IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v283y2023ics0378377423001828.html
   My bibliography  Save this article

Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network

Author

Listed:
  • Rozenstein, Offer
  • Fine, Lior
  • Malachy, Nitzan
  • Richard, Antoine
  • Pradalier, Cedric
  • Tanny, Josef

Abstract

Recent advances in remote sensing and machine learning show potential for improving irrigation use efficiency. In this study, two independent methods to determine the irrigation dose in processing tomatoes were calibrated, validated, and tested in an irrigation experiment. The first method used multispectral imagery acquired from an unoccupied aerial vehicle (UAV) to estimate the FAO-56 crop coefficient, Kc. The second method used an artificial neural network (ANN) trained on eddy covariance measurements of latent heat flux and meteorological variables from a nearby meteorological station. An irrigation experiment was conducted, where the farmer was instructed through a mobile application with updated irrigation recommendations. Evapotranspiration estimated by the new methods was set as the irrigation dose for the UAV and ANN treatments. The best-practice irrigation, commonly used by the regional farmers, was set as the control treatment (100%), guided by an irrigation expert and soil sensors for feedback. Derivatives of this treatment at 50%, 75%, and 125% of the control irrigation dose were tested. Yield, water use efficiency (WUE), and Brix level were measured and analyzed. Results show that both methods, UAV and ANN, estimated evapotranspiration to derive the irrigation dose at a near-perfect agreement with best-practice irrigation, both in the total amount and irrigation rate. Furthermore, there were no significant differences between the best practice and the experimental treatments in yield (117 ton/ha), water-use efficiency (31.7 kg/m3), and Brix (4.5°Bx). These results demonstrate the potential of advanced machine learning techniques and aerial remote sensing to quantify crop water requirements and support irrigation management.

