Estimating high spatiotemporal resolution evapotranspiration over a winter wheat field using an IKONOS image based complementary relationship and Lysimeter observations

• The complementary relationship model (CR) was employed to estimate actual ET.
• The high temporal ET was validated by Lysimeter observation.
• The CR model was combined with IKONOS image to estimate high spatial resolution instantaneous and daily ET.

Mapping high spatiotemporal resolution evapotranspiration (ET) over large areas is important for water resources planning, precision irrigation and monitoring water use efficiency. However, both traditional field measurement and aerodynamic estimation mainly focus on obtaining local ET. Remote sensing observations usually can be used to retrieve instantaneous ET at a low spatial resolution over region or global scale. Therefore, using field measurements and high resolution image data to generate high spatiotemporal resolution ET is becoming an important research direction. In this study, the complementary relationship model (CR) was tested together with meteorological data to estimate actual ET, and the results were validated by the Lysimeter observation. Furthermore, CR model combined with high resolution IKONOS data was used to estimate instantaneous field scale ET that was then transferred to daily ET. The cumulative evapotranspiration (ET) of winter wheat during the reproductive period from March through June of 2011 was 469.12 mm, essentially corresponding to the annual precipitation in the study area. The highest accuracy of estimating ET by CR model with remote sensing data was in May (R2 = 0.863, RMSE = 0.103 mm). The transferred daily ET by a self-preservation of evaporative fraction (EF) approach from the CR modeling instantaneous ET was consistent with The Lysimeter measurements for all four months, March through June, 2011 (R2 = 0.937, RMSE = 0.668 mm). The experimental results demonstrate that CR model can be used to accurately estimate actual ET with both meteorological data and high resolution remote sensing data at a regional scale.