IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v295y2024ics0378377424000891.html
   My bibliography  Save this article

Influence of the sampling time interval of canopy temperature on the dynamic zoning of variable rate irrigation

Author

Listed:
  • Zhang, Minne
  • Zhao, Weixia
  • Zhu, Changxin
  • Li, Jiusheng

Abstract

Generating spatial distribution maps of canopy temperature (Tc) is the basis for the dynamic management of a variable rate irrigation (VRI) system based on infrared thermometers (IRTs). To improve the accuracy of management zone delineation based on Tc maps for summer maize and winter wheat with IRTs mounted on a moving sprinkler irrigation system, the influence of sampling time interval of Tc was studied and the VRI prescription maps were compared with those obtained from unmanned aerial vehicle (UAV) thermal imaging on the same day. The experimental sites were located in Shunyi, Beijing, for summer maize and in Dacaozhuang, Hebei Province, for winter wheat. The sampling time intervals were selected as 0.5–26 min. The results demonstrated that the nRMSE of the predicted and measured values for Tc increased and that their correlation coefficient decreased with the extension of the sampling time interval. When the sampling time interval ranged from 0.5 to 5 min, the correlation coefficient and nRMSE between the predicted and measured values for Tc changed little, and the overlap percentage of the same management zones delineated with NRCT between 5 min and the minimum sampling time interval (0.5 or 1 min) was greater than 79%. For any two measuring dates of Tc, their overlap percentage changed greatly, with a maximum value of 67% for NRCT. The average irrigation amount based on the prescription maps of the IRTs system and UAV thermal imaging system on the three observation days was equal. Our results demonstrated techniques to collect IRTs data and to generate VRI prescription map based on this data, and a sampling time interval of no more than 5 minutes was recommended for Tc.

Suggested Citation

  • Zhang, Minne & Zhao, Weixia & Zhu, Changxin & Li, Jiusheng, 2024. "Influence of the sampling time interval of canopy temperature on the dynamic zoning of variable rate irrigation," Agricultural Water Management, Elsevier, vol. 295(C).
  • Handle: RePEc:eee:agiwat:v:295:y:2024:i:c:s0378377424000891
    DOI: 10.1016/j.agwat.2024.108754
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377424000891
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2024.108754?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. Falkenberg, Nyland R. & Piccinni, Giovanni & Cothren, J. Tom & Leskovar, Daniel I. & Rush, Charlie M., 2007. "Remote sensing of biotic and abiotic stress for irrigation management of cotton," Agricultural Water Management, Elsevier, vol. 87(1), pages 23-31, January.
    2. 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).
    3. O’Shaughnessy, Susan A. & Evett, Steven R. & Colaizzi, Paul D., 2015. "Dynamic prescription maps for site-specific variable rate irrigation of cotton," Agricultural Water Management, Elsevier, vol. 159(C), pages 123-138.
    4. DeJonge, Kendall C. & Taghvaeian, Saleh & Trout, Thomas J. & Comas, Louise H., 2015. "Comparison of canopy temperature-based water stress indices for maize," Agricultural Water Management, Elsevier, vol. 156(C), pages 51-62.
    5. Elsayed, Salah & Elhoweity, Mohamed & Ibrahim, Hazem H. & Dewir, Yaser Hassan & Migdadi, Hussein M. & Schmidhalter, Urs, 2017. "Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes," Agricultural Water Management, Elsevier, vol. 189(C), pages 98-110.
    6. O'Shaughnessy, Susan A. & Evett, Steven R. & Colaizzi, Paul D. & Howell, Terry A., 2012. "A crop water stress index and time threshold for automatic irrigation scheduling of grain sorghum," Agricultural Water Management, Elsevier, vol. 107(C), pages 122-132.
    Full references (including those not matched with items on IDEAS)

