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Optimized spectral index models for accurately retrieving soil moisture (SM) of winter wheat under water stress

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  • Ren, Shoujia
  • Guo, Bin
  • Wang, Zhijun
  • Wang, Juan
  • Fang, Quanxiao
  • Wang, Jianlin

Abstract

Soil moisture (SM) is an important indicator of the photosynthetic rate and growth status of crops. A few related parameters, such as the red-edge parameters and spectral indices, have been adopted for retrieving the SM of winter wheat. To further study their abilities to detect the SM, field-scale water stress experiments on winter wheat were conducted during the 2018/19 growing season. The spectral ratio index in the near-infrared (NIR) shoulder region (NSRI) (700–1100 nm) was selected by comparing the correlations between the SM and the red edge parameters and spectral indices, and it was optimized using the partial least squares regression (PLSR) method. To assess the performance of the sensitive wavebands of the NSRI in retrieving the SM, three types of spectral index models were established using multiple linear regression (MLR) for the winter wheat from the jointing to the ripening stage. The results indicate that the red-edge parameters are more sensitive to the spectral variation during the jointing and flowering stages. The sensitivity decreased with increasing water stress. The red-edge area (SDr) of winter wheat irrigated in the flowering stage (D1 treatment) and irrigated in the jointing stage (D2 treatment) decreased by 20–30%, respectively. In general, all of the parameters and indices were correlated with the surface SM (0–40 cm depth), especially for the NSRI, with a significant coefficient of determination (R2) of 0.52 in the 10–20 cm depth interval (P < 0.01). Moreover, all of the spectral index models based on the optimized NSRI have good capabilities for retrieving the SM in the jointing stage. The model for one derivative of the logarithm of the NSRI (logarithmic NSRI)' performed best, with R2 and root mean square error (RMSE) values of 0.81–0.92 and 0.17–0.89%, respectively. Finally, the (logarithmic NSRI)' model was used to retrieve the SM in the flowering–ripening stage (R2 =0.85). Overall, the optimized spectral index models can accurately and quickly retrieve the SM and can assist in predicting the effect of drought on the crop yield in the future.

Suggested Citation

  • Ren, Shoujia & Guo, Bin & Wang, Zhijun & Wang, Juan & Fang, Quanxiao & Wang, Jianlin, 2022. "Optimized spectral index models for accurately retrieving soil moisture (SM) of winter wheat under water stress," Agricultural Water Management, Elsevier, vol. 261(C).
    • Handle: RePEc:eee:agiwat:v:261:y:2022:i:c:s0378377421006107
    DOI: 10.1016/j.agwat.2021.107333
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    References listed on IDEAS

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    1. Peng, Zhigong & Lin, Shaozhe & Zhang, Baozhong & Wei, Zheng & Liu, Lu & Han, Nana & Cai, Jiabing & Chen, He, 2020. "Winter Wheat Canopy Water Content Monitoring Based on Spectral Transforms and “Three-edge” Parameters," Agricultural Water Management, Elsevier, vol. 240(C).
    2. Xindong Wei & Ning Wang & Pingping Luo & Jie Yang & Jian Zhang & Kangli Lin, 2021. "Spatiotemporal Assessment of Land Marketization and Its Driving Forces for Sustainable Urban–Rural Development in Shaanxi Province in China," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    3. Hyonho Chun & Sündüz Keleş, 2010. "Sparse partial least squares regression for simultaneous dimension reduction and variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 3-25, January.
    4. Bai, Shanshan & Kang, Yaohu & Wan, Shuqin, 2020. "Drip fertigation regimes for winter wheat in the North China Plain," Agricultural Water Management, Elsevier, vol. 228(C).
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    1. Deng, Juntao & Pan, Shijia & Zhou, Mingu & Gao, Wen & Yan, Yuncai & Niu, Zijie & Han, Wenting, 2023. "Optimum sampling window size and vegetation index selection for low-altitude multispectral estimation of root soil moisture content for Xuxiang Kiwifruit," Agricultural Water Management, Elsevier, vol. 282(C).
    2. Du, Ruiqi & Xiang, Youzhen & Zhang, Fucang & Chen, Junying & Shi, Hongzhao & Liu, Hao & Yang, Xiaofei & Yang, Ning & Yang, Xizhen & Wang, Tianyang & Wu, Yuxiao, 2024. "Combing transfer learning with the OPtical TRApezoid Model (OPTRAM) to diagnosis small-scale field soil moisture from hyperspectral data," Agricultural Water Management, Elsevier, vol. 298(C).

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