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Journal of Arid Land
Journal of Arid Land  2022, Vol. 14 Issue (2): 186-202    DOI: 10.1007/s40333-022-0052-6     CSTR: 32276.14.s40333-022-0052-6
Research article     
Integrating multiple electromagnetic data to map spatiotemporal variability of soil salinity in Kairouan region, Central Tunisia
Besma ZARAI1,2,*(), Christian WALTER3, Didier MICHOT3, Jean P MONTOROI4, Mohamed HACHICHA1
1University of Carthage, National Research Institute of Rural Engineering, Water and Forestry LR16INRGREF02, Non-Conventional Water Valorization, Ariana 2080, Tunisia
2National Institute of Agronomy Tunis, University of Carthage, Nicolle 1082, Tunisia
3SAS, Mixed Research Unit Soil Agro and Hydrosystem Spatialization, INRAE, National Research Institute for Agriculture, Food and the Environment, Rennes 35000, France
4Bondy Research and Development Institute, Bondy 93140, France
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Abstract  

Soil salinization is a major problem affecting soils and threatening agricultural sustainability in arid and semi-arid regions, which makes it necessary to establish an efficient strategy to manage soil salinity and confront economic challenges that arise from it. Saline soil recovery involving drainage of shallow saline groundwater and the removal of soil salts by natural rainfall or by irrigation are good strategies for the reclamation of salty soil. To develop suitable management strategies for salty soil reclamation, it is essential to improve soil salinity assessment process/mechanism and to adopt new approaches and techniques. This study mapped a recovered area of 7200 m2to assess and verify variations in soil salinity in space and time in Kairouan region in Central Tunisia, taking into account the thickness of soil materials. Two electromagnetic conductivity meters (EM38 and EM31) were used to measure the electrical conductivity of saturated soil-paste extract (ECe) and apparent electrical conductivity (ECa). Multiple linear regression was established between ECe and ECa, and it was revealed that ECa-EM38 is optimal for ECe prediction in the surface soils. Salinity maps demonstrated that the spatial structure of soil salinity in the region of interest was relatively unchanged but varied temporally. Variation in salinity at the soil surface was greater than that at a depth. These findings can not only be used to track soil salinity variations and their significance in the field but also help to identify the spatial and temporal features of soil salinity, thus improving the efficiency of soil management.

Key words： electrical conductivity      soil salinity      saturated paste extract      apparent electrical conductivity      multiple linear regression      Tunisia     
Received: 17 December 2020      Published: 28 February 2022
Corresponding Authors: *Besma ZARAI (E-mail: bessmazarai@yahoo.fr)
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Besma ZARAI
Christian WALTER
Didier MICHOT
Jean P MONTOROI
Mohamed HACHICHA
Cite this article:

Besma ZARAI, Christian WALTER, Didier MICHOT, Jean P MONTOROI, Mohamed HACHICHA. Integrating multiple electromagnetic data to map spatiotemporal variability of soil salinity in Kairouan region, Central Tunisia. Journal of Arid Land, 2022, 14(2): 186-202.

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http://jal.xjegi.com/10.1007/s40333-022-0052-6     OR     http://jal.xjegi.com/Y2022/V14/I2/186

