Chair of Soil Science publishes new book chapter:

A brief review of digital soil mapping in Iran

The anthology Remote Sensing of Soil and Land Surface Processes: Monitoring, Mapping, and Modeling couples artificial intelligence and remote sensing for mapping and modeling natural resources, thus expanding the applicability of AI and machine learning for soils and landscape studies and providing a hybridized approach that also increases the accuracy of image analysis. The book covers topics including digital soil mapping, satellite land surface imagery, assessment of land degradation, and deep learning networks and their applicability to land surface processes and natural hazards, including case studies and real life examples where appropriate.

The chapter provides a brief overview of digital soil mapping (DSM) in Iran, which uses machine learning and environmental data to create soil maps for better soil management. The report reviews the background of DSM in Iran, recent developments in machine learning methods and the environmental covariates commonly used in DSM. Despite its short history in Iran, DSM has attracted significant interest in recent years, with several studies conducted in different regions of the country. The report highlights the importance of advanced machine learning methods, such as random forests and artificial neural networks, for accurate prediction and classification of soil properties. It also discusses the importance of environmental covariates such as topography, geology, climate and land use for DSM in Iran. The article concludes that DSM has the potential to play a critical role in soil management and conservation in Iran and that further research is needed to realise this potential.

Taghizadeh, R., Zeraatpisheh, M., Amirian Chakan, A., Scholten, T. (2024): A Brief Review of Digital Soil Mapping in Iran. In: Melesse, A.M., Rahamati, O. und Khsoravi, R. (Hrsg.): Remote Sensing of Soil and Land Surface Processes. Elsevier Earth Observation Series. Amsterdam, S. 217–228, ISBN: 978-0-443-15341-9.