Ruhollah Taghizadeh

Ruhollah Taghizadeh

Postdoc Researcher

University of Tübingen

About me

My primary research interest is in Pedometrics with a particular focus on remote/proximal soil sensing and Digital Soil Mapping. The core of the pedometric approach integrates soil system knowledge with applied statistics, Machine Learning, geoinformatics, and Remote Sensing. I apply the most recent technology in spatial data analysis to model and predict various environmental metrics such as soils, water, vegetation, and climate.

I’m interested in:

  • Pedology and Digital Soil Mapping
  • GIS, Remote and Proximal Sensing
  • Spatial Data Analysis and Machine Learning
  • Soil Health, Climate Change and Precision Agriculture


Machine Learning
Remote Sensing


Tübingen University
Postdoc in Pedometrics
Jul 2017 – Present Tübingen, Germany
South Dakota State University
Postdoc in Pedometrics
May 2016 – Aug 2016 Brookings, USA
Ardakan University
Assistant Professor in Soil Science
Feb 2013 – Jun 2016 Ardakan, Iran
The University of Sydney
Postgraduate Visiting Scholar
Jan 2012 – Jul 2012 Sydney, Australia
University of Tehran
PhD Student in Soil Science
Sep 2008 – Dec 2012 Tehran, Iran


Spatial Prediction of Soils
Spatial information about the soil is needed increasingly for making informed management decisions about the environment, particularly with regard to developing different types of agriculture. In this project, machine learning models are used to quantify the spatial distribution of soils on local, regional, and national scales based on the relationships between environmental data and soil properties/classes. article1,article2,article3.
Spatial Prediction of Soils
Land Degradation Modelling
Soil degradations are predominant environmental problem world-wide their accurate assessment is essential for determining appropriate ways to deal with land degradation, for better soil and crop management. In this project, the applicability of machine learning models and remote sensing is evaluated to monitor and assess land degradations such as soil salinity, soil erosion, and air/water/soil pollution. article1,article2,article3.
Land Degradation Modelling
Soil Organic Carbon
Soil organic carbon storage is a key function of soils, influencing soil physicochemical properties. As the world’s soils contain more organic carbon than the atmosphere and the biosphere together, soils are considered to be a crucial pool in the global carbon cycle. Thus, accurate spatial information of SOC is vital to estimate and predict greenhouse gas emissions and physicochemical functions of soils. article1,article2,article3.
Soil Organic Carbon
Precision Agriculture
An increasing amount of sophisticated data, from remote sensing and especially from proximal sensing, make it possible to bridge the gap between data and decisions within agricultural planning. On-demand representative sampling and modeling of useful soil information leads to an improvement in the decision-making processes of fertilization, higher productivity, and biofuel production. article1,article2,article3.
Precision Agriculture