Improving reference crop evapotranspiration estimation using Solar-Induced chlorophyll fluorescence

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 (ET_0PM) 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.