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Evaluation of variable rate irrigation using a remote-sensing-based model

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  • Barker, J. Burdette
  • Heeren, Derek M.
  • Neale, Christopher M.U.
  • Rudnick, Daran R.

Abstract

Improvements in soil water balance modeling can be beneficial for optimizing irrigation management to account for spatial variability in soil properties and evapotranspiration (ET). A remote-sensing-based ET and water balance model was tested for irrigation management in an experiment at two University of Nebraska-Lincoln research sites located near Mead and Brule, Nebraska. Both fields included a center pivot equipped with variable rate irrigation (VRI). The study included maize in 2015 and 2016 and soybean in 2016 at Mead, and maize in 2016 at Brule, for a total of 210 plot-years. Four irrigation treatments were applied at Mead, including: VRI based on a remote sensing model (VRI-RS); VRI based on neutron probe soil water content measurement (VRI-NP); uniform irrigation based on neutron probe measurement; and rainfed. Only the VRI-RS and uniform treatments were applied at Brule. Landsat 7 and 8 imagery were used for model input. In 2015, the remote sensing model included reflectance-based crop coefficients for ET estimation in the water balance. In 2016, a hybrid component of the model was activated, which included energy-balance-modeled ET as an input. Both 2015 and 2016 had above-average precipitation at Mead; subsequently, irrigation amounts were relatively low. Seasonal irrigation was greatest for the VRI-RS treatment in all cases because of drift in the water balance model. This was likely caused by excessive soil evaporation estimates. Irrigation application for the VRI-NP at Mead was about 0 mm, 6 mm, and –12 mm less in separate analyses than for the uniform treatment. Irrigation for the VRI-RS was about 40 mm, 50 mm, and –98 mm greater in separate analyses than the uniform at Mead and about 18 mm greater at Brule. For maize at Mead, treatment effects were primarily limited to hydrologic responses (e.g., ET), with differences in yield generally attributed to random error. Rainfed soybean yields were greater than VRI-RS yields, which may have been related to yield loss from lodging, perhaps due to over-irrigation. Regarding the magnitude of spatial variability in the fields, soil available water capacity generally ranked above ET, precipitation, and yield. Future research should include increased cloud-free imagery frequency, incorporation of soil water content measurements into the model, and improved wet soil evaporation and drainage estimates.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:agiwat:v:203:y:2018:i:c:p:63-74
    DOI: 10.1016/j.agwat.2018.02.022
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    References listed on IDEAS

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    1. 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.
    2. Barker, J. Burdette & Franz, Trenton E. & Heeren, Derek M. & Neale, Christopher M.U. & Luck, Joe D., 2017. "Soil water content monitoring for irrigation management: A geostatistical analysis," Agricultural Water Management, Elsevier, vol. 188(C), pages 36-49.
    3. Odhiambo, L.O. & Irmak, S., 2012. "Evaluation of the impact of surface residue cover on single and dual crop coefficient for estimating soybean actual evapotranspiration," Agricultural Water Management, Elsevier, vol. 104(C), pages 221-234.
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    4. Hui, Xin & Lin, Xueji & Zhao, Yue & Xue, Mengyun & Zhuo, Yue & Guo, Hui & Xu, Yuncheng & Yan, Haijun, 2022. "Assessing water distribution characteristics of a variable-rate irrigation system," Agricultural Water Management, Elsevier, vol. 260(C).
    5. Amazirh, Abdelhakim & Er-Raki, Salah & Ojha, Nitu & Bouras, El houssaine & Rivalland, Vincent & Merlin, Olivier & Chehbouni, Abdelghani, 2022. "Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions," Agricultural Water Management, Elsevier, vol. 260(C).
    6. Lo, Tsz Him & Rudnick, Daran R. & Singh, Jasreman & Nakabuye, Hope Njuki & Katimbo, Abia & Heeren, Derek M. & Ge, Yufeng, 2020. "Field assessment of interreplicate variability from eight electromagnetic soil moisture sensors," Agricultural Water Management, Elsevier, vol. 231(C).
    7. 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).
    8. El-Naggar, A.G. & Hedley, C.B. & Horne, D. & Roudier, P. & Clothier, B.E., 2020. "Soil sensing technology improves application of irrigation water," Agricultural Water Management, Elsevier, vol. 228(C).
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    11. Pôças, I. & Calera, A. & Campos, I. & Cunha, M., 2020. "Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches," Agricultural Water Management, Elsevier, vol. 233(C).
    12. 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).
    13. 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).

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