Document Type : Research Paper

Authors

1 Department of Soil Science and Engineering, College of Agriculture, Bu-Ali Sina University, Hamadan, Iran

2 Soil and Water Department, Agriculture Faculty, Ilam University, Ilam, Iran

3 Department of Environment, Research Group of Environmental Assessment and Risk, Research Center for Environment and Sustainable Development (RCESD), Tehran, Iran

10.22059/jrwm.2025.395192.1832

Abstract

Accurate spatial data on soil property distribution is crucial for monitoring of land resources, informed management practices, and robust environmental modeling, especially in arid and semi-arid regions. This study aimed to develop a spatial prediction model for soil salinity in the Meymeh Plain, Dehloran Province. The Random Forest (RF) algorithm was employed to investigate spatial variations in soil salinity within the surface (0–30 cm) and subsurface (30–60 cm) soil layers. Soil samples were collected from 100 sites, analyzed for electrical conductivity (EC), and the spatial variability of soil salinity was modeled using random forest (RF) analysis. Seven environmental variables of Greenery, Diffuse Radiation, Valley Bottom Flatness Index, Normalized Difference Vegetation Index, Salinity Index, Wind Direction Index, and Brightness were selected based on the Variance Inflation Factor, including parameters from a digital elevation model and Sentinel-2 satellite reflectance data. The model used 80% of the data for calibration and 20% for validation, with performance assessed through root mean square error (RMSE), coefficient of determination (R²), and concordance correlation coefficient (CCC). The RF model showed high prediction accuracy for surface EC and relatively acceptable results for subsurface layers. The R² for the surface layer was 0.92, and for the subsurface layer was 0.37; the RMSE for the surface and subsurface layers was 0.22; and the CCC for the surface layer was 0.82 and for the subsurface layer was 0.97. Overall, topographic derivatives demonstrated a greater influence on predicting soil salinity in both surface and subsurface layers compared to remote sensing data. The multi-resolution valley bottom flatness index with high spatial resolution was identified as the most important predictor of soil salinity, highlighting the impact of topographic factors in the study area.

