Dr. Seyed Roohollah Mousavi | Pedometric | Best Researcher Award
Dr. Seyed Roohollah Mousavi | Pedometric | PhD Scholar at University of Tehran | Iran
Dr. Seyed Roohollah Mousavi is an accomplished soil scientist and researcher recognized for his expertise in digital soil mapping, pedometrics, and environmental modeling. He obtained his Ph.D. in Soil Resource Management from the University of Tehran, where he specialized in developing predictive models for soil properties in arid and semi-arid regions using advanced statistical and machine learning techniques. Dr. Seyed Roohollah Mousavi also holds an M.Sc. in Soil Genesis and Classification and a B.Sc. in Agricultural Engineering–Soil Science, which laid the foundation for his distinguished academic and research career. Over the years, he has contributed extensively to soil data science, integrating remote sensing, GIS, and artificial intelligence to enhance the precision of land resource evaluation. Professionally, Dr. Seyed Roohollah Mousavi has collaborated with several national and international institutions, including the Earth and Life Institute at Université Catholique de Louvain (UCLouvain), Belgium, where his research explored applications of Google Earth Engine (GEE) and R programming in soil and environmental studies. His research interests span across soil spatial modeling, soil carbon and nitrogen prediction, geostatistics, structural equation modeling, and digital soil property assessment. His technical proficiency includes GIS, remote sensing, multivariate analysis, and environmental data mining, which he applies innovatively in understanding soil-environment interactions. Dr. Seyed Roohollah Mousavi has received recognition for his outstanding contributions to soil science, including citations in high-impact journals and memberships in professional bodies such as the European Geosciences Union (EGU) and the Iranian Soil Science Society (ISSS). His academic achievements and research leadership highlight his dedication to advancing climate-smart agriculture, sustainable land use, and global soil health monitoring. Dr. Seyed Roohollah Mousavi continues to bridge data science and soil ecology, shaping the future of precision soil management and environmental conservation through innovative and evidence-based research.
Featured Publications
Mousavi, S. R., Sarmadian, F., Omid, M., & Bogaert, P. (2022). Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran. Measurement. (47 citations)
Matinfar, H. R., Maghsodi, Z., Mousavi, S. R., & Rahmani, A. (2021). Evaluation and prediction of topsoil organic carbon using machine learning and hybrid models at a field-scale. Catena. (108 citations)
Rezaei, M., Mousavi, S. R., Rahmani, A., Zeraatpisheh, M., & Rahmati, M. (2023). Incorporating machine learning models and remote sensing to assess the spatial distribution of saturated hydraulic conductivity in a light-textured soil. Computers and Electronics in Agriculture. (39 citations)
Mousavi, S. R., Sarmadian, F., Angelini, M. E., Bogaert, P., & Omid, M. (2023). Cause-effect relationships using structural equation modeling for soil properties in arid and semi-arid regions. Catena. (32 citations)
Parsaie, F., Farrokhian Firouzi, A., Mousavi, S. R., Rahmani, A., & Sedri, M. H. (2021). Large-scale digital mapping of topsoil total nitrogen using machine learning models and associated uncertainty map. Environmental Monitoring and Assessment. (39 citations)
Mousavi, S. R., Sarmadian, F., Dehghani, S., Sadikhani, M. R., & Taati, A. (2017). Evaluating inverse distance weighting and kriging methods in estimation of some physical and chemical properties of soil in Qazvin Plain. Eurasian Journal of Soil Science. (29 citations)
Mahmood Rostaminia, Z. M., Rahmani, A., Mousavi, S. R., & Taghizadeh, R. (2021). Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms. Environmental Monitoring and Assessment. (28 citations)