ArticleEnvironmental monitoring and assessment2021
Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms.
Article in Environmental monitoring and assessment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Data fusion of EnMAP and sentinel-2 for high-resolution soil fertility assessment in wheat cultivation of central Khuzestan plain.Scientific reports · 2026Article
- Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning.PloS one · 2023Article
- Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches.Frontiers in plant science · 2023Article
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Authors and funding
5 authors.
Funding
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Abstract
Assessing the role of machine learning (ML) models concerning environmental predictors on spatial variation of soil organic carbon stocks (SOCS) in arid rangelands is very necessary. This study was conducted to explore the variability of surface SOCS in rangeland in the west of Iran using ML approaches. A number of 33 environmental predictors derived from Sentinel-2B and DEM were used. The optimal soil sampling (n = 80) position was determined by Latin hypercube sampling (cLHS) method. Robust and popular random Forest (RF), cubist (CB) along with random forest-ordinary kriging (RF-OK), and cubist-ordinary kriging (CB-OK) hybrid ML models were applied to the prediction of SOCS. Ten-fold CV was implemented for modeling performance and uncertainty map. According to data analysis, the maximum, minimum, and average values of SOCS are 44.50, 10.50, and 20.50 (ton. ha
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