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Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (7): 779-796    DOI: 10.1007/s40333-023-0020-9     CSTR: 32276.14.s40333-023-0020-9
Research article     
Soil quality assessment for desertification based on multi-indicators with the best-worst method in a semi-arid ecosystem
Orhan DENGİZ1,*(), İnci DEMİRAĞ TURAN2
1Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Ondokuz Mayıs University, Samsun-55139, Turkey
2Department of Geography, Faculty of Economic, Administrative and Social Sciences, Samsun University, Samsun-55030, Turkey
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Abstract  

Since there are some signs of land degradation and desertification showing how soil sustainability is threatened, it is crucial to create a soil quality index (SQI) model in the semi-arid Çorum Basin, situated between the Black Sea and Anatolia Region, Central Turkey. The primary aims of the study are: (1) to determine SQI values of the micro-basin in terms of land degradation and desertification. Moreover, the best-worst method (BWM) was used to determine the weighting score for each parameter; (2) to produce the soils' spatial distribution by utilizing different geostatistical models and GIS (geographic information system) techniques; and (3) to validate the obtained SQI values with biomass reflectance values. Therefore, the relationship of RE-OSAVI (red-edge optimized soil-adjusted vegetation index) and NDVI (normalized difference vegetation index) generated from Sentinel-2A satellite images at different time series with soil quality was examined. Results showed that SQI values were high in the areas that had almost a flat and slight slope. Moreover, the areas with high clay content and thick soil depth did not have salinity problems, and were generally distributed in the middle parts of the basin. However, the areas with a high slope, poor vegetation, high sand content, and low water holding capacity had low SQI values. Furthermore, a statistically high positive correlation of RE-OSAVI and NDVI indices with soil quality was found, and NDVI had the highest correlative value for June (R2=0.802) compared with RE-OSAVI.

Key words： soil quality      land degradation      desertification      best-worst method      remote sensing     
Received: 17 January 2023      Published: 31 July 2023
Corresponding Authors: *Orhan DENGİZ (E-mail: odengiz@omu.edu.tr)
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Orhan DENGİZ, İnci DEMİRAĞ TURAN. Soil quality assessment for desertification based on multi-indicators with the best-worst method in a semi-arid ecosystem. Journal of Arid Land, 2023, 15(7): 779-796.

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http://jal.xjegi.com/10.1007/s40333-023-0020-9     OR     http://jal.xjegi.com/Y2023/V15/I7/779

