Precision modelling of leaf area index for enhanced surface temperature partitioning and improved evapotranspiration estimation

Abstract

Remote Sensing-based two-source model is widely used to estimate crop evapotranspiration (ET), involving one key step of partitioning land surface temperature (LST) into canopy and soil temperatures (Tc and Tₛ). Leaf area index (LAI) plays a significant part in available energy allocation during this process. However, the asymptotic saturation problem makes the mismatch between vegetation index and LAI. In this study, two-stage LAI models were developed through the red-edge chlorophyll index (CIᵣₑd₋ₑdgₑ) considering the hysteresis between them. Considering the distinct characteristics, modeling LAI by one-degree linear equations for sunflower (C3), linear and exponential functions for maize (C4) were presented in the distinguished grow-up and senescence periods. The two-source energy balance (TSEB) and hybrid dual-source scheme and trapezoid framework-based evapotranspiration (HTEM) models were selected to estimate Tc, Tₛ, ET, and its components contrastively. The established LAI models and other modified parameters were then integrated into the two models to improve the estimation of Tc, Tₛ, and ET (named the R-TSEB and R-HTEM models, respectively). Results demonstrated that the partitioned Tc & Tₛ became closer to the measurements after utilizing the presented LAI models. For daily ET, the R-TSEB and R-HTEM models alleviated the overestimation and underestimation existing in the original two models, respectively. At monthly and seasonal scales compared to the water balance results (ETwb), the ET of R-TSEB model had significant promotion, including the determination coefficient (R²), mean relative error (RE), root mean square error (RMSE), and model agreement index (d) with values of 0.87, 6.54%, 11.65 mm, and 0.95, from the according values of 0.80, 12.85%, 17.60 mm, and 0.90 for the TSEB model, respectively. The estimated ET by the R-HTEM model was more consistent with ETwb than the HTEM model. These results indicate that the established LAI models can enhance ET estimation and further advance water cycle understanding.

Plant phenotyping relevance

作物のLAIという植物形質を赤縁クロロフィル指数から推定するモデルを開発し、複数作物・生育段階で検証している。ET推定への応用も含むが、LAI取得・推定手法が中心的な技術貢献である。

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