thus Yazd-Ardakan Plain, has shown that among the Landsat their productivity. Assessing the spatial distribution of ETM + bands the third one (red band) had the highest salinity became, therefore, so important to appropriately correlation with EC measured. Bouaziz et al. (2011) has manage and to protect the soils for agriculture purpose. In recent decades, with the progress of remote sensing demonstrated by conducting research on soil salinity from a semi-arid climate in North-east Brazil that the technologies, the prediction of soil salinity and mapping its spatial distribution in large-scales are becoming more incorporation of the Salinity Index (SI2) with near- important and easy. This prediction helps to prevent and infrared (NIR) (band 3) into a statistical model allowed minimize the salinity phenomena (Zhang, Wang, & Wang, to gain a great insight into the spatial detection of the CONTACT ahmed Barakat a.barakat@usms.ma © 2018 The a uthor(s). published by Informa UK limited, trading as Taylor & Francis Group. This is an open a ccess article distributed under the terms of the creative c ommons a ttribution license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. GEOLOGY, ECOLOGY, AND LANDSCAPES 23 spread of soil salinity. Tajgardan, Shataee, and Ayoubi Plateau in the north (Figure 1). The geological forma- (2007) evaluated the spatial distribution of soil salinity tions are mainly composed of limestone, marls, and North of Ag ghala, Golestan Province (Iran) by combining sandstone. They are aged from Palaeozoic to Quaternary. remote sensing and field EC measurement, and demon- Concerning the altitude, it varies from 400 m to 700 m strated that the use of ASTER sensor (Advanced Space with high peaks located close to the Atlas Piedmont. The borne Thermal Emission and Reflection Radiometer) annual temperatures range from 38 to 40 °C in summer can be used to elaborate a suitable regression model to and from 3 to 4 °C in winter. The average rain fall is 259 predict soil salinity. The common between all previous mm/y in the plain while 456 mm/y in mountain. It’s studies is that they apply statistical methods as Principal characterized by a Mediterranean climate with a domi- Components Analysis (PCA) techniques and regression nance of semi-arid weather by a dry season from April analysis. This combination has enabled them to predict to October and a rainy season from November to March. and map soil salinity in their regions. e T Th adla plain is one of the main agricultural zones e c Th urrent soil salinity problem is related to time and in Morocco. This is owing to suitable soil conditions, space, for which traditional methods (field investigation favourable climate, and abundant underground and and laboratory analysis) are insufficient to monitor this surface water resources. Hence, agriculture is the first phenomenon. The aim of this research article is to iden - socio-economic activity in the region (Barakat, Ennaji, tify the saline areas in the north-east part of the Tadla El Jazouli, Amediaz, & Touhami, 2017). Cropping is plain using the new imagery techniques of Landsat Oli based on cereals, forages, orchards (olive and citrus fruit 8 with a spatial resolution of 30 m. trees), sugar beet and cotton, and vegetables. However, due to the agricultural intensification and to the water stress that the region is having in the last decades, irri- 2. Study area gation and excessive inputs are required for intensive e Th study area is located in the north-east area of Tadla crop production. Such human activities are responsible plain, which extends over an estimated area of 3,600 km , for increasing the salt in the soil and for degradation between the High Atlas in the south and the Phosphates’ of water resource quality, as reported in the scientific Figure 1. s tudy area and sampling points. 24 W. ENNAJI ET AL. Raw images Field sampling Processed images Laboratory analyzes Indices calculation (SI, Electrical conductivity NDSI…. value Correlation between laboratory EC and different indices Statistical and ground validation Salinity map Figure 2. Flowchart of the methodology used in the study. literature in some other areas of the Tadla plain (Barakat, is measured. EC measurement by the saturated paste El Baghdadi, Rais, Aghezzaf, & Slassi, 2016; Barbouchi et method is the preferred method to estimate soil salinity al., 2015; Hammoumi, Sinan, Lekhlif, & Lakhdar, 2013; (Figure 3). Lahlou, Ajerame, Bogaert, & Bousetta, 2013; Lhissoui, El Harti, & Chokmani, 2014). Such investigation was 3.2. Satellite data acquisition and processing not realized in our study area; hence the need to assess Free satellite images became widely used in several and map soil salinity in this area is becoming a wide domains that require cartographic information. Satellite necessity. imagery data help to investigate the large area with low cost and time consuming. e Th remote sensing data 3. Materials and methods selected in this study are captured by the Landsat sen- The methodology adopted in the present work began sor launched on 11 February 2013, which consists of with a field campaign to collect representative soil two science instruments, the Operational Land Imager samples spread throughout the study area. These sam - (OLI) and the Thermal Infrared Sensor (TIRS). These ples were analysed by the saturated paste method to two sensors provide seasonal coverage of the global determine the electrical conductivity. Simultaneously, landmass at a spatial resolution of 30 m (visible, NIR, Landsat Oli8 images with the same time as our field SWIR), 100 m (thermal), and 15 m (panchromatic). e Th campaign were used to calculate the various stand- wavelength and the use of all bands in Landsat 8 sonar ard indices giving information about soil salinity. are given in Table 1. Afterwards, a statistical correlation between the elec- e s Th atellite image used in this study was acquired trical conductivity data and the computed spectral on 1 March 2015, simultaneously with soil sampling indices was made. Figure 2 summarized the steps dates. This image was corrected atmospherically using that we have followed to map the soil salinity in the the Dark Object Subtraction (DOS) model, which is a study area. radiative transfer model that corrects the raw image and takes into account the state of the atmosphere at the acquisition date (Dial, Bowen, Gerlach, Grodecki, & 3.1. Sampling and analysis Oleszczuk, 2003). All the remote sensing processing was Soil samples were collected during March month 2015, performed using ENVI (Environment for Visualizing from 97 sites scattered in a way to cover the entire north- Images) software. Producing the soil salinity maps was east part of the Tadla plain. The exact coordinate of each carried out using ArcGIS 10.2. composite sample was recorded using a global position- ing system (GPS) with an accuracy of ±5 m (Figure 1). 3.3. Data analysis and model generation and e s Th amples were dried in open air and sieved to 2 mm, selection and the fraction less than 2 mm was used to measure the electrical conductivity (EC) of the soil with a con- e d Th ata processing step consisted to separate the ductivity metre. To measure the ECe that is the electrical Landsat sensor bands individually. Then, some spec- conductivity of a saturated soil-water extract, the water tral soil salinity indices were tested and computed for is removed from a just-saturated soil sample by a cen- assessing and enhancing the variation in surface soil trifuge or vacuum pump, and ECe of the water extract salinity. Out of all indices tested, the SI (Salinity Index) GEOLOGY, ECOLOGY, AND LANDSCAPES 25 Figure 3. procedure for measuring the electric conductivity. Table 1. Wavelengths of all bands in landsat 8 and their uses. SI = (Band1 × Band3) (1) Wavelength Band (μm) Useful for mapping Band 1: coastal .43–.45 c oastal and aerosol studies (Band3 − Band4) aerosol NDSI = (2) Band 2: blue .45–.51 Bathymetric mapping, distinguish- (Band3 + Band4) ing soil from vegetation and decid- uous from coniferous vegetation After computation of SI and NDSI index, and extrac- Band 3: green .53–.59 emphasizes peak vegetation, which is useful for assessing plant vigour tion of Landsat bands, an extraction has been done Band 4: red .64–.67 discriminates vegetation slopes into the field sampling points corresponding values Band 5: near- .85–.88 emphasizes biomass content and in all Landsat bands and two indices created. The infrared (nIR) shorelines Band6: short- 1.57–1.65 discriminates moisture content of values extracted from the different bands are then wave infrared soil and vegetation, penetrates used to determine their correlation with the results (sWIR) 1 thin clouds Band 7: short- 2.11–2.29 Improved moisture content of soil obtained in the electrical conductivity measured in wave infrared and vegetation and thin cloud the laboratory. (sWIR) 2 penetration Band 8: pan- .50–.68 15 m resolution, sharper image In order to determine the band’s ration in our model, chromatic definition a stepwise regression method was performed to gener- Band 9: cirrus 1.36–1.38 Improved detection of cirrus cloud contamination ate several suggestion models with different components Band 10: TIRs 1 10.60–11.19 100 m resolution, thermal mapping characterized. es Th e models are statistically evaluated and estimated soil moisture based on their estimated error and correlation. Band 11: TIRs 2 11.5–12.51 100 m resolution improved thermal mapping and estimated soil e p Th erformance and choice of the developed linear moisture regression models that met the selection criteria were quantified using the test subset to ensure the use of the different data-sets and not only a particular set. The two (Equation 1), which has been proposed by Tripathi, Rai, quantitative criteria, a coefficient of determination (R ) and Dwivedi (1997), and the NDSI index (normalized and a root mean square error (RMSE) were calculated salinity index) (Equation 2) by Aldakheel, Elprince, based on Equations (3) and (4) for evaluating the rela- and Al-Hosaini (2005) are the wildly used to create an tionship between soil EC measured and EC predicted enhanced images for soil salinity due to their very highly from satellite images index. significant correlation with EC. 26 W. ENNAJI ET AL. with the values 8.42, 2.11, and .16 are the integration ⎛ ⎞ coefficients of every band, and .31 is the elaborate model � �� � x − x ̄ y − y ̄ ⎜ ⎟ i=1 i i 2 constant. R = (3) ⎜ ⎟ � � � � ∑ ∑ 2 2 This elaborate equation has been integrated into a n n ⎜ ⎟ x − x ̄ + y − y ̄ i=1 i i=1 i ⎝ ⎠ satellite