Publications

Trebs, I; Mallick, K; Bhattarai, N; Sulis, M; Cleverly, J; Woodgate, W; Silberstein, R; Hinko-Najera, N; Beringer, J; Meyer, WS; Su, ZB; Boulet, G (2021). The role of aerodynamic resistance in thermal remote sensing-based evapotranspiration models. REMOTE SENSING OF ENVIRONMENT, 264, 112602.

Abstract
Aerodynamic resistance (hereafter r(a)) is a preeminent variable in evapotranspiration (ET) modelling. The accurate quantification of r(a) plays a pivotal role in determining the performance and consistency of thermal remote sensing-based surface energy balance (SEB) models for estimating ET at local to regional scales. Atmospheric stability links r(a) with land surface temperature (LST) and the representation of their interactions in the SEB models determines the accuracy of ET estimates. The present study investigates the influence of r(a) and its relation to LST uncertainties on the performance of three structurally different SEB models. It used data from nine Australian OzFlux eddy covariance sites of contrasting aridity in conjunction with MODIS Terra and Aqua LST and leaf area index (LAI) products. Simulations of the sensible heat flux (H) and the latent heat flux (LE, the energy equivalent of ET in W/m(2)) from the SPARSE (Soil Plant Atmosphere and Remote Sensing Evapotranspiration), SEBS (Surface Energy Balance System) and STIC (Surface Temperature Initiated Closure) models forced with MODIS LST, LAI, and in-situ meteorological datasets were evaluated against flux observations in water-limited (arid and semi-arid) and energy-limited (mesic) ecosystems from 2011 to 2019. Our results revealed an overestimation tendency of instantaneous LE by all three models in the water-limited shrubland, woodland and grassland ecosystems by up to 50% on average, which was caused by an underestimation of H. Overestimation of LE was associated with discrepancies in r(a) retrievals under conditions of high atmospheric instability, during which uncertainties in LST (expressed as the difference between MODIS LST and in-situ LST) apparently played a minor role. On the other hand, a positive difference in LST coincided with low r(a) (high wind speeds) and caused a slight underestimation of LE at the water-limited sites. The impact of r(a) on the LE residual error was found to be of the same magnitude as the influence of LST uncertainties in the semi-arid ecosystems as indicated by variable importance in projection (VIP) coefficients from partial least squares regression above unity. In contrast, our results for the mesic forest ecosystems indicated minor dependency on r(a) for modelling LE (VIP < 0.4), which was due to a higher roughness length and lower LST resulting in the dominance of mechanically generated turbulence, thereby diminishing the importance of buoyancy production for the determination of r(a).

DOI:
10.1016/j.rse.2021.112602

ISSN:
0034-4257