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.

中文翻译：

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叶面积指数的精确建模，以增强表面温度分区和改进蒸散量估计


基于遥感的双源模型被广泛用于估算作物蒸散量（ET），其中一个关键步骤是将地表温度（LST）划分为冠层温度和土壤温度（Tc和Ts）。叶面积指数（LAI）在此过程中的可用能量分配中发挥着重要作用。然而，渐近饱和问题使得植被指数与LAI不匹配。在本研究中，考虑到它们之间的滞后性，通过红边叶绿素指数（CIred-edge）开发了两阶段 LAI 模型。考虑到不同的特征，对向日葵（C3）采用一次线性方程，对玉米（C4）采用线性和指数函数对LAI进行建模，并提出了不同的成长期和衰老期的LAI模型。选择双源能量平衡（TSEB）和混合双源方案以及基于梯形框架的蒸散发（HTEM）模型来对比估计Tc、Ts、ET及其组成部分。然后将建立的 LAI 模型和其他修改参数集成到两个模型中，以改进 Tc、Ts 和 ET 的估计（分别称为 R-TSEB 和 R-HTEM 模型）。结果表明，利用所提出的 LAI 模型后，分区的 Tc 和 T 变得更接近测量结果。对于日常ET，R-TSEB和R-HTEM模型分别缓解了原始两个模型中存在的高估和低估问题。在月度和季节尺度上，与水平衡结果（ETwb）相比，R-TSEB模型的ET有显着的提升，包括决定系数（R2）、平均相对误差（RE）、均方根误差（RMSE）和模型一致性指数 (d) 的值为 0.87、6.54%、11.65 mm 和 0.95，相应值为 0.80、12.85%、17。TSEB 型号分别为 60 mm 和 0.90。 R-HTEM 模型估计的 ET 比 HTEM 模型与 ETwb 更一致。这些结果表明，建立的 LAI 模型可以增强 ET 估算并进一步促进水循环的理解。