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A Systematic Review on Digital Soil Mapping Approaches in Lowland Areas

Author

Listed:
  • Odunayo David Adeniyi

    (Department of Earth and Environmental Sciences, University of Pavia, Via Ferrata 1, 27100 Pavia, Italy)

  • Hauwa Bature

    (Department for Sustainable Development and Ecological Transition, University of Eastern Piedmont, Via Duomo 6, 13100 Vercelli, Italy)

  • Michael Mearker

    (Department of Earth and Environmental Sciences, University of Pavia, Via Ferrata 1, 27100 Pavia, Italy
    Leibniz Centre for Agricultural Landscape Research, Working Group on Soil Erosion and Feedback, Eberswalder Str. 84, 15374 Müncheberg, Germany
    Consiglio Nazionale delle Ricerche, Institute for Georesources and Geodynamics, Via Ferrata 1, 27100 Pavia, Italy)

Abstract

Digital soil mapping (DSM) around the world is mostly conducted in areas with a certain relief characterized by significant heterogeneities in soil-forming factors. However, lowland areas (e.g., plains, low-relief areas), prevalently used for agricultural purposes, might also show a certain variability in soil characteristics. To assess the spatial distribution of soil properties and classes, accurate soil datasets are a prerequisite to facilitate the effective management of agricultural areas. This systematic review explores the DSM approaches in lowland areas by compiling and analysing published articles from 2008 to mid-2023. A total of 67 relevant articles were identified from Web of Science and Scopus. The study reveals a rising trend in publications, particularly in recent years, indicative of the growing recognition of DSM’s pivotal role in comprehending soil properties in lowland ecosystems. Noteworthy knowledge gaps are identified, emphasizing the need for nuanced exploration of specific environmental variables influencing soil heterogeneity. This review underscores the dominance of agricultural cropland as a focus, reflecting the intricate relationship between soil attributes and agricultural productivity in lowlands. Vegetation-related covariates, relief-related factors, and statistical machine learning models, with random forest at the forefront, emerge prominently. The study concludes by outlining future research directions, highlighting the urgency of understanding the intricacies of lowland soil mapping for improved land management, heightened agricultural productivity, and effective environmental conservation strategies.

Suggested Citation

  • Odunayo David Adeniyi & Hauwa Bature & Michael Mearker, 2024. "A Systematic Review on Digital Soil Mapping Approaches in Lowland Areas," Land, MDPI, vol. 13(3), pages 1-22, March.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:3:p:379-:d:1358488
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    References listed on IDEAS

    as
    1. Xiaochen Liu & Zhenxing Bian & Zhentao Sun & Chuqiao Wang & Zhiquan Sun & Shuang Wang & Guoli Wang, 2023. "Integrating Landscape Pattern Metrics to Map Spatial Distribution of Farmland Soil Organic Carbon on Lower Liaohe Plain of Northeast China," Land, MDPI, vol. 12(7), pages 1-19, July.
    2. Fuat Kaya & Ali Keshavarzi & Rosa Francaviglia & Gordana Kaplan & Levent Başayiğit & Mert Dedeoğlu, 2022. "Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus," Agriculture, MDPI, vol. 12(7), pages 1-27, July.
    3. Odunayo David Adeniyi & Alexander Brenning & Alice Bernini & Stefano Brenna & Michael Maerker, 2023. "Digital Mapping of Soil Properties Using Ensemble Machine Learning Approaches in an Agricultural Lowland Area of Lombardy, Italy," Land, MDPI, vol. 12(2), pages 1-17, February.
    4. Kanat Samarkhanov & Jilili Abuduwaili & Alim Samat & Yongxiao Ge & Wen Liu & Long Ma & Zhassulan Smanov & Gabit Adamin & Azamat Yershibul & Zhassulan Sadykov, 2022. "Dimensionality-Transformed Remote Sensing Data Application to Map Soil Salinization at Lowlands of the Syr Darya River," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
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