Application of remote sensing-based two-source energy balance model for mapping field surface fluxes with composite and component surface temperatures

Abstract

Operational application of a remote sensing-based two source energy balance model (TSEB) to estimate evaportranspiration (ET) and the components evaporation (E), transpiration (T) at a range of space and time scales is very useful for managing water resources in arid and semiarid watersheds. The TSEB model uses composite land surface temperature as input and applies a simplified Priestly-Taylor formulation to partition this temperature into soil and vegetation component temperatures and then computes subsequent component energy fluxes. The remote sensing-based TSEB model using component temperatures of the soil and canopy has not been adequately evaluated due to a dearth of reliable observations. In this study, soil and vegetation component temperatures partitioned from visible and near infrared and thermal remote sensing data supplied by advanced scanning thermal emission and reflection radiometer (ASTER) are applied as model inputs (TSEB CT) to assess and refine the subsequent component energy fluxes estimation in TSEB scheme under heterogeneous land surface conditions in an advective environment. The model outputs including sensible heat flux (H), latent heat flux (LE), component LE from soil and canopy from the TSEB CT and original model (TSEB PT) are compared with ground measurements from eddy covariance (EC) and larger aperture scintillometers (LAS) technique, and stable isotopic method. Both model versions yield errors of about 10% with LE observations. However, the TSEB CT model output of H and LE are in closer agreement with the observations and is found to be generally more robust in component flux estimation compared to the TSEB PT using the ASTER data in this heterogeneous advective environment. Thus given accurate soil and canopy temperatures, TSEB CT may provide more reliable estimates of plant water use and values of water use efficiency at large scales for water resource management in arid and semiarid landscapes.

Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

“…Component net radiation was calculated using the approach described in Xia et al (2016) and Song et al (2016), which is based on measurements of incoming radiation components, the component temperatures T s and T c , and information on surface properties such as albedo, transmittance through the canopy and emissivity. Soil heat flux was estimated as a function of soil net radiation following the approach proposed by Santanello and Friedl (2003), which accounts for the phase shift between the two quantities during daytime.…”

Validation of Regional-Scale Remote Sensing Products in China: From Site to Network

“…In addition, these comprehensive measurements were beneficial for identifying scale effects and providing ground truth that coincides with the development of the remote sensing models and scaling approaches for validation purposes. As demonstrated in [69,70], Song et al validated corn transpiration and soil evaporation estimated by the two-source energy balance model (TSEB) at site and field scales based on the EC and stable oxygen and hydrogen isotope measurements. In [73], an innovative validation framework including quantification of the spatial heterogeneity, optimisation of the ground sampling strategy, multi-scale measurements, upscaling theory, uncertainty analyses, and validation method (direct validation, indirect validation and cross validation) was proposed to validate ET RSPs at different scales.…”

Intercomparison of Three Two-Source Energy Balance Models for Partitioning Evaporation and Transpiration in Semiarid Climates

“…The iterative procedure is commonly adopted in the TSEB model and this approach is verified by different researchers [69,74,75]. The VFC/LST-based approach and related contextual-based method are applied by different researchers, such as Zhang et al [24], Zhang et al [55], Merlin et al [44], Merlin et al [45], Long and Singh [47], Yang et al [48], Song et al [53], and Sun [49].…”

“…2018, 10, 1149 4 of 20 where t is the solar time angle (s), a is the amplitude parameter (dimensionless), b is the period length (s), and c is the shift (s). In this study, parameters a, b, and c take the values of 0.3, 86,400, and 10,800 following Colaizzi et al [43] and Song et al [53]. In this study, the series resistance network form was applied, in which H c , H s , and the sum of both terms are calculated as:…”