A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation

Abstract

[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 $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.

Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review

“…Larger time in acquiring the satellite imagery and ET estimation can make the method impractical in operational applications. Gap filling procedures [29,119] and coupling models [120] have shown some promises to resolve this issue.…”

“…Therefore, evapotranspiration, which is a combined process of evaporation and transpiration, is a crucial factor in the hydrological cycle. ET is the largest outgoing water flux from the Earth's surface; accurate quantifying ET is critical to developing a greater understanding of a range of hydrological, climatic, and ecosystem processes, and beneficial in numerous applications, e.g., water resources management, drought monitoring, improvement of hydrological modeling, weather forecasts, and vulnerability of forest to fire [29,30]. The regional evapotranspiration is more than half of the total precipitation and the amount of evapotranspiration is almost equal to the precipitation in the semi-arid region [31].…”

Comparison of satellite-based evapotranspiration estimates over the Tibetan Plateau

“…HOLAPS is actually a frame-work that can provide surface energy and water fluxes at sub-hourly timescales and spatial resolutions at the kilometre scale. It is also worth noting that very high spatial resolution (on the order of 10 m) ET product at regional scale can be provided by ALEXI/DisALEXI based on thermal observations from polar and geostationary orbiting satellites (Anderson et al, 2011(Anderson et al, , 2007. Although these global ET products have been applied to many applications such as multidecadal trend analysis (Y.…”

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

“…Due to their very high spatial resolution, the thermal band data of the Landsat series, such as the Landsat 5 Thematic Mapper (TM) and the Landsat 7 Enhanced Thematic Mapper (ETM), have been widely applied for LST retrieval for such studies as surface energy budget estimation [3,4], surface moisture and evapotranspiration monitoring [5,6], urban heat island monitoring [7,8] and environmental biogeochemistry process simulation [9], requiring LST as a basic input.…”