Methods to estimate evapotranspiration (ET) over large area, such as at watershed scale, rely on land-surface models and the use of remote sensing data. Within modelling frameworks that use remote sensing data, ET estimation has focused on the use of vegetation indices (VIs) to represent the variability of vegetation and their ET response. The purpose of this study was to explore the relationship between common VIs derived from a hyperspectral sensor and ET over a growing season. Further we explore, using a Random Forest (RF) approach, the vegetation response at numerous spectral regions to ET. Using a network of large weighing lysimeters we measured and recorded ET and VIs over a summer growing season. Most tested VIs had an insignificant correlation to ET measurements, including LAI which was found to have a weak relationship with the ET measurements (R = 0.529). However, NDVI and SAVI (soil adjusted vegetation index) were found to have strong (R = 0.844 and 0.923 respectively), significant relationships to the measured ET amounts, proving to be a better representation than several commonly used VIs. Results from the RF model demonstrated several spectral regions sensitive to ET amounts, however when isolated their responses did not match the relationships observed using SAVI.