image processing software, which allows us to develop a new salinity map for the study area. This soil � salinity map showed a variable soil salinity distribution � � varying from .11 to 2.86 ds/cm (Figure 4). x − y i=1 i i (4) RMES = According to the produced soil salinity map (Figure 4), the salinity is high in the north part of the study area, in With x and y are the measured and predicted values, proximity of the Oum Er Rbia River. Such salinity could i i respectively; x ̄ and y ̄ represent, respectively, the means be attributed to the use of the river saline water of Oum Er of the measured and predicted values; n is the number Rbia that is used in irrigation, and subsequently, ae ff cted of samples. agricultural soil by salinization (Faouzi & Larabi, 2001). e va Th lues of R indicate the strength of the statistical Compared to the Piedmont area, this zone presented a linear relationship between the measured and predicted high rate of salinity in the study area. e Th increased salin - values of soil salinity, and the mean root means square ity near the two cities of Ouled Yaich and Beni-Mellal error (RMSE) indicates absolute estimation errors. could be linked into irrigation by groundwater originat- ing from Béni-Moussa-East (Dir) water table, which con- tains salinity between 700 and 1000 mg/l. These values 4. Results and discussion are classified of high salinity concentrations according to e r Th esults obtained using stepwise regression anal- Moroccan standards (Bellouti et al., 2002). ysis allowed us to determine the most correlated and e r Th esulting model from the R and RMSE param- uncorrelated bands with EC measured in the laboratory, eters showed an increase in prediction power (R = .71) then eliminate and select these able bands to be used of predicting soil electrical conductivity and mapping during the development of our salinity Model. From the spatial variability of soil salinity in the study area, these results, only the NDSI index, B4 and B1 bands compared to similar case studies, i.e. that carried out by were selected for our model with their different integra - Fallah Shamsi, Zare, and Abtahi (2013) where R = .39, tion coefficients. e Th results obtained from the stepwise and recently that conducted by Allbed, Kumar, and Sinha regression are summarized in Table 2. (2014) showed R value of .65, which have been devel- According to several research studies (Eldeiry & oped using different spatial resolution satellite images, Garcia, 2008, 2010; Hick & Russell, 1990; Howari, this is partly due to the high spatial resolution of the 2003; McLaughlin, Palmer, Tiller, Beech, & Smart, Landsat 8 Oli images. We precise that the prediction 1994; Metternicht & Zinck, 2003; Tripathi et al., 1997), of soil salinity based on the Landsat 8 Oli images oer ff s the model equation is determined by multiplying the better results than these based on moderate resolution integration coefficients of each band into the band pixel images. It should be taken into account that spatial res- values itself, then adding a model constant. Equation olution is one of the important factors to deduce the soil (5) is our model equation that can describe the most salinity. In order to validate and verify the accuracy of variation of EC: the elaborate model results, a field trip was made on 1 May 2015 to take seven arbitrary samples in the study area, and compare their results with those obtained from Estimated EC = 8.42 ∗ (B1) + 2.11 × (B4) (5) our elaborate model. Table 3 showed the values obtained − .16 ×(NDSI)− .31 Table 2. Results obtained from the stepwise regression. Degree of Sum of squares of freedom of error 2 2 deviations (SSE) (DFE) RMSE R R adjusted Cp p AlCc BIC Current estimates Degrees of freedom of the Lock Seizure Coefficient Estimate numerator Sum of squares Report F Prob. ˃ F 0,5898391 82 .0848125 .7130 .7025 4 4 −173,666 −162,145 c onstant −.312658 1 0 .000 1 ndsI −.1618451 1 .02208 3.070 .08351 B1 8.42057941 1 .119895 16.668 .0001 B4 2.11426761 1 .036538 5.079 .02688 GEOLOGY, ECOLOGY, AND LANDSCAPES 27 Figure 4. s oil salinity map. Table 3. Validation of the elaborated model using other samples. Point P1 P2 P3 P4 P5 P6 P7 X 428828.77 423788.07 409405.48 415854.63 411599.09 407927.76 449366.73 y 213473.53 209193.92 216155.96 205038.25 195250.21 201572.51 225531.39 ec estimated (ds/cm) .134 .198 .712 .152 .154 .163 .203 ec measured (ds/cm) .145 .267 .643 .135 .166 .154 .210 for these seven samples and their equivalent in the elab- are satisfactory for the detection and mapping of soil orated model. salinity, with a lower cost compared to other conven- From the validation points, we can say that our model tional approaches. Thus, this approach can be used by gives results with ± .016 ds/cm, which means that this decision-makers to develop effective programmes to model can be very well used to give an estimation of the reduce or prevent future increases in soil salinity. salinity at a large surface scale in a very fast and reliable manner. Disclosure statement 5. Conclusion No potential conflict of interest was reported by the authors. 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Journal
Geology Ecology and Landscapes – Taylor & Francis
Published: Jan 2, 2018
Keywords: Tadla plain; soil salinity; SI; NDSI; Landsat 8 Oli