Remote sensing of surface energy fluxes at 10¹‐m pixel resolutions

Abstract

[1] Many applications exist within the fields of agriculture, forestry, land management, and hydrologic assessment for routine estimation of surface energy fluxes, particularly evapotranspiration (ET), at spatial resolutions of the order of 10 1 m. A new two-step approach (called the disaggregated atmosphere land exchange inverse model, or DisALEXI) has been developed to combine low-and high-resolution remote sensing data to estimate ET on the 10 1 -10 2 m scale without requiring any local observations. The first step uses surface brightness-temperature-change measurements made over a 4-hour morning interval from the GOES satellite to estimate average surface fluxes on the scale of about 5 km with an algorithm known as ALEXI. The second step disaggregates the GOES 5-km surface flux estimates by using high-spatial-resolution images of vegetation index and surface temperature, such as from ASTER, Landsat, MODIS, or aircraft, to produce high-spatial-resolution maps of surface fluxes. Using data from the Southern Great Plains field experiment of 1997, the root-mean-square difference between remote estimates of surface fluxes and ground-based measurements is about 40 W m À2 , comparable to uncertainties associated with micrometeorological surface flux measurement techniques. The DisALEXI approach was useful for estimating field-scale, surface energy fluxes in a heterogeneous area of central Oklahoma without using any local observations, thus providing a means for scaling kilometer-scale flux estimates down to a surface flux-tower footprint. Although the DisALEXI approach is promising for general applicability, further tests with varying surface conditions are necessary to establish greater confidence.

Estimating evaporation with thermal UAV data and two-source energy balance models

“…Since the 1980s, estimates of evaporation have been obtained through remotely sensed LST, and advanced land surface heat flux models accounting for vegetation, soil, and atmospheric conditions (Anderson et al, 1997;Kalma et al, 2008), and a large number of heat flux models exist with significant variations in physical system conceptualisation and input requirements (Boulet et al, 2012;Kustas and Norman, 1996;Stisen et al, 2008). Norman et al (1995) applied the two-source energy balance model (TSEB) (Shuttleworth and Wallace, 1985) to remotely sensed data and this modelling scheme has proven to estimate reliable surface heat fluxes over cropland, rangeland, and forest at various spatial scales (Anderson et al, 2004;Norman et al, 2003). The TSEB modelling scheme generates robust estimates of surface heat fluxes despite being a simple solution scheme demanding relatively few input data.…”

ET mapping for agricultural water management: present status and challenges

“…Using the TSM, Kustas et al (2003) and Norman et al (2003) developed a two-step approach called the Disaggregated Atmosphere Land EXchange Inverse model (DisALEXI) to combine low-and high-resolution remote sensing data to estimate ET on the 10-100 m scale without requiring local observations. The first step involves deriving surface fluxes from low spatial resolution but frequently available remote sensing data using a coupled TSM-ABL model known as ALEXI.…”

Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

“…One-source REB models include Surface Energy Balance Algorithm for Land (SEBAL) [15], Surface Energy Balance System (SEBS) [16], Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) [17], and Simplified Surface Energy Balance (SSEB) [18,19]. Two-source models, such as the Two-Source Model (TSM) [20,21] and the Disaggregated Atmosphere-Land Exchange Inverse (DisALEXI) [22][23][24][25], integrate the biophysical processes and remote sensing data to estimate the plant transpiration and evaporation from non-vegetated surfaces separately.…”