Samuel N Araya

Postdoctoral Scholar

Stanford University

I am interested in data-driven analysis of surface and environmental variables to address soil hydrology and biogeochemistry problems. My current research involves developing remote sensing-based techniques to decipher arsenic and other heavy metal contaminants in rice.

My doctoral research experience involved the use of machine learning, numerical simulations, and unmanned aircraft systems (UAS) to investigate soil structure and soil hydrology.


  • Soils
  • Data science
  • Machine learning
  • Spatial analysis


  • PhD in Environmental Systems, 2019

    University of California, Merced

  • MSc in Environmental Systems, 2014

    University of California, Merced

  • BSc in Land Resources and Environment, 2007

    Asmara University

Technical Skills

Soil Science

R | Python | JavaScript

Machine Learning




Linking sub-subsurface biogeochemistry with surface observations

Using data science methods to understand sub-surface biogeochemistry from surface observations

Rice arsenic contamination from a drone

Arsenic detection in rice from drone images using machine learning

Machine Learning Pedotransfer

Unsaturated hydraulic conductivity pedotransfer using machine learning.

Long-term conservation agriculture impact on soil hydrology

Impact of long-term conservation agriculture on soil hydraulic properties and moisture storage

Soil moisture observation from a drone

High resolution soil moisture prediction from drone images using machine learning


  • 367 Panama Street, Stanford, CA, 94305, United States
  • Green Earth Sciences, Room 323