Evaluation of an extreme-condition-inverse calibration remote sensing model for mapping energy balance fluxes in arid riparian areas

Abstract

Abstract. Accurate information on the distribution of the surface energy balance components in arid riparian areas is needed for sustainable management of water resources as well as for a better understanding of water and heat exchange processes between the land surface and the atmosphere. Since the spatial and temporal distributions of these fluxes over large areas are difficult to determine from ground measurements alone, their prediction from remote sensing data is very attractive as it enables large area coverage and a high repetition rate. In this study the Surface Energy Balance Algorithm for Land (SEBAL) was used to estimate all the energy balance components in the arid riparian areas of the Middle Rio Grande Basin (New Mexico), San Pedro Basin (Arizona), and Owens Valley (California). We compare instantaneous and daily SEBAL fluxes derived from Landsat TM images to surface-based measurements with eddy covariance flux towers. This study presents evidence that SEBAL yields reliable estimates for actual evapotranspiration rates in riparian areas of the southwestern United States. The great strength of the SEBAL method is its internal calibration procedure that eliminates most of the bias in latent heat flux at the expense of increased bias in sensible heat flux.

Patterns and drivers of evapotranspiration in South American wetlands

“…Overall, ET models based on the energy balance, including SEBAL, present known limitations, related for instance to the need of high quality images (with low presence of clouds) and meteorological information, as well as a poor representation of instantaneous soil heat dynamics, which typically corresponds to a small fraction of the energy balance in subtropical/tropical wetlands 1 , 7 , 28 . Moreover, underestimations may occur due to the non-representation of advection processes 41 , 91 , leading to an underestimation of the instantaneous latent heat by 10-20% under moist conditions 92 , which however results in a low impact in the evaporative fraction.…”

A Global Implementation of Single‐ and Dual‐Source Surface Energy Balance Models for Estimating Actual Evapotranspiration at 30‐m Resolution Using Google Earth Engine

“…In both TSEB and HSEB implementations used here, the daily latent heat is derived from the evaporative fraction ($$f{EV}_{i}$$) at satellite overpass computed from the instantaneous ratio LE/( R n − G ) and an advection factor Ω (a function of the vapor pressure deficit) to account for the increase in latent heat during the afternoon period (Gentine et al., 2007; Hong et al., 2014; Teixeira et al., 2009).…”

“…) at satellite overpass computed from the instantaneous ratio LE/(R n − G) and an advection factor Ω (a function of the vapor pressure deficit) to account for the increase in latent heat during the afternoon period (Gentine et al, 2007;Hong et al, 2014;Teixeira et al, 2009).…”

Evaluation of remote sensing-based evapotranspiration models against surface renewal in almonds, tomatoes and maize

“…Based on the instantaneous R n , H and G at the satellite overpass time, the instantaneous evaporative fraction (EF i ) can be calculated (Eq.8) and converted into daily evaporative fraction (EF 24 ) (Eq.9) by using an advection factor Ω, which is used to reduce errors caused by the ET a increase during the afternoon (Hong et al, 2014):…”