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

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  • Bhatti, Sandeep
  • Heeren, Derek M.
  • Barker, J. Burdette
  • Neale, Christopher M.U.
  • Woldt, Wayne E.
  • Maguire, Mitchell S.
  • Rudnick, Daran R.

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 (Ip) for VRI-L was significantly greater (α = 0.05) than the Ip for U for soybean. In 2018, Ip 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 Ip 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.

Suggested Citation

  • Bhatti, Sandeep & Heeren, Derek M. & Barker, J. Burdette & Neale, Christopher M.U. & Woldt, Wayne E. & Maguire, Mitchell S. & Rudnick, Daran R., 2020. "Site-specific irrigation management in a sub-humid climate using a spatial evapotranspiration model with satellite and airborne imagery," Agricultural Water Management, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:agiwat:v:230:y:2020:i:c:s0378377419309552
    DOI: 10.1016/j.agwat.2019.105950
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    1. Daccache, A. & Knox, J.W. & Weatherhead, E.K. & Daneshkhah, A. & Hess, T.M., 2015. "Implementing precision irrigation in a humid climate – Recent experiences and on-going challenges," Agricultural Water Management, Elsevier, vol. 147(C), pages 135-143.
    2. Hedley, C.B. & Yule, I.J., 2009. "A method for spatial prediction of daily soil water status for precise irrigation scheduling," Agricultural Water Management, Elsevier, vol. 96(12), pages 1737-1745, December.
    3. Campos, Isidro & Neale, Christopher M.U. & Suyker, Andrew E. & Arkebauer, Timothy J. & Gonçalves, Ivo Z., 2017. "Reflectance-based crop coefficients REDUX: For operational evapotranspiration estimates in the age of high producing hybrid varieties," Agricultural Water Management, Elsevier, vol. 187(C), pages 140-153.
    4. Barker, J. Burdette & Heeren, Derek M. & Neale, Christopher M.U. & Rudnick, Daran R., 2018. "Evaluation of variable rate irrigation using a remote-sensing-based model," Agricultural Water Management, Elsevier, vol. 203(C), pages 63-74.
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    1. Bhatti, Sandeep & Heeren, Derek M. & Evett, Steven R. & O’Shaughnessy, Susan A. & Rudnick, Daran R. & Franz, Trenton E. & Ge, Yufeng & Neale, Christopher M.U., 2022. "Crop response to thermal stress without yield loss in irrigated maize and soybean in Nebraska," Agricultural Water Management, Elsevier, vol. 274(C).
    2. Xue, Jingyuan & Fulton, Allan & Kisekka, Isaya, 2021. "Evaluating the role of remote sensing-based energy balance models in improving site-specific irrigation management for young walnut orchards," Agricultural Water Management, Elsevier, vol. 256(C).
    3. Singh, Jasreman & Ge, Yufeng & Heeren, Derek M. & Walter-Shea, Elizabeth & Neale, Christopher M.U. & Irmak, Suat & Woldt, Wayne E. & Bai, Geng & Bhatti, Sandeep & Maguire, Mitchell S., 2021. "Inter-relationships between water depletion and temperature differential in row crop canopies in a sub-humid climate," Agricultural Water Management, Elsevier, vol. 256(C).
    4. Li, Maona & Wang, Yunling & Guo, Hui & Ding, Feng & Yan, Haijun, 2023. "Evaluation of variable rate irrigation management in forage crops: Saving water and increasing water productivity," Agricultural Water Management, Elsevier, vol. 275(C).
    5. O’Shaughnessy, Susan A. & Kim, Minyoung & Andrade, Manuel A. & Colaizzi, Paul D. & Evett, Steven R., 2020. "Site-specific irrigation of grain sorghum using plant and soil water sensing feedback - Texas High Plains," Agricultural Water Management, Elsevier, vol. 240(C).
    6. Maguire, Mitchell S. & Neale, Christopher M.U. & Woldt, Wayne E. & Heeren, Derek M., 2022. "Managing spatial irrigation using remote-sensing-based evapotranspiration and soil water adaptive control model," Agricultural Water Management, Elsevier, vol. 272(C).
    7. Souza, Silas Alves & Rodrigues, Lineu Neiva, 2022. "Increased profitability and energy savings potential with the use of precision irrigation," Agricultural Water Management, Elsevier, vol. 270(C).
    8. Chandra, Ankit & Heeren, Derek M. & Odhiambo, Lameck & Brozović, Nicholas, 2023. "Water-energy-food linkages in community smallholder irrigation schemes: Center pivot irrigation in Rwanda," Agricultural Water Management, Elsevier, vol. 289(C).

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