Accurate evapotranspiration (ET) estimation is crucial for understanding ecosystem dynamics and managing water resources. Existing methodologies, including traditional techniques like the Penman–Monteith model, remote sensing approaches utilizing Solar-Induced Fluorescence (SIF), and machine learning algorithms, have demonstrated varying levels of effectiveness in ET estimation. However, these methods often face significant challenges, such as reliance on empirical coefficients, inadequate representation of canopy dynamics, and limitations due to cloud cover and sensor constraints. These issues can lead to inaccuracies in capturing ET’s spatial and temporal variability, highlighting the need for improved estimation techniques. This study introduces a novel approach to enhance ET estimation by integrating SIF partitioning with Photosynthetically Active Radiation (PAR) and leaf area index (LAI) data, utilizing the TL-LUE model (Two-Leaf Light Use Efficiency). Partitioning SIF data into sunlit and shaded components allows for a more detailed representation of the canopy’s functional dynamics, significantly improving ET modelling. Our analysis reveals significant advancements in ET modelling through SIF partitioning. At Xiaotangshan Station, the correlation between modelled ET and SIFsu is 0.71, while the correlation between modelled ET and SIFsh is 0.65. The overall correlation (R2) between the modelled ET and the combined SIF partitioning (SIF(P)) is 0.69, indicating a strong positive relationship at Xiaotangshan Station. The correlations between SIFsh and SIFsu with modelled ET show notable patterns, with R2 values of 0.89 and 0.88 at Heihe Daman, respectively. These findings highlight the effectiveness of SIF partitioning in capturing canopy dynamics and its impact on ET estimation. Comparing modelled ET with observed ET and the Penman–Monteith model (PM model) demonstrates substantial improvements. R2 values for modelled ET against observed ET were 0.68, 0.76, and 0.88 across HuaiLai, Shangqiu, and Yunxiao Stations. Modelled ET correlations to the PM model were 0.75, 0.73, and 0.90, respectively, at three stations. These results underscore the model’s capability to enhance ET estimations by integrating physiological and remote sensing data. This innovative SIF-partitioning approach offers a more nuanced perspective on canopy photosynthesis, providing a more accurate and comprehensive method for understanding and managing ecosystem water dynamics across diverse environments.
