A novel TIR-derived three-source energy balance model for estimating daily latent heat flux in mainland China using an all-weather land surface temperature product

涡度相关法 环境科学 蒸腾作用 潜热 能量平衡 拦截 天蓬 大气科学 叶面积指数 显热 焊剂(冶金) 蒸发 生物气象学 水平衡 水文学(农业) 气象学 地理 生态系统 生态学 材料科学 地质学 光合作用 植物 岩土工程 生物 考古 冶金
作者
Junming Yang,Yunjun Yao,Changliang Shao,Yufu Li,Joshua B. Fisher,Jie Cheng,Jiquan Chen,Kun Jia,Xiaotong Zhang,Ke Shang,Ruiyang Yu,Xiaozheng Guo,Zijing Xie,Lu Liu,Jing Ning,Lilin Zhang
出处
期刊:Agricultural and Forest Meteorology [Elsevier BV]
卷期号:323: 109066-109066 被引量:13
标识
DOI:10.1016/j.agrformet.2022.109066
摘要

A reliable thermal-derived method for spatially and temporally continuous latent heat flux (LE) estimates is vital for agricultural water resource management. In this study, we proposed a novel three-source LE model (TSLEM) derived by all-weather thermal infrared (TIR) land surface temperature (LST) to generate all-sky daily LE in mainland China. In this approach, LE was partitioned into the energy fluxes from canopy transpiration, soil evaporation, and interception water evaporation, respectively. Importantly, a new strategy was used to decompose radiational temperature into soil temperature (Ts), canopy temperature (Tc) and interception water temperature (Ti). Then soil evaporation was estimated by Penman-Monteith (PM) equation that parameterizes a soil resistance as a function of the normalized difference temperature index (NDTI) derived from Ts. A simplified MOD16 algorithm framework was used to estimate canopy transpiration and a Priestley-Taylor (PT) model to estimate interception evaporation. The proposed method was validated using 26 eddy covariance (EC) tower sites in mainland China across various vegetation types and applied to generate spatial continuous daily LE in mainland China. The results show that TSLEM accurately yielded daily LE with an average coefficient of determination (R2) of 0.53 (p<0.01) and root-mean-square-error (RMSE) of 27.37 W/m2, indicating TSLEM is a promising method for generating daily LE using all-weather LST at the regional scale.

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