A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation
A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation
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DOI：
10.1029/2006jd007506
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发表时间：
2007-05-24
影响因子：
4.4
通讯作者：
Kustas, William P.
中科院分区：
文献类型：
--
作者：
Anderson, Martha C.;Norman, John M.;Kustas, William P.
[1]由于蒸发对地表温度的影响，热遥感数据提供了关于地表水分状况的有价值的信息。大气-土地交换逆(AXI)模式使用从地球静止卫星平台测量的早晨地表温度上升来推算美国大陆上空5-10公里分辨率的地表能量和水通量。描述了对Alexi模型的最新改进。像大多数热遥感模型一样，Alexi被限制在晴朗的天空条件下工作，当卫星传感器可以看到表面时，通常会在模型输出记录中留下很大的差距。给出了一种估算多云时段通量的算法，定义了一个水分应力函数，该函数将晴天模型获得的潜在蒸散量与土壤表层和根区有效水含量的估计值联系起来。在阴天，这个应力函数被用来预测土壤和冠层通量。在2002年的土壤水分实验(SMEX02)中，使用在爱荷华州中部用密集通量塔网络获得的具有代表性的分水岭尺度的通量测量对该方法进行了评估。缺口填充算法以合理的精度再现观测到的通量，在每小时的时间尺度上产生类似于ET的20%的误差，在每天的时间步长产生类似于15%的误差。此外，模拟的土壤湿度对主要降水事件表现出合理的响应。该算法具有较强的通用性，可以方便地应用于其他热能平衡模型。通过填补空白，Alexi模型可以近乎实时地估计美国模型域中每个网格单元的每小时地表通量。一篇配对论文介绍了2002-2004年由Alexi得出的蒸散量和水汽应力场的气候学评估。
[1] Due to the influence of evaporation on land-surface temperature, thermal remote sensing data provide valuable information regarding the surface moisture status. The Atmosphere-Land Exchange Inverse (ALEXI) model uses the morning surface temperature rise, as measured from a geostationary satellite platform, to deduce surface energy and water fluxes at 5 - 10 km resolution over the continental United States. Recent improvements to the ALEXI model are described. Like most thermal remote sensing models, ALEXI is constrained to work under clear-sky conditions when the surface is visible to the satellite sensor, often leaving large gaps in the model output record. An algorithm for estimating fluxes during cloudy intervals is presented, defining a moisture stress function relating the fraction of potential evapotranspiration obtained from the model on clear days to estimates of the available water fraction in the soil surface layer and root zone. On cloudy days, this stress function is inverted to predict the soil and canopy fluxes. The method is evaluated using flux measurements representative at the watershed scale acquired in central Iowa with a dense flux tower network during the Soil Moisture Experiment of 2002 (SMEX02). The gap-filling algorithm reproduces observed fluxes with reasonable accuracy, yielding similar to 20% errors in ET at the hourly timescale, and 15% errors at daily timesteps. In addition, modeled soil moisture shows reasonable response to major precipitation events. This algorithm is generic enough that it can easily be applied to other thermal energy balance models. With gap-filling, the ALEXI model can estimate hourly surface fluxes at every grid cell in the U. S. modeling domain in near real-time. A companion paper presents a climatological evaluation of ALEXI-derived evapotranspiration and moisture stress fields for the years 2002 - 2004.