Keywords

Abd El Kader Douaoui, H. N., & Walter, C. (2006). Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data. Geoderma134(1-2), 217-230.
Allbed, A., & Kumar, L. (2013). Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Advances in remote sensing2(4), 373-385.
Amini, D., Tavakoli, M., & Rostaminya, M. (2018). Mapping spatial variability of soil salinity using remote sensing data and geostatistical analysis: A case of Shadegan, Khuzestan. Environmental Erosion Research Journal, 7(4), 24-43.
Alexakis, D. D., Daliakopoulos, I. N., Panagea, I. S., & Tsanis, I. K. (2018). Assessing soil salinity using WorldView-2 multispectral images in Timpaki, Crete, Greece. Geocarto International, 33(4), 321-338.
Asadi, Y., Ezimand, K., Keshtkar, H., & Alavipanah, S.K. (2019). A Survey of Landscape Metrics and Land-use/land-cover Structures on Urban Heat Islands Surface: A Case Study on Urmia City, Iran. Desert, 24(2), 293-306.
Asfaw, E., Suryabhagavan, K. V., & Argaw, M. (2018). Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia. Journal of the Saudi Society of Agricultural Sciences, 17(3), 250-258.
Batista, F. (2020). Geostatistical analysis of soil properties of the karstic sub-horizontal plain of the Yucatan Peninsula. Tropical and Subtropical Agroecosystems, 24, 09.
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
Corwin, D. L., & Lesch, S. M. (2005). Apparent soil electrical conductivity measurements in agriculture. Computers and Electronics in Agriculture, 46(1–3), 11–43.
Corwin, D. L. (2021). Climate change impacts on soil salinity in agricultural areas. European Journal of Soil Science, 72(2), 842-862.
D’Odorico, P., Bhattachan, A., Davis, K. F., Ravi, S., & Runyan, C. W. (2013). Global desertification: Drivers and feedbacks. Advances in water resources, 51, 326-344.Elhag, M., & Bahrawi, J. A. (2017). Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques. Geoscientific Instrumentation, Methods and Data Systems, 6(1), 149-158.
El Sebai, T., Lagacherie, B., Soulas, G., & Martin-Laurent, F. (2007). Spatial variability of isoproturon mineralizing activity within an agricultural field: geostatistical analysis of simple physicochemical and microbiological soil parameters. Environmental Pollution, 145(3), 680-690.
Fathizad, H., Ardakani, M. A. H., Sodaiezadeh, H., Kerry, R., & Taghizadeh-Mehrjardi, R. (2020). Investigation of the spatial and temporal variation of soil salinity using random forests in the central desert of Iran. Geoderma365, 114233.
Gallant, J. C., & Dowling, T. I. (2003). A multiresolution index of valley bottom flatness for mapping depositional areas. Water resources research39(12).
Grunwald, S. (2009). Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma, 152(3-4), 195-207.
Guo, Y., Huang, J., Shi, Z., & Li, H. (2015). Mapping spatial variability of soil salinity in a coastal paddy field based on electromagnetic sensors. PloS one10(5), e0127996.
Hartemink, A. E., & McBratney, A. (2008). A soil science renaissance. Geoderma, 148(2), 123-129.
Hassani, A., Azapagic, A., & Shokri, N. (2020). Predicting long-term dynamics of soil salinity and sodicity on a global scale. Proceedings of the National Academy of Sciences, 117(52), 33017-33027.
Hoa, P. V., Giang, N. V., Binh, N. A., Hai, L. V. H., Pham, T. D., Hasanlou, M., & Tien Bui, D. (2019). Soil salinity mapping using SAR sentinel-1 data and advanced machine learning algorithms: A case study at Ben Tre Province of the Mekong River Delta (Vietnam). Remote Sensing, 11(2), 128.
Hirich, E. H., Bouizgarne, B., Zouahri, A., Ibn Halima, O., & Azim, K. (2022). How Does Compost Amendment Affect Stevia Yield and Soil Fertility?. Environmental Sciences Proceedings, 16(1), 46.
Khamoshi, S. E., Sarmadian, F., & Keshavarzi, A. (2018). Digital soil mapping using random forests model in Abyek, Qazvin province. Iranian Journal of Soil Research, 32(3), 393-402. (in Persian)
Lagacherie, P., & Mcbratney, A. B. (2006). Spatial soil information systems and spatial soil inference systems: perspectives for digital soil mapping. Developments in soil science, 31, 3-22.
Leitão, P., M. Schwieder, F. Pötzschner, J., R. Pinto, A., M. Teixeira, F. Pedroni, M.Sanchez, C. Rogass, S. van der Linden, & Bustamante, M. (2018). From sample to pixel:multi‐scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes. Ecosphere. 9-8: e02298.
Liu, L., Wu, Y., Yin, M., Ma, X., Yu, X., Guo, X., & Guo, W. (2023). Soil salinity, not plant genotype or geographical distance, shapes soil microbial community of a reed wetland at a fine scale in the Yellow River Delta. Science of The Total Environment, 856, 159136.
Manteghi, S., Moravej, K., Mousavi, S. R., Delavar, M. A., & Mastinu, A. (2024). Digital soil mapping for soil types using machine learning approaches at the landscape scale in the arid regions of Iran. Advances in Space Research74(1), 1-16.
Masoudi, R., Mousavi, S. R., Rahimabadi, P. D., Panahi, M., & Rahmani, A. (2023). Assessing data mining algorithms to predict the quality of groundwater resources for determining irrigation hazard. Environmental monitoring and assessment, 195(2), 319.
McBratney, A. B., Santos, M. M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1-2), 3-52.
