Using Google Earth Engine for the Automated Mapping of Center Pivot Irrigation fields in Saudi Arabia

  • Areej Alwahas

Student thesis: Master's Thesis


Groundwater is a vital non-renewable resource that is being over exploited at an alarming rate. In Saudi Arabia, the majority of groundwater is used for agricultural activities. As such, the mapping of irrigated lands is a crucial step for managing available water resources. Even though traditional in-field mapping is effective, it is expensive, physically demanding, and spatially restricted. The use of remote sensing combined with advanced computational approaches provide a potential solution to this scale problem. However, when attempted at large scales, traditional computing tends to have significant processing and storage limitations. To address the scalability challenge, this project explores open-source cloud-based resources to map and quantify center-pivot irrigation fields on a national scale. This is achieved by first applying a land cover classification using Random Forest which is a machine learning approach, and then implementing a circle detection algorithm. While the analysis represents a preliminary exploration of these emerging cloud-based techniques, there is clear potential for broad application to many problems in the Earth and environmental sciences.
Date of AwardApr 2021
Original languageEnglish (US)
Awarding Institution
  • Biological, Environmental Sciences and Engineering
SupervisorMatthew McCabe (Supervisor)


  • Google-earth-engine
  • Random Forest
  • Center pivot irrigation
  • Landcover classification
  • Saudi Arabia

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