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Estimating maize water stress by standard deviation of canopy temperature in thermal imagery

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  • Han, Ming
  • Zhang, Huihui
  • DeJonge, Kendall C.
  • Comas, Louise H.
  • Trout, Thomas J.

Abstract

A new crop water stress indicator, standard deviation of canopy temperature within a thermal image (CTSD), was developed to monitor crop water status. In this study, thermal imagery was taken from maize (Zea mays L.) under various levels of deficit irrigation at different crop growing stages. The Expectation-Maximization algorithm was used to estimate the canopy temperature distribution from thermal imagery under a range of crop coverage and water stress conditions. Soil water deficit (SWD), leaf water potential (ψ), stomatal conductance, and other crop water stress indices were used to evaluate the CTSD. We found that the temperature differences between sunlit and shaded parts of the canopy would increase with larger canopy resistance in the sunlit part of the crop canopy. The CTSD well described impact of irrigation events (timing and depth) on crop water stress. All water stress measurements showed statistically significant relationship with CTSD. Although CTSD is not sensitive to small changes in water stress, the result suggests that the canopy temperature standard deviation could be used as a water stress indicator. This index has strong application potential because it only relies on the canopy temperature itself, and is easy to calculate. Moreover, it may also be applied to high resolution thermal imagery from other remote sensing platforms, such as unmanned aerial vehicles.

Suggested Citation

  • Han, Ming & Zhang, Huihui & DeJonge, Kendall C. & Comas, Louise H. & Trout, Thomas J., 2016. "Estimating maize water stress by standard deviation of canopy temperature in thermal imagery," Agricultural Water Management, Elsevier, vol. 177(C), pages 400-409.
    • Handle: RePEc:eee:agiwat:v:177:y:2016:i:c:p:400-409
    DOI: 10.1016/j.agwat.2016.08.031
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    References listed on IDEAS

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    Cited by:

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    3. Zhang, Liyuan & Zhang, Huihui & Han, Wenting & Niu, Yaxiao & Chávez, José L. & Ma, Weitong, 2022. "Effects of image spatial resolution and statistical scale on water stress estimation performance of MGDEXG: A new crop water stress indicator derived from RGB images," Agricultural Water Management, Elsevier, vol. 264(C).
    4. de Almeida, Ailson Maciel & Coelho, Rubens Duarte & da Silva Barros, Timóteo Herculino & de Oliveira Costa, Jéfferson & Quiloango-Chimarro, Carlos Alberto & Moreno-Pizani, Maria Alejandra & Farias-Ram, 2022. "Water productivity and canopy thermal response of pearl millet subjected to different irrigation levels," Agricultural Water Management, Elsevier, vol. 272(C).
    5. 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.
    6. 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).
    7. 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).
    8. Zhang, Liyuan & Zhang, Huihui & Han, Wenting & Niu, Yaxiao & Chávez, José L. & Ma, Weitong, 2021. "The mean value of gaussian distribution of excess green index: A new crop water stress indicator," Agricultural Water Management, Elsevier, vol. 251(C).
    9. Zhang, Yu & Han, Wenting & Zhang, Huihui & Niu, Xiaotao & Shao, Guomin, 2023. "Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach," Agricultural Water Management, Elsevier, vol. 275(C).
    10. Rahul Raj & Jeffrey P. Walker & Adinarayana Jagarlapudi, 2023. "Maize On-Farm Stressed Area Identification Using Airborne RGB Images Derived Leaf Area Index and Canopy Height," Agriculture, MDPI, vol. 13(7), pages 1-14, June.
    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).

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