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<p>Accurately estimating reference crop evapotranspiration (ET₀) is essential for assessing crop water requirements and optimizing regional water resource management. Traditional ET₀ estimation models are limited by incomplete meteorological data, while sun-induced chlorophyll fluorescence (SIF) provides new opportunities for ET₀ estimation. However, existing models neglect the influence of environmental variables on the relationship between ET<sub>0</sub> and SIF, resulting in an inability to accurately capture the dynamic variations of ET<sub>0</sub>. To overcome this limitation, we incorporated the basal crop coefficient (Kcb) into the original ET<sub>0</sub>_SIF model to enhance its constraints, developing a hybrid SIF-based model (RET<sub>0</sub>_SIF). By integrating this model with satellite observations and reanalysis data, we produced high-resolution spatiotemporal ET<sub>0</sub> estimates (RET<sub>0</sub>_SIFd). The research findings demonstrate that: (1) the improved RET<sub>0</sub>_SIF model significantly enhances ET<sub>0</sub> estimation accuracy, effectively capturing seasonal ET<sub>0</sub> variations across 22 monitoring stations; (2) RET<sub>0</sub>_SIF outperforms conventional empirical models, exhibiting a minimal multi-year mean bias (0.59&#xa0;mm/8-day) compared to the Penman-Monteith (ET<sub>0PM</sub>) at all 22 stations; (3) RET<sub>0</sub>_SIFd reveals a spatial pattern of gradual decrease from west to east in the study area, along with an increasing trend over time (2.56&#xa0;mm/year). This study provides a novel methodology for precise ET<sub>0</sub> estimation in arid and semi-arid regions.</p>

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Improving reference crop evapotranspiration estimation using Solar-Induced chlorophyll fluorescence

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月3日

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  • Renjun Wang,
  • Shuaiqiang Zhang,
  • Jianghua Zheng

摘要

Accurately estimating reference crop evapotranspiration (ET₀) is essential for assessing crop water requirements and optimizing regional water resource management. Traditional ET₀ estimation models are limited by incomplete meteorological data, while sun-induced chlorophyll fluorescence (SIF) provides new opportunities for ET₀ estimation. However, existing models neglect the influence of environmental variables on the relationship between ET0 and SIF, resulting in an inability to accurately capture the dynamic variations of ET0. To overcome this limitation, we incorporated the basal crop coefficient (Kcb) into the original ET0_SIF model to enhance its constraints, developing a hybrid SIF-based model (RET0_SIF). By integrating this model with satellite observations and reanalysis data, we produced high-resolution spatiotemporal ET0 estimates (RET0_SIFd). The research findings demonstrate that: (1) the improved RET0_SIF model significantly enhances ET0 estimation accuracy, effectively capturing seasonal ET0 variations across 22 monitoring stations; (2) RET0_SIF outperforms conventional empirical models, exhibiting a minimal multi-year mean bias (0.59 mm/8-day) compared to the Penman-Monteith (ET0PM) at all 22 stations; (3) RET0_SIFd reveals a spatial pattern of gradual decrease from west to east in the study area, along with an increasing trend over time (2.56 mm/year). This study provides a novel methodology for precise ET0 estimation in arid and semi-arid regions.