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Objective <p> Reliable estimation of radiation use efficiency (RUE) in winter wheat is critical for optimizing crop productivity and quantifying global carbon cycles. The photochemical reflectance index (PRI 570,531) is widely used for RUE prediction, but its simplistic two-band formulation fails to decouple individual pigment contributions and is constrained by spectral saturation, limiting estimation accuracy under heterogeneous field conditions. To address these limitations, this study proposed a modified PRI (mPRI) integrated with multi-angular hyperspectral data to improve RUE estimation</p> Methods <p>Field experiments were conducted over 2020–2023 in Zhengzhou, China, with two winter wheat cultivars grown under contrasting irrigation and nitrogen (N) input levels. Canopy multi-angular hyperspectral reflectance (13 view zenith angles, VZAs) and key agronomic parameters were measured across critical growth stages. The mPRI was developed by incorporating the ratio of chlorophyll a+b (Chl) to carotenoid (Car) content—characterized by a spectral ratio (SR 485,625)—into the traditional PRI, and its performance was evaluated against conventional vegetation indices (e.g., NDVI 810,680) under different VZAs.</p> Results <p>Results showed that, under most VZAs, the mPRI effectively reduced spectral saturation and improved RUE estimation accuracy compared to the original PRI and NDVI, particularly under low-nitrogen or water-limited conditions. Back-scatter angles outperformed forward-scatter angles, with the optimal viewing angle range identified as -20º to +10º. Within this range, the mPRI achieved calibration R² &gt; 0.63 and validation R² &gt; 0.61, with RMSE &lt; 0.48, outperforming all other tested indices. The mPRI derived from MODIS bands also exhibited robust performance (validation R² = 0.52), demonstrating potential for scaling to satellite-based monitoring.</p> Conclusion <p>The proposed mPRI, combined with optimal multi-angular observations, provides a practical and scalable approach for high-precision RUE monitoring in winter wheat under varying water and N conditions. This method enhances our ability to guide precision agronomic management and improve the accuracy of global carbon cycle modeling.</p>

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Hyperspectral multi-angle driven modified photochemical reflectance index (mPRI): A novel approach for accurately estimating radiation use efficiency in winter wheat

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • Haiyan Zhang,
  • Yapeng Wu,
  • Qiwen Chen,
  • Yangyang Wang,
  • Jianzhao Duan,
  • Li He,
  • Geng Ma,
  • Yingxin Xie,
  • Jiandong Hu,
  • Chenyang Wang,
  • Wei Feng

摘要

Objective

Reliable estimation of radiation use efficiency (RUE) in winter wheat is critical for optimizing crop productivity and quantifying global carbon cycles. The photochemical reflectance index (PRI 570,531) is widely used for RUE prediction, but its simplistic two-band formulation fails to decouple individual pigment contributions and is constrained by spectral saturation, limiting estimation accuracy under heterogeneous field conditions. To address these limitations, this study proposed a modified PRI (mPRI) integrated with multi-angular hyperspectral data to improve RUE estimation

Methods

Field experiments were conducted over 2020–2023 in Zhengzhou, China, with two winter wheat cultivars grown under contrasting irrigation and nitrogen (N) input levels. Canopy multi-angular hyperspectral reflectance (13 view zenith angles, VZAs) and key agronomic parameters were measured across critical growth stages. The mPRI was developed by incorporating the ratio of chlorophyll a+b (Chl) to carotenoid (Car) content—characterized by a spectral ratio (SR 485,625)—into the traditional PRI, and its performance was evaluated against conventional vegetation indices (e.g., NDVI 810,680) under different VZAs.

Results

Results showed that, under most VZAs, the mPRI effectively reduced spectral saturation and improved RUE estimation accuracy compared to the original PRI and NDVI, particularly under low-nitrogen or water-limited conditions. Back-scatter angles outperformed forward-scatter angles, with the optimal viewing angle range identified as -20º to +10º. Within this range, the mPRI achieved calibration R² > 0.63 and validation R² > 0.61, with RMSE < 0.48, outperforming all other tested indices. The mPRI derived from MODIS bands also exhibited robust performance (validation R² = 0.52), demonstrating potential for scaling to satellite-based monitoring.

Conclusion

The proposed mPRI, combined with optimal multi-angular observations, provides a practical and scalable approach for high-precision RUE monitoring in winter wheat under varying water and N conditions. This method enhances our ability to guide precision agronomic management and improve the accuracy of global carbon cycle modeling.