Nieto Héctor, Kustas William P, Torres-Rúa Alfonso, Alfieri Joseph G, Gao Feng, Anderson Martha C, White W Alex, Song Lisheng, Del Mar Alsina María, Prueger John H, McKee Mac, Elarab Manal, McKee Lynn G
IRTA, Institute of Agriculture and Food Research and Technology, Lleida, Spain.
Hydrology and Remote Sensing Lab, USDA-Agricultural Research Service, Beltsville, MD, USA.
Irrig Sci. 2019;37(3):389-406. doi: 10.1007/s00271-018-0585-9.
The thermal-based Two-Source Energy Balance (TSEB) model partitions the evapotranspiration (ET) and energy fluxes from vegetation and soil components providing the capability for estimating soil evaporation (E) and canopy transpiration (T). However, it is crucial for ET partitioning to retrieve reliable estimates of canopy and soil temperatures and net radiation, as the latter determines the available energy for water and heat exchange from soil and canopy sources. These two factors become especially relevant in row crops with wide spacing and strongly clumped vegetation such as vineyards and orchards. To better understand these effects, very high spatial resolution remote-sensing data from an unmanned aerial vehicle were collected over vineyards in California, as part of the Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment and used in four different TSEB approaches to estimate the component soil and canopy temperatures, and ET partitioning between soil and canopy. Two approaches rely on the use of composite , and assume initially that the canopy transpires at the Priestley-Taylor potential rate. The other two algorithms are based on the contextual relationship between optical and thermal imagery partition into soil and canopy component temperatures, which are then used to drive the TSEB without requiring a priori assumptions regarding initial canopy transpiration rate. The results showed that a simple contextual algorithm based on the inverse relationship of a vegetation index and to derive soil and canopy temperatures yielded the closest agreement with flux tower measurements. The utility in very high-resolution remote-sensing data for estimating ET and E and T partitioning at the canopy level is also discussed.
基于热的双源能量平衡（TSEB）模型对植被和土壤组分的蒸散量（ET）及能量通量进行划分，从而具备估算土壤蒸发（E）和冠层蒸腾（T）的能力。然而，对于ET划分而言，获取可靠的冠层和土壤温度以及净辐射估算值至关重要，因为后者决定了土壤和冠层源进行水热交换的可用能量。这两个因素在葡萄园和果园等行距宽且植被高度丛生的大田作物中尤为重要。为了更好地理解这些影响，作为葡萄遥感与大气剖面及蒸散实验的一部分，在加利福尼亚州的葡萄园收集了来自无人机的超高空间分辨率遥感数据，并将其用于四种不同的TSEB方法，以估算土壤和冠层温度分量以及土壤和冠层之间的ET划分。两种方法依赖于合成数据的使用，并最初假设冠层以普里斯特利 - 泰勒潜在速率进行蒸腾。另外两种算法基于光学和热成像之间的上下文关系将温度划分为土壤和冠层分量温度，然后用于驱动TSEB，而无需关于初始冠层蒸腾速率的先验假设。结果表明，基于植被指数与温度的反比关系来推导土壤和冠层温度的简单上下文算法与通量塔测量结果最为吻合。还讨论了超高分辨率遥感数据在估算冠层水平的ET、E以及E和T划分方面的实用性。
