IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v230y2020ics0378377419309552.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377419309552
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2019.105950?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. 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).
    3. 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).
    4. 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).
    5. 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).
    6. Gonçalves, Ivo Zution & Mekonnen, Mesfin M. & Neale, Christopher M.U. & Campos, Isidro & Neale, Michael R., 2020. "Temporal and spatial variations of irrigation water use for commercial corn fields in Central Nebraska," Agricultural Water Management, Elsevier, vol. 228(C).
    7. Bispo, R.C. & Hernandez, F.B.T. & Gonçalves, I.Z. & Neale, C.M.U. & Teixeira, A.H.C., 2022. "Remote sensing based evapotranspiration modeling for sugarcane in Brazil using a hybrid approach," Agricultural Water Management, Elsevier, vol. 271(C).
    8. Jovanovic, N. & Pereira, L.S. & Paredes, P. & Pôças, I. & Cantore, V. & Todorovic, M., 2020. "A review of strategies, methods and technologies to reduce non-beneficial consumptive water use on farms considering the FAO56 methods," Agricultural Water Management, Elsevier, vol. 239(C).
    9. Fernández-Pacheco, D.G. & Ferrández-Villena, M. & Molina-Martínez, J.M. & Ruiz-Canales, A., 2015. "Performance indicators to assess the implementation of automation in water user associations: A case study in southeast Spain," Agricultural Water Management, Elsevier, vol. 151(C), pages 87-92.
    10. López, Juan A. & Navarro, H. & Soto, F. & Pavón, N. & Suardíaz, J. & Torres, R., 2015. "GAIA2: A multifunctional wireless device for enhancing crop management," Agricultural Water Management, Elsevier, vol. 151(C), pages 75-86.
    11. 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.
    12. Mahmoud, Shereif H. & Gan, Thian Yew, 2019. "Irrigation water management in arid regions of Middle East: Assessing spatio-temporal variation of actual evapotranspiration through remote sensing techniques and meteorological data," Agricultural Water Management, Elsevier, vol. 212(C), pages 35-47.
    13. Athanasios Balafoutis & Bert Beck & Spyros Fountas & Jurgen Vangeyte & Tamme Van der Wal & Iria Soto & Manuel Gómez-Barbero & Andrew Barnes & Vera Eory, 2017. "Precision Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics," Sustainability, MDPI, vol. 9(8), pages 1-28, July.
    14. Pankaj Dey, 2023. "On the Structure of the Intermittency of Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1461-1472, February.
    15. Shao, Guomin & Han, Wenting & Zhang, Huihui & Zhang, Liyuan & Wang, Yi & Zhang, Yu, 2023. "Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods," Agricultural Water Management, Elsevier, vol. 276(C).
    16. Domínguez-Niño, Jesús María & Oliver-Manera, Jordi & Girona, Joan & Casadesús, Jaume, 2020. "Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors," Agricultural Water Management, Elsevier, vol. 228(C).
    17. 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).
    18. Ouaadi, Nadia & Jarlan, Lionel & Khabba, Saïd & Le Page, Michel & Chakir, Adnane & Er-Raki, Salah & Frison, Pierre-Louis, 2023. "Are the C-band backscattering coefficient and interferometric coherence suitable substitutes of NDVI for the monitoring of the FAO-56 crop coefficient?," Agricultural Water Management, Elsevier, vol. 282(C).
    19. Beeson Jr., R.C., 2011. "Weighing lysimeter systems for quantifying water use and studies of controlled water stress for crops grown in low bulk density substrates," Agricultural Water Management, Elsevier, vol. 98(6), pages 967-976, April.
    20. Kelechi Igwe & Vaishali Sharda & Trevor Hefley, 2023. "Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach," Land, MDPI, vol. 12(8), pages 1-26, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:230:y:2020:i:c:s0378377419309552. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.