Dr. Younes Garosi

Research Interests

My primary research interest lies in Pedometrics and Soil Geography, with a special focus on Digital Soil Mapping (DSM) to predict and understand the spatial variability of soil properties. I integrate soil system knowledge with machine learning, classical statistics models, and environmental variables such as remote sensing data and topographic attributes to model and predict the spatial variability of key soil physicochemical properties (e.g. soil organic carbon, soil texture fractions) and landforms (e.g. erosion susceptibility). Additionally, I specialize in processing very large geospatial datasets, combining advanced geographical information system (GIS), statistics, and environmental monitoring techniques to deepen the understanding of soil dynamics and their responses to environmental and climatic changes.

Current Project

AQUAWALD: forecast long-term forest development under climate change and examines its effects on soil nutrient dynamics and water quality.

 

Work Experience

Edication

Publications

Azizi, K. Garosi, Y., Ayoubi, Sh., Tajik. S. 2024. Integration of Sentinel-1/2 and topographic attributes to predict the spatial distribution of soil texture fractions in some agricultural soils of western Iran. Soil and Tillage Research. 229, 105681. (https://doi.org/10.1016/j.still.2023.105681).

Garosi, Y., Ayoubi, Sh., Nussbaum, M., Nael., M., Sheklabadi, M., Kimiaee, I., 2023. Use of Sintinel-1/2 satellite imagery to predict soil inorganic and organic carbon in low-relief area with the semi-arid environment . International Journal of Remote Sensing, 43(18): 6856-6880. (https://doi.org/10.1080/01431161.2022.2147037).

Garosi, Y., Ayoubi, Sh., Nussbaum, M., Sheklabadi, M., 2022. Effect of different sources and spatial resolutions of covariates to predict soil organic carbon using machine learning. Geoderma Regional, 29, e00513. (https://doi.org/10.1016/j.geodrs.2022.e00513).

Azizi, K., Ayoubi, Sh., Nabiolahi, K., Garosi, Y., Gislum, R., 2022. Predicting heavy metal contents by applying machine learning approches and environmentla covariates in west of Iran. Journal of Geochemical Exploration, 233, 10692 (https://doi.org/10.1016/j.gexplo.2021.106921).

Zerratpisheh, M., Garosi, Y., Owliaie, H.R., Ayoubi, Sh., Taghizadeh-Mehrjardi, R., Scholten, T., Xu, M., 2022. Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates. Catena, 8: 105723 (https://doi.org/10.1016/j.catena.2021.105723).

Garosi, Y., Sheklabadi, M., Conoscenti, Ch., Pourghasemi, H.R. and Van Oost, K. 2019. Assessing the performance of GIS- based machine learning models using different accuracy measures to determine the susceptibility to gully erosion. Science of Total Environment. 664: 1117-1132. (https://doi.org/10.1016/j.scitotenv.2019.02.093).

Garosi, Y., Sheklabadi, M., Porhghasemi, A.R., Besalat Pour, A.A., Conocenti, Ch., and Van Oost, K.,(2018).Comparison of the different resolution and source of controlling factors for gully erosion susceptibility mapping. Geoderma, 330: 65- 78. (https://doi.org/10.1016/j.geoderma.2018.05.027).

Jafarzadeh, A.A., Garosi, Y., Oustan, Sh, & Ahmadi, A. (2014). The effect of clay minerals on soils interrill erodibility factor and management in Dast- e Tabriz. Asia Pacific Journal of sustainable Agriculture Food and Energy, 2, 23-31.