Scholarlink

<p>The digital soil mapping (DSM) technique was widely implemented to uncover the various soil-dependent variables and phenomena. This research emphasizes the importance of the DSM in monitoring soil profile- and horizon-development indices (PDI and HDIs, respectively) across the East Azerbaijan Province, Iran. The random forest (RF) modeling technique and suitable environmental covariates were used. Six hundred seventy soil profile data were also applied to determine the PDI and HDIs. A continuous spline function within the R environment was primarily fitted to HDIs values in advance of creating a dataset at two standard depth intervals: (I) surface (HDI<sub>sur</sub>: 0–25 cm) and (II) subsurface (HDI<sub>sub</sub>: 25–100 cm). The uncertainty of the provided maps was then quantified using the bootstrapping method. The results revealed that the performance of predictions increased when the data was combined. The diffuse insolation and slope are farther away from other covariates. Climate was also ranked as the next priority. The RF modeling respectively underestimated the PDI and HDI<sub>sur</sub> by 7.63% and 2.34%, while it overestimated by 10.01% in predicting HDI<sub>sub</sub>. Overall, the PDI normalized by area within soil order boundaries shows the following soil orders rank by development as expected: Mollisols &gt; Aridisols &gt; Inceptisols &gt; Entisols. The digital maps justified that the soils in lithic subgroups are weakly developed among the present subgroups. It was also found that Typic Aquisalids are more developed soils than Xeric Haplocalcids, indicating the role of water in soil formation and development. This research showed that the DSM technique successfully addresses process-based soil–landscape development.</p> Graphical abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Unravelling the role of digital soil mapping to assess the soil subgroup development in northwestern Iran

  • 期刊论文

  • 发表日期：

    2025年7月23日

查看全文
  • Behzad Mohammadhosseini Sagayesh,
  • Ali Asghar Jafarzadeh,
  • Farzin Shahbazi,
  • Asghar Farajnia

摘要

The digital soil mapping (DSM) technique was widely implemented to uncover the various soil-dependent variables and phenomena. This research emphasizes the importance of the DSM in monitoring soil profile- and horizon-development indices (PDI and HDIs, respectively) across the East Azerbaijan Province, Iran. The random forest (RF) modeling technique and suitable environmental covariates were used. Six hundred seventy soil profile data were also applied to determine the PDI and HDIs. A continuous spline function within the R environment was primarily fitted to HDIs values in advance of creating a dataset at two standard depth intervals: (I) surface (HDIsur: 0–25 cm) and (II) subsurface (HDIsub: 25–100 cm). The uncertainty of the provided maps was then quantified using the bootstrapping method. The results revealed that the performance of predictions increased when the data was combined. The diffuse insolation and slope are farther away from other covariates. Climate was also ranked as the next priority. The RF modeling respectively underestimated the PDI and HDIsur by 7.63% and 2.34%, while it overestimated by 10.01% in predicting HDIsub. Overall, the PDI normalized by area within soil order boundaries shows the following soil orders rank by development as expected: Mollisols > Aridisols > Inceptisols > Entisols. The digital maps justified that the soils in lithic subgroups are weakly developed among the present subgroups. It was also found that Typic Aquisalids are more developed soils than Xeric Haplocalcids, indicating the role of water in soil formation and development. This research showed that the DSM technique successfully addresses process-based soil–landscape development.

Graphical abstract