Work

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

Year: 2024

Type: article

Abstract: 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 wall-to-wall spatial prediction maps for two fundamental SAS proxies [mean weight diameter (MWD) and geometric mean diameter (GMD)], across a 5000-hectare area in Southwest Iran. Machine learning algorithms coupled with environmental and soil covariates were used. Our results showed that topographic covariates were the most influential covariates in predicting these SAS proxies. Overall, our SAS maps are valuable tools for sustainable soil and natural resource management, enabling decision-making for addressing potential soil degradation and promoting sustainable land use in semi-arid regions.

Source: Remote Sensing

DOI: 10.3390/rs16224304

Authors: Pegah Khosravani, Ali Akbar Moosavi, Majid Baghernejad, Ndiye M. Kebonye, Seyed Roohollah Mousavi, Thomas Scholten

Institutions: Shiraz University, University of Tübingen, University of Tehran

Language: English

Topic: Soil Geostatistics and Mapping

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Zero hunger

Open Access status: gold

Estimated APC paid: $3,069