Site-specific irrigation management in a sub-humid climate using a spatial evapotranspiration model with satellite and airborne imagery

Authors

Sandeep Bhatti, University of Nebraska-LincolnFollow
Derek M. Heeren, University of Nebraska-LincolnFollow
J. Burdette Barker, University of Nebraska–LincolnFollow
Christopher M. U. Neale, University of Nebraska-LincolnFollow
Wayne Woldt, University of Nebraska - LincolnFollow
Mitchell S. Maguire, University of Nebraska–LincolnFollow
Daran Rudnick, University of Nebraska - LincolnFollow

Date of this Version

2020

Citation

Published in Agricultural Water Management 230 (2020) 105950

doi 10.1016/j.agwat.2019.105950

Comments

Copyright © 2019 Elsevier B.V. Used by permission.

Abstract

Variable Rate Irrigation (VRI) considers spatial variability in soil and plant characteristics to optimize irrigation management in agricultural fields. The advent of unmanned aircraft systems (UAS) creates an opportunity to utilize high-resolution (spatial and temporal) imagery into irrigation management due to decreasing costs, ease of operation, and reduction of regulatory constraints. This research aimed to evaluate the use of UAS data for VRI, and to quantify the potential of VRI in terms of relative crop and water response. Irrigation treatments were: (1) VRI using Landsat imagery (VRI-L), (2) VRI using UAS imagery (VRI-U), (3) uniform (U), and (4) rainfed (R). An updated remote-sensing-based evapotranspiration and water balance model, incorporating soil water measurements, was used to make prescriptions for the VRI treatments at a field site in eastern Nebraska. In 2017, the mean prescribed seasonal irrigation depth (I_p) for VRI-L was significantly greater (α=0.05) than the I_p for U for soybean. In 2018, I_p for soybean was greatest for VRI-U treatment followed by the U and VRI-L treatments, with all being significantly different from each other. No significant differences in I_p for maize were observed in 2017 or 2018. In all crop-year combinations, the VRI and U treatments had significantly greater evapotranspiration (ET) than the R treatment. Yield differences among treatments were not significant (except for rainfed maize compared to VRI-L in 2017). For maize in 2017, IWUE for VRI-L was comparable to the U treatment. The UAS imagery was a better match for the scale of crop management than Landsat imagery, particularly for thermal data. The multispectral UAS data was successfully used in the crop coefficient ET model for real-time irrigation, but using UAS to determine accurate canopy temperatures for surface energy balance modeling remains a challenge.