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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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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).
    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.
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    2. Keabetswe, Larona & He, Yiyin & Li, Chao & Zhou, Zhenjiang, 2024. "Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields," Agricultural Water Management, Elsevier, vol. 306(C).
    3. Peddinti, Srinivasa Rao & Kisekka, Isaya, 2025. "Evaluation of the LI-710 evapotranspiration sensor in comparison to full eddy covariance for monitoring energy fluxes in perennial and annual crops," Agricultural Water Management, Elsevier, vol. 313(C).

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