Abstract:Surface Energy Balance Algorithms for Land (SEBAL) and Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) are satellite-based image-processing models that calculate evapotranspiration (ET) as a residual of a surface energy balance. Both models are calibrated using inverse modelling at extreme conditions approach to develop imagespecific estimations of the sensible heat flux (H) component of the surface energy balance and to effectively remove systematic biases in net radiation, soil heat flux, radiometric temperature and aerodynamic estimates. SEBAL and METRIC express the near-surface temperature gradient as an indexed function of radiometric surface temperature, eliminating the need for absolutely accurate surface temperature and the need for air temperature measurements. Slope and aspect functions and temperature lapsing are used in METRIC applications in mountainous terrains. SEBAL and METRIC algorithms are designed for relatively routine application by trained professionals familiar with energy balance, aerodynamics and basic radiation physics. The primary inputs for the models are short-wave and long-wave (thermal) images from satellite (e.g. Landsat and MODIS), a digital elevation model and ground-based weather data measured within or near the area of interest. ET 'maps' (i.e. images) developed using Landsat images provide means to quantify ET on a field basis in terms of both rate and spatial distribution. METRIC takes advantage of calibration using weather-based reference ET so that both calibration and extrapolation of instantaneous ET to 24-h and longer periods compensate for regional advection effects where ET can exceed daily net radiation. SEBAL and METRIC have advantages over conventional methods of estimating ET using crop coefficient curves or vegetation indices in that specific crop or vegetation type does not need to be known and the energy balance can detect reduced ET caused by water shortage, salinity or frost as well as evaporation from bare soil.

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A remaining challenge to applying satellite-based energy-balance algorithms for operational estimation of evapotranspiration (ET) is the calibration of the energy-balance model. Customized calibration for each image date is generally required to overcome biases associated with radiometric accuracy of the image, uncertainties in aerodynamic features of the landscape, background thermal conditions, and model assumptions. The CIMEC process (calibration using inverse modeling at extreme conditions) is an endpoint calibration procedure where near extreme conditions in the image are identified where the ET can be estimated and assigned. In the Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC TM ) energy-balance model, two endpoints represent the dry and wet ends of the ET spectrum. Generally, user-intervention is required to select locations in the image to produce best accuracy. To bring the METRIC and similar processes into the domain of less experienced operators, a consistent, reproducible, and dependable statistics-based procedure is introduced where relationships between vegetation amount and surface temperature are used to identify a subpopulation of locations (pixels) in an image that may best represent the calibration endpoints. This article describes the background and logic for the statistical approach, how the statistics were developed, area of interest requirements and assumptions, adjustment for dry conditions in desert climates, and implementation in a common image processing environment (ERDAS Imagine).

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While forest evapotranspiration (ET) dynamics in the Amazon have been studied both as point estimates using flux towers, as well as spatially coarse surfaces using satellite data, higher resolution (e.g., 30 m resolution) ET estimates are necessary to address finer spatial variability associated with forest biophysical characteristics and their changes by natural and human impacts. The objective of this study is to evaluate the potential of the Landsat-based METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model to estimate high-resolution (30 m) forest ET by comparing to flux tower ET (FT ET) data collected over seasonally dry tropical forests in Rondônia, the southwestern region of the Amazon. Analyses were conducted at daily, monthly and seasonal scales for the dry seasons (June-September for Rondônia) of 2000-2002. Overall daily ET comparison between FT ET and METRIC ET across the study site showed r 2 = 0.67 with RMSE = 0.81 mm. For seasonal ET comparison, METRIC-derived ET estimates showed an agreement with FT ET measurements during the dry season of r 2 >0.70 and %MAE <15%. We also discuss some challenges and potential applications of METRIC for Amazonian forests.