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Enhancing the accuracy of digital soil mapping using the surface and subsurface soil characteristics as continuous diagnostic layers

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Abstract

Digital soil mapping relies on relating soils to a particular set of covariates, which capture inherent soil spatial variation. In digital mapping of soil classes, the most commonly used covariates are topographic attributes, RS attributes, and maps, including geology, geomorphology, and land use; in contrast, the subsurface soil characteristics are usually ignored. Therefore, we investigate the possibility of using soil diagnostic characteristics as covariates in a mountainous landscape as the main aim of this study. Conventional covariates (CC) and a combination of soil subsurface covariates with conventional covariates (SCC) were used as covariates, and random forest (RF), Multinomial Logistic Regression (LR), and C5.0 Decision Trees (C5) were used as different machine learning algorithms in digital mapping of soil family classes. Based on the results, the RF model with the SCC dataset had the best performance (KC = 0.85, OA = 90). In all three models, adding soil covariates to the sets of covariates increased the model performance. Soil covariates, slope, and aspect were selected as the principal auxiliary variables in describing the distribution of soil family classes.

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Funding

This research was funded by the University of Tehran and the College of Agriculture and Natural Resources under grant number 7104017/6/19.

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Authors and Affiliations

  1. Horticulture Crop Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran

    Maryam Osat

  2. Soil Science Department, University of Tehran, Karaj, 31587-77871, Iran

    Ahmad Heidari

  3. Soil and Water Research Department, Kermanshah Agricultural and Natural Resources Research and Education Center, AREEO, Kermanshah, Iran

    Shahrokh Fatehi

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Maryam Osat and Ahmad Heidari did fieldwork and performed the primary data analysis. Maryam Osat and Shahrokh Fatehi analyzed the data and prepared digital soil maps. Maryam Osat wrote the main manuscript text and prepared all figures and tables. All authors read and approved the final manuscript.

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Osat, M., Heidari, A. & Fatehi, S. Enhancing the accuracy of digital soil mapping using the surface and subsurface soil characteristics as continuous diagnostic layers. Environ Monit Assess 196, 55 (2024). https://doi.org/10.1007/s10661-023-12088-7

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