Suggested Citation

  • Rozenstein, Offer & Fine, Lior & Malachy, Nitzan & Richard, Antoine & Pradalier, Cedric & Tanny, Josef, 2023. "Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network," Agricultural Water Management, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:agiwat:v:283:y:2023:i:c:s0378377423001828
    DOI: 10.1016/j.agwat.2023.108317
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377423001828
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2023.108317?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. Markus Reichstein & Gustau Camps-Valls & Bjorn Stevens & Martin Jung & Joachim Denzler & Nuno Carvalhais & Prabhat, 2019. "Deep learning and process understanding for data-driven Earth system science," Nature, Nature, vol. 566(7743), pages 195-204, February.
    2. Gregoriy Kaplan & Lior Fine & Victor Lukyanov & V. S. Manivasagam & Josef Tanny & Offer Rozenstein, 2021. "Normalizing the Local Incidence Angle in Sentinel-1 Imagery to Improve Leaf Area Index, Vegetation Height, and Crop Coefficient Estimations," Land, MDPI, vol. 10(7), pages 1-23, June.
    3. Shao, Guomin & Han, Wenting & Zhang, Huihui & Liu, Shouyang & Wang, Yi & Zhang, Liyuan & Cui, Xin, 2021. "Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices," Agricultural Water Management, Elsevier, vol. 252(C).
    4. Kuşçu, Hayrettin & Turhan, Ahmet & Demir, Ali Osman, 2014. "The response of processing tomato to deficit irrigation at various phenological stages in a sub-humid environment," Agricultural Water Management, Elsevier, vol. 133(C), pages 92-103.
    5. Gregoriy Kaplan & Offer Rozenstein, 2021. "Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2," Land, MDPI, vol. 10(5), pages 1-13, May.
    6. Rozenstein, Offer & Haymann, Nitai & Kaplan, Gregoriy & Tanny, Josef, 2019. "Validation of the cotton crop coefficient estimation model based on Sentinel-2 imagery and eddy covariance measurements," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    7. Kaplan, Gregoriy & Fine, Lior & Lukyanov, Victor & Malachy, Nitzan & Tanny, Josef & Rozenstein, Offer, 2023. "Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and Leaf Area Index," Agricultural Water Management, Elsevier, vol. 276(C).
    8. Rozenstein, Offer & Haymann, Nitai & Kaplan, Gregoriy & Tanny, Josef, 2018. "Estimating cotton water consumption using a time series of Sentinel-2 imagery," Agricultural Water Management, Elsevier, vol. 207(C), pages 44-52.
    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. Sara, Ourrai & Bouchra, Aithssaine & Abdelhakim, Amazirh & Salah, Er-RAKI & Lhoussaine, Bouchaou & Frederic, Jacob & Abdelghani, Chehbouni, 2024. "Assessment of the modified two-source energy balance (TSEB) model for estimating evapotranspiration and its components over an irrigated olive orchard in Morocco," Agricultural Water Management, Elsevier, vol. 298(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. Kaplan, Gregoriy & Fine, Lior & Lukyanov, Victor & Malachy, Nitzan & Tanny, Josef & Rozenstein, Offer, 2023. "Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and Leaf Area Index," Agricultural Water Management, Elsevier, vol. 276(C).
    2. Gregoriy Kaplan & Lior Fine & Victor Lukyanov & V. S. Manivasagam & Josef Tanny & Offer Rozenstein, 2021. "Normalizing the Local Incidence Angle in Sentinel-1 Imagery to Improve Leaf Area Index, Vegetation Height, and Crop Coefficient Estimations," Land, MDPI, vol. 10(7), pages 1-23, June.
    3. Xianglong Fan & Xin Lv & Pan Gao & Lifu Zhang & Ze Zhang & Qiang Zhang & Yiru Ma & Xiang Yi & Caixia Yin & Lulu Ma, 2022. "Establishment of a Monitoring Model for the Cotton Leaf Area Index Based on the Canopy Reflectance Spectrum," Land, MDPI, vol. 12(1), pages 1-19, December.
    4. 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).
    5. Gregoriy Kaplan & Offer Rozenstein, 2021. "Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2," Land, MDPI, vol. 10(5), pages 1-13, May.
    6. Licheng Liu & Wang Zhou & Kaiyu Guan & Bin Peng & Shaoming Xu & Jinyun Tang & Qing Zhu & Jessica Till & Xiaowei Jia & Chongya Jiang & Sheng Wang & Ziqi Qin & Hui Kong & Robert Grant & Symon Mezbahuddi, 2024. "Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    7. Zhang, Xianbo & Yang, Hui & Shukla, Manoj K. & Du, Taisheng, 2023. "Proposing a crop-water-salt production function based on plant response to stem water potential," Agricultural Water Management, Elsevier, vol. 278(C).
    8. Jingwei Wang & Yuan Li & Wenquan Niu, 2020. "Deficit Alternate Drip Irrigation Increased Root-Soil-Plant Interaction, Tomato Yield, and Quality," IJERPH, MDPI, vol. 17(3), pages 1-18, January.
    9. Zhang, Shuangyi & Li, Xichen, 2021. "Future projections of offshore wind energy resources in China using CMIP6 simulations and a deep learning-based downscaling method," Energy, Elsevier, vol. 217(C).
    10. Florian Reiner & Martin Brandt & Xiaoye Tong & David Skole & Ankit Kariryaa & Philippe Ciais & Andrew Davies & Pierre Hiernaux & Jérôme Chave & Maurice Mugabowindekwe & Christian Igel & Stefan Oehmcke, 2023. "More than one quarter of Africa’s tree cover is found outside areas previously classified as forest," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    11. Shao, Guomin & Han, Wenting & Zhang, Huihui & Liu, Shouyang & Wang, Yi & Zhang, Liyuan & Cui, Xin, 2021. "Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices," Agricultural Water Management, Elsevier, vol. 252(C).
    12. Pereira, L.S. & Paredes, P. & Hunsaker, D.J. & López-Urrea, R. & Mohammadi Shad, Z., 2021. "Standard single and basal crop coefficients for field crops. Updates and advances to the FAO56 crop water requirements method," Agricultural Water Management, Elsevier, vol. 243(C).
    13. Gianluca Biggi & Martina Iori & Julia Mazzei & Andrea Mina, 2024. "Green Intelligence: The AI content of green technologies," LEM Papers Series 2024/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    14. Zhang, Huimeng & Xiong, Yunwu & Huang, Guanhua & Xu, Xu & Huang, Quanzhong, 2017. "Effects of water stress on processing tomatoes yield, quality and water use efficiency with plastic mulched drip irrigation in sandy soil of the Hetao Irrigation District," Agricultural Water Management, Elsevier, vol. 179(C), pages 205-214.
    15. Wang, Yangjun & Liu, Kefeng & Zhang, Ren & Qian, Longxia & Shan, Yulong, 2021. "Feasibility of the Northeast Passage: The role of vessel speed, route planning, and icebreaking assistance determined by sea-ice conditions for the container shipping market during 2020–2030," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    16. Richards, Daniel Rex & Lavorel, Sandra, 2022. "Integrating social media data and machine learning to analyse scenarios of landscape appreciation," Ecosystem Services, Elsevier, vol. 55(C).
    17. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    18. Guoxiong Chen & Qiuming Cheng & Timothy W. Lyons & Jun Shen & Frits Agterberg & Ning Huang & Molei Zhao, 2022. "Reconstructing Earth’s atmospheric oxygenation history using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    19. Zhang, Xinru & Hou, Lei & Liu, Jiaquan & Yang, Kai & Chai, Chong & Li, Yanhao & He, Sichen, 2022. "Energy consumption prediction for crude oil pipelines based on integrating mechanism analysis and data mining," Energy, Elsevier, vol. 254(PB).
    20. Zhou, Huanyu & Qiu, Yingning & Feng, Yanhui & Liu, Jing, 2022. "Power prediction of wind turbine in the wake using hybrid physical process and machine learning models," Renewable Energy, Elsevier, vol. 198(C), pages 568-586.

    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:283:y:2023:i:c:s0378377423001828. 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.