Evaluate and improve crop yield estimation models by assimilating UAV and satellite remote sensing data - Year two

It has been widely recognized that satellite remote sensing data have a great potential in retrieval of crop biophysical variable such as Leaf Area Index (LAI), vegetation canopy cover and fraction of absorbed photosynthetically active radiation (fAPAR), that are indicative of crop growth condition and yield formation. Unmanned Aerial Vehicle (UAV) data are popular in precision agriculture applications, due to their advantage of flexibility, low cost and high spatial resolution.
This project proposes to (1) Calibrate and evaluate several crop yield models for corn and winter wheat in Southwestern Ontario through assimilating different remotely sensed datasets acquired by satellites and UAV. TO BE CONT'D

Chunhua Liao
Faculty Supervisor: 
Jinfei Wang
Partner University: