Spatial prediction of soil properties through hybridized random forest model and combination of reflectance spectroscopy and environmental covariates

Author:

Shahabi Aram1,Nabiollahi Kamal1ORCID,Davari Masoud1ORCID,Zeraatpisheh Mojtaba23ORCID,Heung Brandon4ORCID,Scholten Thomas567ORCID,Taghizadeh-Mehrjardi Ruhollah567ORCID

Affiliation:

1. Department of Soil Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

2. Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, USA

3. Gund Institute for Environment, University of Vermont, Burlington, VT, USA

4. Department of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, Halifax, Nova Scotia, Canada

5. Department of Geosciences, University of Tübingen, Tübingen, Germany

6. CRC 1070 Resource Cultures, University of Tübingen, Tübingen, Germany

7. DFG Cluster of Excellence “Machine Learning”, University of Tübingen, Tübingen, Germany

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Reference101 articles.

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