A physical-based two-source evapotranspiration model with Monin–Obukhov similarity theory

Accurate quantification of energy budget components and its partitioning between canopy and soil is essential for improving water resource management. During the last few decades, several two-source evapotranspiration (ET) models have been developed by exploiting satellite datasets in conjunction with meteorological observations to estimate fluxes at different spatio-temporal scales. However, the complex parameterization scheme intrinsic to those models as well as the need for a large number of input datasets hampers their applications in a broader hydro-meteorological studies. In this study, we formulated a Leaf Area Index (LAI)-based methodology to develop a Two-Source Surface Energy Balance System (TS-SEBS) based on Monin–Obukhov similarity theory and Su (2002) principles, which uses fewer input datasets from satellite and meteorological observations to obtain fluxes on local as well as regional scales. Specifically, 97 medium-resolution Landsat TM/ETM+ images have been used to validate the TS-SEBS model estimations with (a) Eddy Covariance (EC)-based in-situ flux tower observations and (b) one-source SEBS (Surface Energy Balance System) model estimations in East Asian ecosystems. The mean bias and RMSE were 10.90 and 58.95 Wm−2 for instantaneous net radiation, 35.14, and 63.35 Wm−2 for instantaneous net radiation over the soil, and −25.06 and 45.93 Wm−2 for instantaneous net radiation over the canopy, whereas the ground heat flux showed a mean bias and RMSE of 45.60 and 59.21 Wm−2, respectively, over six selected sites. The results of sensible heat flux (H) for SEBS revealed its underestimation pattern (mean bias value of −20.68 Wm−2), whereas TS-SEBS tended to slightly overestimate values (mean bias value of 2.32 Wm−2) compared with EC observations. The TS-SEBS exhibited relatively better approximations of H (mean RMSE of 57.83 Wm−2) compared with SEBS (mean RMSE of 79.47 Wm−2) by reducing the error by ~27%. In terms of spatially distributed estimation of ET, the TS-SEBS outperformed SEBS with a mean normalized standard deviation value of 1.08 compared with 1.25 for SEBS, a reduction of ~14%. A mean Pearson’s correlation coefficient value >0.73 was obtained for both selected models based on a Taylor diagram, and these statistics more closely approximate the ground-based EC observations. The better performance of TS-SEBS compared with SEBS is due to the incorporation of improved LAI-based formulations in its structural algorithm to partition fluxes between soil and canopy, incorporation of improved wind speed profile extinction coefficient, and separate calculations of aerodynamic resistance for both soil and canopy components. The proposed methodology in the next-generation TS-SEBS model provides an unprecedented opportunity for quantifying improved energy budgets and partitioning fluxes between soil and canopy in an accurate and computationally efficient approach for better plant-soil-atmosphere interaction studies.