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Diagnosis of winter-wheat water stress based on UAV-borne multispectral image texture and vegetation indices

Author

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  • Zhou, Yongcai
  • Lao, Congcong
  • Yang, Yalong
  • Zhang, Zhitao
  • Chen, Haiying
  • Chen, Yinwen
  • Chen, Junying
  • Ning, Jifeng
  • Yang, Ning

Abstract

Timely and accurate detection of crop water stress is vital for precision irrigation. Whether the accuracy of the prevailing diagnosis of crop water stress using vegetation indices (VIs) and spectral reflectance can be improved still remains to be investigated. The crop surface characteristics such as grayscale or color vary under different water stress, so in this study one more variable, image texture, was utilized together to diagnose water stress. For this end, the canopy image of winter wheat in bloom was obtained by unmanned aerial vehicle (UAV) equipped with multispectral sensor, and the effect of soil background was eliminated using vegetation index threshold method. On this basis, Grey level co-occurrence matrix (GLCM) was used to calculate the mean (MEA), variance (VAR), homogeneity (HOM), contrast (CON), dissimilarity (DIS), entropy (ENT), second moment (SEC) and correlation (COR) of the image texture under different spatial resolutions (0.008 m, 0.01 m, 0.02 m, 0.05 m, 0.1 m and 0.2 m). Next, the canopy vegetation indices were obtained by mathematical transformation of canopy reflectance, and then sensitive image texture and vegetation indices by full subset regression method. Finally, Cubist, BPNN (Back Propagation Neural Network) and ELM (Extreme Learning Machine) methods were adopted to build the estimation models of the stomatal conductance (Gs) of winter wheat (between the sensitive image texture and Gs, and between vegetation index and Gs), and the water stress map was plotted based on the optimal Gs estimation model. The result showed: (i) the image texture obtained from the high-resolution multispectral image had a high correlation with Gs, and the image texture (VAR, HOM, CON, DIS, ENT and SEC) at 550 nm had the most significant correlation; (ii) the higher the ground resolution, the higher the correlation between the Gs and the image texture, the vegetation indices, respectively. The image texture with a ground resolution of 0.008 m combined with VIs and Gs had the highest correlation, and combining image texture and vegetation index can significantly improve the estimation accuracy of winter wheat Gs; (iii) Among the three estimation models, the BPNN model constructed by combining the image texture and VIs (MEA, VAR, ENT, DWSI and EXG) had the best estimation performance (Calibration:Rc2 = 0.899, RMSEc = 0.01, MAEc = 0.006; Validation:Rc2 = 0.834, RMSEv =;0.018, MAEv = 0.014), and an accurate estimation could even be achieved at a lower Gs value. Compared with the BPNN model solely based on VIs or image texture, the Rc2 of the BPNN model based on the combined variables increased by 24% and 22.48%, respectively. Therefore, combining UAV multispectral image texture and VIs to estimate Gs provides a feasible and accurate method for water stress diagnosis of winter wheat.

Suggested Citation

  • Zhou, Yongcai & Lao, Congcong & Yang, Yalong & Zhang, Zhitao & Chen, Haiying & Chen, Yinwen & Chen, Junying & Ning, Jifeng & Yang, Ning, 2021. "Diagnosis of winter-wheat water stress based on UAV-borne multispectral image texture and vegetation indices," Agricultural Water Management, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:agiwat:v:256:y:2021:i:c:s0378377421003413
    DOI: 10.1016/j.agwat.2021.107076
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    1. Wang, Chu & Zhu, Kai & Bai, YanYan & Li, ChenYan & Li, Maona & Sun, Yan, 2024. "Response of stomatal conductance to plant water stress in buffalograss seed production: Observation with UAV thermal infrared imagery," Agricultural Water Management, Elsevier, vol. 292(C).
    2. Lili Zhou & Chenwei Nie & Tao Su & Xiaobin Xu & Yang Song & Dameng Yin & Shuaibing Liu & Yadong Liu & Yi Bai & Xiao Jia & Xiuliang Jin, 2023. "Evaluating the Canopy Chlorophyll Density of Maize at the Whole Growth Stage Based on Multi-Scale UAV Image Feature Fusion and Machine Learning Methods," Agriculture, MDPI, vol. 13(4), pages 1-22, April.
    3. Aliloo, Jamileh & Abbasi, Enayat & Karamidehkordi, Esmail & Ghanbari Parmehr, Ebadat & Canavari, Maurizio, 2024. "Dos and Don'ts of using drone technology in the crop fields," Technology in Society, Elsevier, vol. 76(C).
    4. Wang, Jingjing & Lou, Yu & Wang, Wentao & Liu, Suyi & Zhang, Haohui & Hui, Xin & Wang, Yunling & Yan, Haijun & Maes, Wouter H., 2024. "A robust model for diagnosing water stress of winter wheat by combining UAV multispectral and thermal remote sensing," Agricultural Water Management, Elsevier, vol. 291(C).

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