MACHINE LEARNING AND BIG DATA TECHNIQUES FOR SATELLITE-BASED RICE PHENOLOGY MONITORING

UoM administered thesis: Master of Philosophy

  • Authors:
  • Andres Aguilar Ariza

Abstract

New sources of information are required to support rice production decisions. To cope with this challenge, studies have found practical applications on mapping rice through the use of remote sensing techniques. This study attempts to implement a methodology aimed at mon- itoring rice phenology using optical satellite data. The relationship between rice phenology and reflectance metrics was explored at two levels: growth stages and biophysical modifications caused by diseases. Two optical moderate-resolution missions were combined to detect growth phases. Three machine learning approaches (random forest, support vector machine, and gra- dient boosting trees) were trained with multitemporal NDVI data. Analytics from validation showed that the algorithms were able to estimate rice phases with performances above 0.94 in f-1 score. Tested models yielded an overall accuracy of 71.8%, 71.2%, 60.9% and 94.7% for vegetative, reproductive, ripening and harvested categories. A second exploration was carried out by combining Sentinel-2 data and ground-based information about rice disease incidence. K-means clustering was used to map rice biophysical changes across reproductive and ripening phases. The findings ascertained the remote sensing capabilities to create new information about rice for Colombia’s conditions.

Details

Original languageEnglish
Awarding Institution
Supervisors/Advisors
  • Joseph Fennell (Supervisor)
  • Sarah Bridle (Supervisor)
Award date31 Dec 2019