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Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes

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  • Elsayed, Salah
  • Elhoweity, Mohamed
  • Ibrahim, Hazem H.
  • Dewir, Yaser Hassan
  • Migdadi, Hussein M.
  • Schmidhalter, Urs

Abstract

The water demand for agricultural purposes is steadily increasing. The use of contactless sensing techniques, such as passive reflectance sensors and thermal imaging cameras, is therefore becoming imperative and will be one of the major adaptation strategies to control the irrigation schedule under arid and semi-arid conditions. In this study, the performance of hyperspectral passive reflectance sensing and infrared thermal imaging was tested to assess their relationship with the water status and grain yield (GY) of wheat cultivars via simple linear regression and partial least square regression (PLSR) analyses. The models included data of the (i) normalized relative canopy temperature (NRCT); (ii) PLSR based on selected spectral indices; (iii) data fusion model of PLSR based on selected spectral indices and the NRCT; and (iv) data fusion model of PLSR based on selected spectral indices, NRCT, relative water content (RWC), and canopy water content (CWC). The experimental treatments involved two wheat cultivars (Gmiza 11 and Sods 1) and three water regimes (irrigated with 100%, 75%, and 50% of estimated crop evapotranspiration). The results show that the NRCT was closely and significantly associated with RWC, CWC, and GY, with R2=0.84, 0.87 and 0.81, respectively. The data fusion model of PLSR based on selected spectral indices, NRCT, RWC, and CWC improved the yield prediction under three irrigation regimes (R2=0.97, slope=0.99, root-mean-square error=26.48g/m2). In conclusion, improvements can be made in the yield prediction when traits that are physiologically related in different ways to the yield are combined with non-destructive data.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:agiwat:v:189:y:2017:i:c:p:98-110
    DOI: 10.1016/j.agwat.2017.05.001
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    References listed on IDEAS

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    1. El-Shikha, D.M. & Waller, P. & Hunsaker, D. & Clarke, T. & Barnes, E., 2007. "Ground-based remote sensing for assessing water and nitrogen status of broccoli," Agricultural Water Management, Elsevier, vol. 92(3), pages 183-193, September.
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    1. Adel H. Elmetwalli & Yasser S. A. Mazrou & Andrew N. Tyler & Peter D. Hunter & Osama Elsherbiny & Zaher Mundher Yaseen & Salah Elsayed, 2022. "Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt," Agriculture, MDPI, vol. 12(3), pages 1-21, February.
    2. 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).
    3. El-Hendawy, Salah E. & Al-Suhaibani, Nasser A. & Elsayed, Salah & Hassan, Wael M. & Dewir, Yaser Hassan & Refay, Yahya & Abdella, Kamel A., 2019. "Potential of the existing and novel spectral reflectance indices for estimating the leaf water status and grain yield of spring wheat exposed to different irrigation rates," Agricultural Water Management, Elsevier, vol. 217(C), pages 356-373.
    4. 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).
    5. Melo, Leonardo Leite de & Melo, Verônica Gaspar Martins Leite de & Marques, Patrícia Angélica Alves & Frizzone, Jose Antônio & Coelho, Rubens Duarte & Romero, Roseli Aparecida Francelin & Barros, Timó, 2022. "Deep learning for identification of water deficits in sugarcane based on thermal images," Agricultural Water Management, Elsevier, vol. 272(C).
    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. Fan Ding & Changchun Li & Weiguang Zhai & Shuaipeng Fei & Qian Cheng & Zhen Chen, 2022. "Estimation of Nitrogen Content in Winter Wheat Based on Multi-Source Data Fusion and Machine Learning," Agriculture, MDPI, vol. 12(11), pages 1-16, October.
    8. Mohamed E. Abowaly & Abdel-Aziz A. Belal & Enas E. Abd Elkhalek & Salah Elsayed & Rasha M. Abou Samra & Abdullah S. Alshammari & Farahat S. Moghanm & Kamal H. Shaltout & Saad A. M. Alamri & Ebrahem M., 2021. "Assessment of Soil Pollution Levels in North Nile Delta, by Integrating Contamination Indices, GIS, and Multivariate Modeling," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    9. Cheng, Minghan & Jiao, Xiyun & Liu, Yadong & Shao, Mingchao & Yu, Xun & Bai, Yi & Wang, Zixu & Wang, Siyu & Tuohuti, Nuremanguli & Liu, Shuaibing & Shi, Lei & Yin, Dameng & Huang, Xiao & Nie, Chenwei , 2022. "Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning," Agricultural Water Management, Elsevier, vol. 264(C).
    10. Salah Elsayed & Mohamed Gad & Mohamed Farouk & Ali H. Saleh & Hend Hussein & Adel H. Elmetwalli & Osama Elsherbiny & Farahat S. Moghanm & Moustapha E. Moustapha & Mostafa A. Taher & Ebrahem M. Eid & M, 2021. "Using Optimized Two and Three-Band Spectral Indices and Multivariate Models to Assess Some Water Quality Indicators of Qaroun Lake in Egypt," Sustainability, MDPI, vol. 13(18), pages 1-23, September.
    11. Wenfeng Li & Kun Pan & Wenrong Liu & Weihua Xiao & Shijian Ni & Peng Shi & Xiuyue Chen & Tong Li, 2024. "Monitoring Maize Canopy Chlorophyll Content throughout the Growth Stages Based on UAV MS and RGB Feature Fusion," Agriculture, MDPI, vol. 14(8), pages 1-22, August.

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