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Applying high-resolution visible-channel aerial imaging of crop canopy to precision irrigation management

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  • Chen, Assaf
  • Orlov-Levin, Valerie
  • Meron, Moshe

Abstract

Canopy cover (or vegetation cover) maps serve in irrigation management mainly to determine the primary evapotranspiration (ET) coefficient, as radiation interception and evaporative surface area are directly related to canopy cover. Crop size and development with time depends on water supply; therefore, crop canopy maps are tools for the detection of the spatial uniformity of irrigation systems. Several aerial scan campaigns were deployed in the Upper Galilee of Israel in the 2017 and 2018 growing seasons to follow up and evaluate the irrigation uniformity and crop coefficients of peanuts and cotton by RGB scans of a Phantom 4 multirotor unmanned aerial vehicle (UAV) and DJI Mavic-Pro UAV equipped with RGB and near-infrared (NIR) sensors. Foliage intensity and coverage were enhanced by a green-red vegetation index (GRVI), which is a normalized difference vegetation index (NDVI)-like process where the green channel replaced the NIR. The results demonstrated that the GRVI is suitable for the purpose of determining the vegetation cover. Furthermore, the GRVI yielded better results than the NDVI in recognizing phenological crop changes (especially senescence) and in detecting heterogeneity in field irrigation. Therefore, this research proves the applicability of a low-cost digital camera mounted on an easily accessible UAV for crop cover and actual, in-field, ET coefficients determination and irrigation uniformity evaluation.

Suggested Citation

  • Chen, Assaf & Orlov-Levin, Valerie & Meron, Moshe, 2019. "Applying high-resolution visible-channel aerial imaging of crop canopy to precision irrigation management," Agricultural Water Management, Elsevier, vol. 216(C), pages 196-205.
    • Handle: RePEc:eee:agiwat:v:216:y:2019:i:c:p:196-205
    DOI: 10.1016/j.agwat.2019.02.017
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    References listed on IDEAS

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    1. Nahry, A.H. El & Ali, R.R. & Baroudy, A.A. El, 2011. "An approach for precision farming under pivot irrigation system using remote sensing and GIS techniques," Agricultural Water Management, Elsevier, vol. 98(4), pages 517-531, February.
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    Cited by:

    1. Maged Mohammed & Ramasamy Srinivasagan & Ali Alzahrani & Nashi K. Alqahtani, 2023. "Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres," Sustainability, MDPI, vol. 15(17), pages 1-24, August.
    2. Luis Vargas Tamayo & Christopher Thron & Jean Louis Kedieng Ebongue Fendji & Shauna-Kay Thomas & Anna Förster, 2020. "Cost-Minimizing System Design for Surveillance of Large, Inaccessible Agricultural Areas Using Drones of Limited Range," Sustainability, MDPI, vol. 12(21), pages 1-25, October.
    3. Bogdan Kulig & Jacek Waga & Andrzej Oleksy & Marcin Rapacz & Marek Kołodziejczyk & Piotr Wężyk & Agnieszka Klimek-Kopyra & Robert Witkowicz & Andrzej Skoczowski & Grażyna Podolska & Wiesław Grygierzec, 2023. "Forecasting of Hypoallergenic Wheat Productivity Based on Unmanned Aerial Vehicles Remote Sensing Approach—Case Study," Agriculture, MDPI, vol. 13(2), pages 1-21, January.
    4. Wei, Jiaxing & Dong, Weichen & Liu, Shaomin & Song, Lisheng & Zhou, Ji & Xu, Ziwei & Wang, Ziwei & Xu, Tongren & He, Xinlei & Sun, Jingwei, 2023. "Mapping super high resolution evapotranspiration in oasis-desert areas using UAV multi-sensor data," Agricultural Water Management, Elsevier, vol. 287(C).
    5. Filgueiras, Roberto & Almeida, Thomé Simpliciano & Mantovani, Everardo Chartuni & Dias, Santos Henrique Brant & Fernandes-Filho, Elpídio Inácio & da Cunha, Fernando França & Venancio, Luan Peroni, 2020. "Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data," Agricultural Water Management, Elsevier, vol. 241(C).

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