A Coupled Ground Heat Flux-Surface Energy Balance Model of Evaporation Using Thermal Remote Sensing Observations

One of the major undetermined problems in evaporation(ET) retrieval using thermal infrared remote sensingis the lack of a physically based ground heat flux (G) modeland its integration within the surface energy balance (SEB)equation. Here, we present a novel approach based on couplinga thermal inertia (TI)-based mechanistic G model withan analytical surface energy balance model, Surface TemperatureInitiated Closure (STIC, version STIC1.2). The coupledmodel is named STIC-TI. The model is driven by noon–night (13:30 and 01:30TS4 ) land surface temperature, surfacealbedo, and a vegetation index from MODIS Aqua inconjunction with a clear-sky net radiation sub-model and ancillarymeteorological information. SEB flux estimates fromSTIC-TI were evaluated with respect to the in situ fluxesfrom eddy covariance measurements in diverse ecosystemsof contrasting aridity in both the Northern Hemisphere andSouthern Hemisphere. Sensitivity analysis revealed substantialsensitivity of STIC-TI-derived fluxes due to the land surfacetemperature uncertainty. An evaluation of noontime G(Gi) estimates showed 12 %–21% error across six flux towersites, and a comparison between STIC-TI versus empiricalG models also revealed the substantially better performanceof the former. While the instantaneous noontime net radiation(RNi) and latent heat flux (LEi) were overestimated(15% and 25 %), sensible heat flux (Hi) was underestimated(22 %). Overestimation (underestimation) of LEi (Hi) wasassociated with the overestimation of net available energy(RNi 􀀀Gi) and use of unclosed SEB flux measurements inLEi (Hi) validation. The mean percent deviations in Gi andHi estimates were found to be strongly correlated with satel-lite day–night view angle difference in parabolic and linearpattern, and a relatively weak correlation was found betweenday–night view angle difference versus LEi deviation.Findings from this parameter-sparse coupled G–ET modelcan make a valuable contribution to mapping and monitor-ing the spatiotemporal variability of ecosystem water stressand evaporation using noon–night thermal infrared observationsfrom future Earth observation satellite missions such asTRISHNA, LSTM, and SBG.