Dr. Ali Zolfaghari | Soil-Plant Relations | Editorial Board Member

Associate Professor at Semnan University | Iran

Dr. AliAsghar Zolfaghari is an Iranian soil physicist and Associate Professor at Semnan University, widely recognized for his influential contributions in Digital Soil Mapping for Spatial Prediction of Soil Properties, Prediction of Soil Hydraulic Properties Using Pedotransfer Functions, Effect of Climate Change on Soil Erosion and Vegetation Cover, Climate Change and Projection of Reference Evapotranspiration, Spatial and Temporal Variability of Reference Evapotranspiration, Bias Correction of Climatic Variables Using Machine Learning Methods, Machine Learning Approaches for Crop Yield Prediction Using Remote Sensing and Climatic Variables, Estimation of Soil Moisture Using SAR Data for Crop Yield Prediction, Spatial Variability of Soil Physical and Hydraulic Properties, Scaling of Infiltration, Determination of Soil Hydraulic Properties Under Field Conditions, and Prediction of Soil Properties Using Easily Available Variables Such as Soil Colour. His research portfolio spans numerous impactful publications, including major works such as Using Quantile Mapping and Random Forest for Bias-Correction of High-Resolution Reanalysis Data and CMIP6 Climate Projections, Machine Learning Method to Estimate Evapotranspiration, Enhancing Fire Susceptibility Mapping Through Machine Learning and Geospatial Analysis, Infection Severity of Arceuthobium oxycedri in Protected Mountain Ecosystems, Comparison of Machine Learning Algorithms for Predicting and Mapping Soil Available Water, Estimation of Vegetation Changes Across Climatic Gradients, Predicting Soil Properties Using Fast and Inexpensive Colour Sensor Data, Evaluation of Mathematical Models for Particle Size Distribution Prediction, Spatial Prediction of Soil Particle Size Distribution in Arid Lands, Sensitivity Analysis of Reference Evapotranspiration in Humid Climate, Mapping Soil Organic Carbon Using MIR Spectroscopy and Environmental Covariates, Observation and Simulation of Water Movement in Water-Repellent Soils, Prediction of Soil Macronutrients Using Fractal Parameters and Artificial Intelligence, Impact of Biochar on Soil Water Repellency, Assessing Performance of Decision Tree and Neural Network Models in Mapping Soil Properties, and Symbiosis of Arbuscular Mycorrhizal Fungi With Drought-Stressed Plant Species. Dr. Zolfaghari leads major national projects on flood-prone area mapping, disaster risk assessment, digital soil mapping integration with hydrological models, satellite-based soil property estimation, and climate-related hazard analysis. He supervises PhD and MSc research in soil physics, environmental modeling, evapotranspiration, satellite-based soil assessment, hydrological modeling, and digital soil mapping. With deep expertise in R, Python, GIS, Google Earth Engine, MATLAB, and environmental modeling platforms, he is also an academic leader serving in roles such as department dean, research deputy, laboratory manager, and consultant for agricultural enterprises. His scholarly and professional accomplishments, combined with numerous awards for teaching and research excellence, position him as one of the leading experts advancing soil physics, environmental modeling, and climate-informed land management in arid and semiarid regions.

Profile : Scopus | ORCID

Featured Publications : 

 (2025). Ensemble machine learning for predicting soil hydraulic properties in semi-arid regions. Modeling Earth Systems and Environment.

(2025). Identification of flash flood-prone areas in arid and semi-arid regions using optical and radar imagery (Case study: Semnan province). Water and Soil Management and Modeling.

(2025). Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data. Journal of Hydrology Regional Studies.