Abstract
Estimation of actual evapotranspiration (ET) for the Middle Rio Grande valley in central New Mexico via the METRIC surface energy balance model using MODIS and Landsat imagery is described. MODIS images are a useful resource for estimating ET at large scales when high spatial resolution is not required. One advantage of MODIS satellites is that images having a view angle < ~15° are potentially available about every four to five days. The main challenge of applying METRIC using MODIS is the selection of the two calibration conditions due to the low spatial resolution of MODIS. A calibration procedure specific to MODIS is described that utilizes the higher vegetation index areas of the image along with a consistently low ET location to develop the estimation function for sensible heat flux. This paper compares ET images for the Rio Grande region as produced by both MODIS and by Landsat. Application of METRIC energy balance processes along the Middle Rio Grande using MODIS imagery indicates that one can successfully produce monthly and annual ET estimates that are similar in value to those obtained using Landsat imagery if a cross-calibration scheme is considered. However, spatial fidelity is degraded.
Highlights
Over the past several decades, there has been a substantial effort to retrieve actual evapotranspiration (ET) over large areas from primarily remotely sensed data
This outcome suggests that the selection of the cold pixel condition for an image and magnitude of ETrFcold is not critical so long as the ETrFcold, normalized difference vegetation index (NDVI), Ts and albedo all correspond to the same equilibrium energy balance condition
ET to develop a calibration scheme to use with MODIS imagery and to compare METRIC-MODIS
Summary
Over the past several decades, there has been a substantial effort to retrieve actual evapotranspiration (ET) over large areas from primarily remotely sensed data. There are two primary approaches: (a) scaling ET based on a vegetation index (VI), with maximum rates of ET based on ground-based weather data or on ground-based ET measurements and (b) using thermal signals to drive a surface energy balance or to more scale maximum ET. Analytical approaches estimate ET by combining remotely sensed spectral data, thermal imagery, and ground-based meteorological inputs to evaluate net radiation (Rn), sensible heat (H) and soil heat flux (G) components of the surface energy balance to obtain latent heat flux (LE) as the residual from the energy balance. The third class uses a combination of models that simulate both soil water and surface energy balance by solving numerical equations for heat and mass transfer, combining both remotely sensed data and ground-based information with daily or shorter calculation time steps [6]
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