Fig. 1 Location (a and b) and elevation (c) of the study area
Fig. 2 Slope (a) and aspect (b) of the study area
Land use/land cover Area
(km²)		 Percentage (%) Land use/land cover Area (km²) Percentage (%)
Continuous urban fabric 6.0 0.9 Complex cultivation patterns 29.0 4.4
Discontinuous urban fabric 10.0 1.5 Lands principally occupied by agriculture with significant areas of natural vegetation 80.0 12.1
Industrial or commercial units 17.2 2.6 Broad-leaved forests 38.0 5.8
Mineral extraction sites 2.0 0.3 Coniferous forests 17.0 2.6
Dump sites 1.0 0.2 Mixed forests 12.0 1.8
Non-irrigated arable lands 187.0 28.3 Natural grasslands 39.0 5.9
Permanently irrigated arable lands 97.0 14.7 Transitional woodlands/shrublands 91.0 13.8
Vineyards 0.8 0.1 Sparsely vegetated areas 22.0 3.3
Fruits trees and berry plantations 3.0 0.5 Water bodies 1.0 0.2
Pastures 7.0 1.1 Total 660.0 100.0
Table 1 Distribution of land use/land cover in the ?orum Basin, Central Trukey
Fig. 3 Land use/land cover (a) and geology (b) of the study area
Fig. 4 Modelling architecture designed to determine SQI (soil quality index). BWM, best-worst method; GIS, geographic information system; NDVI, normalized difference vegetation index; RE-OSAVI, red-edge optimized soil-adjusted vegetation index.
Fig. 5 Soil samples of the study area. DEM, digital elevation model.
Parameter FT L U SSF equation
pH LB 6.90 8.56 $f\left( x \right)=\left\{ 1-0.9\times \frac{\begin{matrix} 0.1 \\ x-L \\ \end{matrix}}{\begin{matrix} U-L \\ 1 \\ \end{matrix}} \right.+0.1\text{, }\begin{matrix} x\le L \\ L\le x\le U \\ x\ge U \\ \end{matrix}$
CaCO3 LB 0.54 43.97
Silt LB 4.45 70.67
Sand LB 14.67 80.41
Slope LB 2.00 30.00
EC LB 0.04 1.55
SOM MB 0.45 6.31 $f~\left( x \right)=\left\{ 0.9\times \frac{\begin{matrix} 0.1 \\ x-L \\\end{matrix}}{\begin{matrix} U-L \\ 1 \\ \end{matrix}} \right.+0.1\text{, }\begin{matrix} x\le L \\ L\le x\le U \\ x\ge U \\ \end{matrix}$
Clay MB 5.63 63.98
Depth MB 20.00 120.00
Table 2 Standard scoring functions (SSF) for soil parameters
Parameter Max Min Mean SD CV Variance
(Dime)		 Skewness
(Dime)		 Kurtosis
(Dime)
SOM (%) 6.31 0.45 2.78 1.23 5.86 1.52 0.34 -0.32
pH 8.56 6.90 7.94 0.37 1.66 0.14 -0.77 -0.00
CaCO3 (%) 43.97 0.54 10.04 8.27 43.43 68.39 1.53 3.23
EC (dS/m) 1.55 0.04 0.24 0.29 1.51 0.08 3.20 10.83
Depth (cm) 120.00 20.00 78.59 35.70 100.00 1280.09 -0.18 -1.38
Clay (%) 63.98 5.63 30.32 13.52 58.35 182.89 0.18 -0.85
Silt (%) 70.67 4.45 25.08 9.97 66.22 99.54 1.77 4.94
Sand (%) 80.41 14.67 44.59 14.42 65.74 208.10 0.18 -0.59
Slope (°) 45.00 2.00 15.10 9.14 28.00 83.71 0.42 -0.99
SQI 0.86 0.31 0.59 0.13 0.55 0.01 -0.15 -1.10
Table 3 Statistics of soil physical-chemical parameters and SQI
Interpolation model Semi-variogram model Soil quality criteria
SOM
(%)		 pH CaCO3
(%)		 EC
(dS/m)		 Depth
(cm)		 Clay
(%)		 Silt
(%)		 Sand
(%)		 Slope
(°)
Inverse distance weighting (IDW) IDW-1 1.214 0.383 7.322 0.264 0.549 0.554 0.990 0.921 0.728
IDW-2 1.218 0.389 7.323 0.269 0.540 0.669 0.904 0.903 0.788
IDW-3 1.243 0.398 7.408 0.279 0.559 0.710 0.990 0.960 0.789
Radial basis functions TPS 1.348 0.475 9.101 0.338 0.610 0.711 0.998 0.998 0.880
CRS 1.219 0.392 7.365 0.270 0.612 0.660 0.992 0.872 0.750
SWT 1.216 0.389 7.334 0.267 0.611 0.566 0.990 0.879 0.765
Kriging Ordinary Gaussian 1.201 0.381 7.288 0.254 0.610 0.674 0.946 0.883 0.838
Exponential 1.196 0.383 7.290 0.258 0.610 0.786 0.945 0.882 0.832
Spherical 1.200 0.382 7.269 0.256 0.610 0.753 0.946 0.883 0.831
Simple Gaussian 1.188 0.371 7.251 0.257 0.612 0.649 0.947 0.885 0.830
Exponential 1.186 0.374 7.372 0.267 0.613 0.662 0.947 0.884 0.831
Spherical 1.188 0.374 7.256 0.257 0.612 0.661 0.948 0.885 0.831
Universal Gaussian 1.201 0.381 7.288 0.254 0.611 0.671 0.948 0.886 0.832
Exponential 1.196 0.383 7.290 0.258 0.612 0.671 0.946 0.886 0.832
Spherical 1.200 0.382 7.269 0.256 0.613 0.672 0.947 0.887 0.833
Table 4 Interpolation models and RMSE values of soil quality criteria
Best to others Depth Slope SOM Clay pH EC Silt CaCO3 Sand
Depth 1 2 3 4 5 6 7 8 9
Table 5 Pairwise comparison vector for the best criterion
Others to the worst Depth Slope SOM Clay pH EC Silt CaCO3 Sand
Sand 7 4 3 5 8 6 8 2 1
Table 6 Pairwise comparison vector for the worst criterion
Fig. 6 Distribution maps of the soil quality criteria. (a), soil organic matter (SOM); (b), pH; (c), CaCO3; (d), electrical conductivity (EC); (e), soil depth; (f), slope; (g), silt; (h), clay; (i), sand.
Fig. 7 Spatial distribution map of soil quality index in the study area
Fig. 8 R2 values between soil quality index (SQI) values and NDVI (normalized difference vegetation index) values with different time series. (a), May; (b), June; (c), July; (d), August.
Fig. 9 R2 values between soil quality index (SQI) values and RE-OSAVI (red-edge optimized soil-adjusted vegetation index) values with different time series. (a), May; (b), June; (c), July; (d), August.
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