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Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas

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  • Azamat Suleymanov

    (Laboratory of Soil Science, Ufa Institute of Biology UFRC RAS, 450054 Ufa, Russia
    Department of Environmental Protection and Prudent Exploitation of Natural Resources, Ufa State Petroleum Technological University, 450064 Ufa, Russia)

  • Ilyusya Gabbasova

    (Laboratory of Soil Science, Ufa Institute of Biology UFRC RAS, 450054 Ufa, Russia
    Laboratory of Climate Change Monitoring and Carbon Ecosystems Balance, Ufa State Petroleum Technological University, 450064 Ufa, Russia)

  • Mikhail Komissarov

    (Laboratory of Soil Science, Ufa Institute of Biology UFRC RAS, 450054 Ufa, Russia
    Laboratory of Climate Change Monitoring and Carbon Ecosystems Balance, Ufa State Petroleum Technological University, 450064 Ufa, Russia)

  • Ruslan Suleymanov

    (Laboratory of Soil Science, Ufa Institute of Biology UFRC RAS, 450054 Ufa, Russia
    Laboratory of Climate Change Monitoring and Carbon Ecosystems Balance, Ufa State Petroleum Technological University, 450064 Ufa, Russia)

  • Timur Garipov

    (Laboratory of Soil Science, Ufa Institute of Biology UFRC RAS, 450054 Ufa, Russia
    Laboratory of Climate Change Monitoring and Carbon Ecosystems Balance, Ufa State Petroleum Technological University, 450064 Ufa, Russia)

  • Iren Tuktarova

    (Department of Environmental Protection and Prudent Exploitation of Natural Resources, Ufa State Petroleum Technological University, 450064 Ufa, Russia)

  • Larisa Belan

    (Department of Environmental Protection and Prudent Exploitation of Natural Resources, Ufa State Petroleum Technological University, 450064 Ufa, Russia
    Department of Geology, Hydrometeorology and Geoecology, Ufa University of Science and Technology, 450076 Ufa, Russia)

Abstract

The problem of salinization/spreading of saline soils is becoming more urgent in many regions of the world, especially in context of climate change. The monitoring of salt-affected soils’ properties is a necessary procedure in land management and irrigation planning and is aimed to obtain high crop harvest and reduce degradation processes. In this work, a machine learning method was applied for modeling of the spatial distribution of topsoil (0–20 cm) properties—in particular: soil organic carbon (SOC), pH, and salt content (dry residue). A random forest (RF) machine learning approach was used in combination with environmental variables to predict soil properties in a semi-arid area (Trans-Ural steppe zone). Soil, salinity, and texture maps; topography attributes; and remote sensing data (RSD) were used as predictors. The coefficient of determination ( R 2 ) and the root mean square error (RMSE) were used to estimate the performance of the RF model. The cross-validation result showed that the RF model achieved an R 2 of 0.59 and an RMSE of 0.68 for SOM; 0.36 and 0.65, respectively, for soil pH; and 0.78 and 1.21, respectively for dry residue prediction. The SOC content ranged from 0.8 to 2.8%, with an average value of 1.9%; soil pH ranged from 5.9 to 8.4, with an average of 7.2; dry residue varied greatly from 0.04 to 16.8%, with an average value of 1.3%. A variable importance analysis indicated that remote sensing variables (salinity indices and NDVI) were dominant in the spatial prediction of soil parameters. The importance of RSD for evaluating saline soils and their properties is explained by their absorption characteristics/reflectivity in the visible and near-infrared spectra. Solonchak soils are distinguished by a salt crust on the land surface and, as a result, reduced SOC contents and vegetation biomass. However, the change in saline and non-saline soils over a short distance with mosaic structure of soil cover requires high-resolution RSD or aerial images obtained from unmanned aerial vehicle/drones for successful digital mapping of soil parameters. The presented results provide an effective method to estimate soil properties in saline landscapes for further land management/reclamation planning of degraded soils in arid and semi-arid regions.

Suggested Citation

  • Azamat Suleymanov & Ilyusya Gabbasova & Mikhail Komissarov & Ruslan Suleymanov & Timur Garipov & Iren Tuktarova & Larisa Belan, 2023. "Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas," Agriculture, MDPI, vol. 13(5), pages 1-11, April.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:5:p:976-:d:1135654
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    References listed on IDEAS

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    1. Khan, Nasir M. & Rastoskuev, Victor V. & Sato, Y. & Shiozawa, S., 2005. "Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators," Agricultural Water Management, Elsevier, vol. 77(1-3), pages 96-109, August.
    2. Aadhityaa Mohanavelu & Sujay Raghavendra Naganna & Nadhir Al-Ansari, 2021. "Irrigation Induced Salinity and Sodicity Hazards on Soil and Groundwater: An Overview of Its Causes, Impacts and Mitigation Strategies," Agriculture, MDPI, vol. 11(10), pages 1-17, October.
    3. Wichelns, Dennis & Qadir, Manzoor, 2015. "Achieving sustainable irrigation requires effective management of salts, soil salinity, and shallow groundwater," Agricultural Water Management, Elsevier, vol. 157(C), pages 31-38.
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    2. Liangwei Cheng & Mingzhi Yan & Wenhui Zhang & Weiyan Guan & Lang Zhong & Jianbo Xu, 2024. "Interpretable Digital Soil Organic Matter Mapping Based on Geographical Gaussian Process-Generalized Additive Model (GGP-GAM)," Agriculture, MDPI, vol. 14(9), pages 1-18, September.
    3. Wenhui Zhang & Liangwei Cheng & Ruitao Xu & Xiaohua He & Weihan Mo & Jianbo Xu, 2023. "Assessing Spatial Variation and Driving Factors of Available Phosphorus in a Hilly Area (Gaozhou, South China) Using Modeling Approaches and Digital Soil Mapping," Agriculture, MDPI, vol. 13(8), pages 1-18, August.
    4. Dorijan Radočaj & Mateo Gašparović & Mladen Jurišić, 2024. "Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review," Agriculture, MDPI, vol. 14(7), pages 1-19, June.

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