Machine Learning Enhances Soil Aggregate Stability Mapping for Effective Land Management in a Semi-Arid Region

Machine Learning Enhances Soil Aggregate Stability Mapping for Effective Land Management in a Semi-Arid Region

Soil aggregate stability (SAS) is needed to evaluate the soil’s resistance to degradation and erosion, especially in semi-arid regions. Traditional laboratory methods for assessing SAS are labor-intensive and costly, limiting timely and cost-effective monitoring. Thus, we developed cost-efficient wa...

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Bibliographic Details
Main Authors: Pegah Khosravani, Ali Akbar Moosavi, Majid Baghernejad, Ndiye M. Kebonye, Seyed Roohollah Mousavi, Thomas Scholten
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Remote Sensing
Subjects:
aggregate stability
digital soil mapping
machine learning
random forest
semi-arid regions Southern Iran
Online Access:https://www.mdpi.com/2072-4292/16/22/4304
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