Fig. 1 An aerial view of the study area (a) and location of soil sampling profiles (b)
Fig. 2 Seasonal variations in different salinity-related metrics recorded in spring 2015, autumn 2015, and autumn 2016. (a1-a2), gravimetric water content (GWC) and electrical conductivity of saturated soil-paste extract (ECe) in the soil layer of 0.0-6.0 m; (b1-b6), GWC and ECe in the soil layer of 0.0-1.2 m. P1-P5 represent five profiles, which respectively contain five soil layers: 0.0-0.2, 0.2-0.4, 0.4-0.6 m, 0.6-0.8 and 0.8-1.2 m.
Fig. 3 Plot of apparent EC measured in vertical dipole orientation (ECa-EMV) versus apparent EC measured in horizontal dipole orientation (ECa-EMH) at all sampling sites (29 sites) using EM31 and EM38 datasets
Statistic Spring 2015 Autumn 2015 Autumn 2016
EM38 EM31 EM38 EM31 EM38 EM31
Eca-EMV Eca-EMH Eca-EMV Eca-EMH Eca-EMV Eca-EMH Eca-EMV Eca-EMH Eca-EMV Eca-EMH Eca-EMV Eca-EMH
Mean (dS/m) 6.97 4.31* 4.54 3.02 6.49* 3.88* 4.52 2.96 6.96 3.42* 5.36* 3.54*
Med. (dS/m) 6.92 4.24 4.48 2.95 6.46 3.77 4.47 2.93 6.93 3.36 5.37 3.51
Min. (dS/m) 4.58 2.84 3.79 2.09 3.71 2.63 3.79 2.48 4.42 2.23 4.65 2.91
Max. (dS/m) 9.35 6.59 5.45 4.46 10.10 5.81 5.45 3.91 9.14 6.86 5.97 4.58
SD (dS/m) 1.09 0.72 0.36 0.40 1.01 0.90 0.35 0.27 0.79 0.82 0.23 0.24
Kurtosis -0.86 0.14 -0.30 2.56 0.39 -0.18 -0.29 0.42 0.38 1.79 1.62 0.16
Skewness 0.01 0.54 0.34 1.27 0.34 -0.02 0.38 0.74 0.13 1.22 0.72 0.02
CV (%) 15.67 16.70 7.95 13.28 15.50 23.17 7.83 9.19 11.36 24.10 4.29 6.77
Table 1 Descriptive statistics of ECa-EMH and ECa-EMV in spring 2015, autumn 2015, and autumn 2016
EM device Soil depth (m) a b c R2 0BN
EM31 0.0-0.6 0.051 0.005 -6.37 0.91 6
0.6-1.2 -0.003 0.017 17.92 0.78
1.2-3.0 -0.050 0.100 35.80 0.77
3.0-6.0 0.048 0.003 19.63 0.95
EM38 0.0-0.6 0.006 0.012 3.20 0.86 23
0.6-1.2 0.003 0.038 6.21 0.85
Table 2 Multiple linear regression coefficients of predicted electrical conductivity of saturated soil-paste extract (ECe) at different soil depths
Period Soil
depth (m)		 Predicted ECe Kriging estimation of ECe
Mean
(dS/m)		 SD
(dS/m)		 Max.
(dS/m)		 Min.
(dS/m)		 Median
(dS/m)		 Kurtosis Skew Mean
(dS/m)		 SD
(dS/m)
Spring 2015 0.0-0.6 13.47 1.52 18.02 10.50 13.27 -0.38 0.31 14.02 1.17
0.6-1.2 23.31 2.57 31.87 18.40 23.06 0.10 0.48 24.98 2.08
1.2-3.0 43.26 3.49 59.90 32.36 41.58 2.52 1.14 43.02 1.77
3.0-6.0 42.32 1.70 46.69 38.63 42.87 -0.25 0.31 42.30 0.83
Autumn 2015 0.0-0.6 12.74 1.27 18.29 10.02 12.53 0.10 -0.29 12.80 1.01
0.6-1.2 19.72 1.23 23.39 16.89 20.02 -0.09 -0.09 22.55 2.27
1.2-3.0 42.82 2.54 52.71 36.13 41.45 0.75 0.62 42.52 1.43
3.0-6.0 42.21 1.68 46.78 38.61 43.03 -0.24 0.38 42.10 0.88
Autumn 2016 0.0-0.6 13.74 1.32 17.79 11.25 13.74 0.47 0.95 12.60 0.56
0.6-1.2 23.11 2.60 32.39 19.38 22.87 1.48 1.16 21.50 1.62
1.2-3.0 44.36 3.02 55.39 37.96 44.23 0.93 0.56 44.74 1.15
3.0-6.0 46.44 1.12 49.31 43.03 46.59 0.18 -0.00 46.46 0.50
Table 3 Descriptive statistics of predicted ECe and the estimation of ECe using Ordinary Kriging method at different soil depths in spring 2015, autumn 2015, and autumn 2016
Period Soil depth (m) Variogram of ECe Cross validation of ECe
Nugget (dS/m) Sill (dS/m) Range (m) Nugget/Sill MAE (dS/m) RMSE (dS/m)
Spring 2015 0.0-0.6 1.12 2.85 27.59 0.39 -0.006 1.22
0.6-1.2 3.23 10.67 48.00 0.30 -0.006 2.33
1.2-3.0 14.72 23.55 26.27 0.63 0.010 4.29
3.0-6.0 2.46 4.09 12.14 0.60 0.006 1.57
Autumn 2015 0.0-0.6 1.14 3.00 33.76 0.38 -0.005 1.39
0.6-1.2 5.90 14.20 37.95 0.42 -0.002 2.78
1.2-3.0 7.02 10.48 45.25 0.67 0.002 2.69
3.0-6.0 2.06 3.94 16.00 0.52 -0.007 1.57
Autumn 2016 0.0-0.6 1.60 2.34 48.00 0.68 0.000 1.39
0.6-1.2 5.70 12.53 96.00 0.45 -0.007 3.04
1.2-3.0 5.43 7.75 64.00 0.68 -0.009 2.49
3.0-6.0 0.70 1.22 15.67 0.57 0.001 0.98
Table 4 Variogram characteristics and cross-validation statistics for ECe at different soil depths in spring 2015, autumn 2015, and autumn 2016
Fig. 4 ECe variograms predicted by the multiple linear regression (MLR) models in spring 2015 (a), autumn 2015 (b), and autumn 2016 (c)
Period Soil depth (m) Cross-validation statistic
MAE (dS/m) RMSE (dS/m)
Spring 2015 0.0-0.6 2.10 2.72
0.0-1.2 1.37 1.73
Autumn 2015 0.0-0.6 1.81 2.34
0.0-1.2 1.66 2.28
Autumn 2015 0.0-0.6 2.66 3.67
0.0-1.2 1.65 2.06
Table 5 Cross-validation statistics of ECe inverted at different soil depths in spring 2015, autumn 2015, and autumn 2016
Fig. 5 Spatial and temporal distributions of the one dimensional inversion in ECe for the soil layers of 0.0-0.6 m (a, b, c) and 0.0-1.2 m (d, e, f) in spring 2015, autumn 2015, and autumn 2016
Fig. 6 Spatial and temporal distributions of ECe obtained from measurements of ECa-EM38 in the soil layers of 0.0-0.6 m (a1-a3) and 0.6-1.2 m (b1-b3) and ECa-EM31 in the soil layers of 1.2-3.0 m (c1-c3) and 3.0-6.0 m (d1-d3) m in spring 2015, autumn 2015, and autumn 2016
Fig. 7 Coefficient of variation (CV) of ECa-EMH (a and b) and ECa-EMV (c and d) measurements using EM31 and EM38 devices
Measurement profile Device CV of ECa
Mean (%) Median (%) SD (%) Kurtosis Skewness
Vertical EM38 10.20 9.07 6.20 1.18 1.11
Horizontal 21.60 21.21 8.81 -0.84 0.12
Vertical EM31 11.20 11.11 3.93 -0.36 0.14
Horizontal 12.80 12.37 5.62 -0.03 0.52
Table 6 Descriptive statistics of the CV of ECa in spring 2015, autumn 2015, and autumn 2016
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