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Improving the performance in crop water deficit diagnosis with canopy temperature spatial distribution information measured by thermal imaging

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  • Luan, Yajun
  • Xu, Junzeng
  • Lv, Yuping
  • Liu, Xiaoyin
  • Wang, Haiyu
  • Liu, Shimeng

Abstract

Infrared thermal imaging cameras are effective tools to monitor the spatial distribution of canopy temperature (Tc), which is the basis of the crop water stress index (CWSI) calculation. This paper presents a new method to improve the CWSI performance in crop water stress diagnosis based on Tc measured by thermal imaging. Cumulative frequency curves of pixel Tc extracted from each thermal image were analysed. Different statistical quantiles of Tc were determined, and the average Tc over different statistics quantiles were used to calculate the CWSI separately. There were large gaps among the CWSI based on Tc over different statistical quantiles. We compared the coefficient of determination (R2) of relationships among the CWSI based on Tc over different statistical quantiles and relative leaf photosynthetic activities. The general sensitive CWSI showed the best correlations with leaf photosynthetic activities, which were calculated based on average values of the top 60%, 50%, 70%, 50% of Tc statistics at different growth stages. The ranges of the CWSI with optimal leaf water use efficiency (between turning-points for downward trends in photosynthesis and transpiration) were 0.556–0.569, 0.481–0.486, 0.571–0.641, and 0.511–0.606 at tillering, panicle initiation to booting, heading to anthesis, and milk to soft dough stages respectively. The corresponding soil moisture levels were consistent with the lower thresholds of the rice under control irrigation. Based on the spatial distribution of canopy temperatures measured by thermal imaging cameras, the general sensitive CWSI, which was calculated by removing low temperatures, had a better performance in crop water stress diagnosis.

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  • Luan, Yajun & Xu, Junzeng & Lv, Yuping & Liu, Xiaoyin & Wang, Haiyu & Liu, Shimeng, 2021. "Improving the performance in crop water deficit diagnosis with canopy temperature spatial distribution information measured by thermal imaging," Agricultural Water Management, Elsevier, vol. 246(C).
    • Handle: RePEc:eee:agiwat:v:246:y:2021:i:c:s0378377420322435
    DOI: 10.1016/j.agwat.2020.106699
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    1. Lebourgeois, V. & Chopart, J.-L. & Bégué, A. & Le Mézo, L., 2010. "Towards using a thermal infrared index combined with water balance modelling to monitor sugarcane irrigation in a tropical environment," Agricultural Water Management, Elsevier, vol. 97(1), pages 75-82, January.
    2. Gontia, N.K. & Tiwari, K.N., 2008. "Development of crop water stress index of wheat crop for scheduling irrigation using infrared thermometry," Agricultural Water Management, Elsevier, vol. 95(10), pages 1144-1152, October.
    3. García-Tejero, I.F. & Rubio, A.E. & Viñuela, I. & Hernández, A & Gutiérrez-Gordillo, S & Rodríguez-Pleguezuelo, C.R. & Durán-Zuazo, V.H., 2018. "Thermal imaging at plant level to assess the crop-water status in almond trees (cv. Guara) under deficit irrigation strategies," Agricultural Water Management, Elsevier, vol. 208(C), pages 176-186.
    4. Gao, Yang & Yang, Linlin & Shen, Xiaojun & Li, Xinqiang & Sun, Jingsheng & Duan, Aiwang & Wu, Laosheng, 2014. "Winter wheat with subsurface drip irrigation (SDI): Crop coefficients, water-use estimates, and effects of SDI on grain yield and water use efficiency," Agricultural Water Management, Elsevier, vol. 146(C), pages 1-10.
    5. Khorsandi, Azar & Hemmat, Abbas & Mireei, Seyed Ahmad & Amirfattahi, Rasoul & Ehsanzadeh, Parviz, 2018. "Plant temperature-based indices using infrared thermography for detecting water status in sesame under greenhouse conditions," Agricultural Water Management, Elsevier, vol. 204(C), pages 222-233.
    6. Pereira, Luis Santos & Oweis, Theib & Zairi, Abdelaziz, 2002. "Irrigation management under water scarcity," Agricultural Water Management, Elsevier, vol. 57(3), pages 175-206, December.
    7. Agam, N. & Cohen, Y. & Berni, J.A.J. & Alchanatis, V. & Kool, D. & Dag, A. & Yermiyahu, U. & Ben-Gal, A., 2013. "An insight to the performance of crop water stress index for olive trees," Agricultural Water Management, Elsevier, vol. 118(C), pages 79-86.
    8. Belder, P. & Bouman, B. A. M. & Cabangon, R. & Guoan, Lu & Quilang, E. J. P. & Yuanhua, Li & Spiertz, J. H. J. & Tuong, T. P., 2004. "Effect of water-saving irrigation on rice yield and water use in typical lowland conditions in Asia," Agricultural Water Management, Elsevier, vol. 65(3), pages 193-210, March.
    9. Wang, D. & Gartung, J., 2010. "Infrared canopy temperature of early-ripening peach trees under postharvest deficit irrigation," Agricultural Water Management, Elsevier, vol. 97(11), pages 1787-1794, November.
    10. García-Tejero, I.F. & Hernández, A. & Padilla-Díaz, C.M. & Diaz-Espejo, A. & Fernández, J.E, 2017. "Assessing plant water status in a hedgerow olive orchard from thermography at plant level," Agricultural Water Management, Elsevier, vol. 188(C), pages 50-60.
    11. Xu, Junzeng & Peng, Shizhang & Yang, Shihong & Wang, Weiguang, 2012. "Ammonia volatilization losses from a rice paddy with different irrigation and nitrogen managements," Agricultural Water Management, Elsevier, vol. 104(C), pages 184-192.
    12. Xu, Junzeng & Bai, Wenhuan & Li, Yawei & Wang, Haiyu & Yang, Shihong & Wei, Zheng, 2019. "Modeling rice development and field water balance using AquaCrop model under drying-wetting cycle condition in eastern China," Agricultural Water Management, Elsevier, vol. 213(C), pages 289-297.
    13. DeJonge, Kendall C. & Taghvaeian, Saleh & Trout, Thomas J. & Comas, Louise H., 2015. "Comparison of canopy temperature-based water stress indices for maize," Agricultural Water Management, Elsevier, vol. 156(C), pages 51-62.
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