    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. Nakabuye, Hope Njuki & Rudnick, Daran & DeJonge, Kendall C. & Lo, Tsz Him & Heeren, Derek & Qiao, Xin & Franz, Trenton E. & Katimbo, Abia & Duan, Jiaming, 2022. "Real-time irrigation scheduling of maize using Degrees Above Non-Stressed (DANS) index in semi-arid environment," Agricultural Water Management, Elsevier, vol. 274(C).
    2. Katimbo, Abia & Rudnick, Daran R. & DeJonge, Kendall C. & Lo, Tsz Him & Qiao, Xin & Franz, Trenton E. & Nakabuye, Hope Njuki & Duan, Jiaming, 2022. "Crop water stress index computation approaches and their sensitivity to soil water dynamics," Agricultural Water Management, Elsevier, vol. 266(C).
    3. Wu, Yinshan & Jiang, Jie & Zhang, Xiufeng & Zhang, Jiayi & Cao, Qiang & Tian, Yongchao & Zhu, Yan & Cao, Weixing & Liu, Xiaojun, 2023. "Combining machine learning algorithm and multi-temporal temperature indices to estimate the water status of rice," Agricultural Water Management, Elsevier, vol. 289(C).
    4. Anzhen Qin & Dongfeng Ning & Zhandong Liu & Sen Li & Ben Zhao & Aiwang Duan, 2021. "Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield," Agriculture, MDPI, vol. 11(10), pages 1-16, October.
    5. Colaizzi, Paul D. & O’Shaughnessy, Susan A. & Evett, Steve R. & Mounce, Ryan B., 2017. "Crop evapotranspiration calculation using infrared thermometers aboard center pivots," Agricultural Water Management, Elsevier, vol. 187(C), pages 173-189.
    6. Katimbo, Abia & Rudnick, Daran R. & Liang, Wei-zhen & DeJonge, Kendall C. & Lo, Tsz Him & Franz, Trenton E. & Ge, Yufeng & Qiao, Xin & Kabenge, Isa & Nakabuye, Hope Njuki & Duan, Jiaming, 2022. "Two source energy balance maize evapotranspiration estimates using close-canopy mobile infrared sensors and upscaling methods under variable water stress conditions," Agricultural Water Management, Elsevier, vol. 274(C).
    7. Ezenne, G.I. & Jupp, Louise & Mantel, S.K. & Tanner, J.L., 2019. "Current and potential capabilities of UAS for crop water productivity in precision agriculture," Agricultural Water Management, Elsevier, vol. 218(C), pages 158-164.
    8. Morales-Santos, Angela & Nolz, Reinhard, 2023. "Assessment of canopy temperature-based water stress indices for irrigated and rainfed soybeans under subhumid conditions," Agricultural Water Management, Elsevier, vol. 279(C).
    9. O’Shaughnessy, Susan A. & Evett, Steven R. & Colaizzi, Paul D., 2015. "Dynamic prescription maps for site-specific variable rate irrigation of cotton," Agricultural Water Management, Elsevier, vol. 159(C), pages 123-138.
    10. 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).
    11. Drechsler, Kelley & Kisekka, Isaya & Upadhyaya, Shrinivasa, 2019. "A comprehensive stress indicator for evaluating plant water status in almond trees," Agricultural Water Management, Elsevier, vol. 216(C), pages 214-223.
    12. 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).
    13. Cheng, Minghan & Sun, Chengming & Nie, Chenwei & Liu, Shuaibing & Yu, Xun & Bai, Yi & Liu, Yadong & Meng, Lin & Jia, Xiao & Liu, Yuan & Zhou, Lili & Nan, Fei & Cui, Tengyu & Jin, Xiuliang, 2023. "Evaluation of UAV-based drought indices for crop water conditions monitoring: A case study of summer maize," Agricultural Water Management, Elsevier, vol. 287(C).
    14. Marek, Gary & Gowda, Prasanna & Marek, Thomas & Auvermann, Brent & Evett, Steven & Colaizzi, Paul & Brauer, David, 2016. "Estimating preseason irrigation losses by characterizing evaporation of effective precipitation under bare soil conditions using large weighing lysimeters," Agricultural Water Management, Elsevier, vol. 169(C), pages 115-128.
    15. Khorsand, Afshin & Rezaverdinejad, Vahid & Asgarzadeh, Hossein & Majnooni-Heris, Abolfazl & Rahimi, Amir & Besharat, Sina, 2019. "Irrigation scheduling of maize based on plant and soil indices with surface drip irrigation subjected to different irrigation regimes," Agricultural Water Management, Elsevier, vol. 224(C), pages 1-1.
    16. Han, Ming & Zhang, Huihui & DeJonge, Kendall C. & Comas, Louise H. & Gleason, Sean, 2018. "Comparison of three crop water stress index models with sap flow measurements in maize," Agricultural Water Management, Elsevier, vol. 203(C), pages 366-375.
    17. Zhang, Liyuan & Zhang, Huihui & Zhu, Qingzhen & Niu, Yaxiao, 2023. "Further investigating the performance of crop water stress index for maize from baseline fluctuation, effects of environmental factors, and variation of critical value," Agricultural Water Management, Elsevier, vol. 285(C).
    18. Silva, Marcos Dornelas Freitas Machado e & Calijuri, Maria Lúcia & Sales, Francisco José Ferreira de & Souza, Mauro Henrique Batalha de & Lopes, Lucas Sampaio, 2014. "Integration of technologies and alternative sources of water and energy to promote the sustainability of urban landscapes," Resources, Conservation & Recycling, Elsevier, vol. 91(C), pages 71-81.
    19. Vantyghem, Mathilde & Merckx, Roel & Stevens, Bert & Hood-Nowotny, Rebecca & Swennen, Rony & Dercon, Gerd, 2022. "The potential of stable carbon isotope ratios and leaf temperature as proxies for drought stress in banana under field conditions," Agricultural Water Management, Elsevier, vol. 260(C).
    20. Oweis, T.Y. & Farahani, H.J. & Hachum, A.Y., 2011. "Evapotranspiration and water use of full and deficit irrigated cotton in the Mediterranean environment in northern Syria," Agricultural Water Management, Elsevier, vol. 98(8), pages 1239-1248, May.

    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:295:y:2024:i:c:s0378377424000891. 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.