Mousavi, S. R., Jahandideh Mahjenabadi, V. A., Khoshru, B., & Rezaei, M. (2024). Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches. Frontiers in Plant Science14, 1309171.
Mousavi, S. R., Sarmadian, F., Omid, M., & Bogaert, P. (2021). Digital modeling of three-dimensional soil salinity variation using machine learning algorithms in arid and semi-arid lands of Qazvin plain. Iranian Journal of Soil and Water Research, 52(7), 1915-1929. (In Persian)
Minasny, B., & McBratney, A. B. (2010). Methodologies for global soil mapping. Digital soil mapping: bridging research, environmental application, and operation, 429-436.
Minasny, B., McBratney, A. B., & Lark, R. M. (2008). Digital soil mapping technologies for countries with sparse data infrastructures. Digital soil mapping with limited data, 15-30.
Nawar, S., & Mouazen, A. M. (2017). Comparison between random forests, artificial neural networks and gradient boosted machines methods of on-line Vis-NIR spectroscopy measurements of soil total nitrogen and total carbon. Sensors, 17(10), 2428.
Periasamy, S., & Shanmugam, R. S. (2017). Multispectral and microwave remote sensing models to survey soil moisture and salinity. Land Degradation & Development. 28(4),1412-1425.
Peng, J., Biswas, A., Jiang, Q., Zhao, R., Hu, J., Hu, B., & Shi, Z. (2019). Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China. Geoderma, 337, 1309-1319.
Perri, S., Suweis, S., Holmes, A., Marpu, P. R., Entekhabi, D., & Molini, A. (2020). River basin salinization as a form of aridity. Proceedings of the National Academy of Sciences, 117(30), 17635-17642.
Pouladi, N., Møller, A. B., Tabatabai, S., & Greve, M. H. (2019). Mapping soil organic matter contents at field level with Cubist, Random Forest and kriging. Geoderma, 342, 85-92.
Rossel, R. V., & McBratney, A. B. (2008). Diffuse reflectance spectroscopy as a tool for digital soil mapping. In Digital soil mapping with limited data (pp. 165-172). Dordrecht: Springer Netherlands.
Robinson, N. P., Allred, B. W., Jones, M. O., Moreno, A., Kimball, J. S., Naugle, D. E., & Richardson, A. D. (2017). A dynamic Landsat derived normalized difference vegetation index (NDVI) product for the conterminous United States. Remote sensing, 9(8), 863.
Rahmani, A., Sarmadian, F., & Arefi, H. (2022). Digital mapping of top-soil thickness and associated uncertainty using machine learning approach in some part of arid and semi-arid lands of Qazvin Plain. Iranian Journal of Soil and Water Research, 53(3), 585-602. (in Persian)
Rath, K. M., & Rousk, J. (2015). Salt effects on the soil microbial decomposer community and their role in organic carbon cycling: a review. Soil Biology and Biochemistry, 81, 108-123.
Rezaie, G., Sarmadian, F., Torkashvand, A. M., Seyedmohammadi, J., & Marashi Aliabadi, M. (2023). Digital Mapping of Surface and Subsurface Soil Organic Carbon and Soil Salinity Variation in a Part of Qazvin Plain (Case Study: Abyek and Nazarabad Regions). Water and Soil, 37(2), 315-331. (in Persion)
Rhoades, J. D. (1982). Cation exchange capacity. Methods of soil analysis: Part 2 chemical and microbiological properties, 9, 149-157.
Suleymanov, A., Abakumov, E., Suleymanov, R., Gabbasova, I., & Komissarov, M. (2021). The soil nutrient digital mapping for precision agriculture cases in the trans-ural steppe zone of Russia using topographic attributes. ISPRS International Journal of Geo-Information, 10(4), 243.
Taghizadeh-Mehrjardi, R., Schmidt, K., Toomanian, N., Heung, B., Behrens, T., Mosavi, A., & Scholten, T. (2021). Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models. Geoderma, 383, 114793.
Tripathi, A., & Tiwari, R. K. (2021). A simplified subsurface soil salinity estimation using synergy of SENTINEL‐1 SAR and SENTINEL‐2 multispectral satellite data, for early stages of wheat crop growth in Rupnagar, Punjab, India. Land Degradation & Development, 32(14), 3905-3919.
Tully, K., Gedan, K., Epanchin-Niell, R., Strong, A., Bernhardt, E. S., BenDor, T., Mitchell, M., Kominoski, J., Jordan, T.E., Neubauer, S.C. & Weston, N. B. (2019). The invisible flood: The chemistry, ecology, and social implications of coastal saltwater intrusion. BioScience, 69(5), 368-378.
Wallach, D., Makowski, D., Jones, J. W., & Brun, F. (2006). Working with dynamic crop models: evaluation, analysis, parameterization, and applications. Elsevier.
Wang, J., Peng, J., Li, H., Yin, C., Liu, W., Wang, T., & Zhang, H. (2021). Soil salinity mapping using machine learning algorithms with the Sentinel-2 MSI in arid areas, China. Remote Sensing, 13(2), 305.
Wilding, L. P. (1985). Spatial variability: its documentation, accommodation and implication to soil surveys. 166-189.
Zhao, C., Zhang, H., Song, C., Zhu, J. K., & Shabala, S. (2020). Mechanisms of plant responses and adaptation to soil salinity. The innovation, 1(1): 100017.
Zhao, S., Ayoubi, S., Mousavi, S. R., Mireei, S. A., Shahpouri, F., Wu, S. X., & Tian, C. Y. (2024). Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China. Journal of Environmental Management364, 121311.
Zhou, T., Geng, Y., Chen, J., Pan, J., Haase, D., & Lausch, A. (2020). High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms. Science of The Total Environment, 729, 138244.