A revised surface resistance parameterisation for estimating latent heat flux from remotely sensed data

Estimating evapotranspiration (ET) is required for many environmental studies. Remote sensing provides the ability to spatially map latent heat flux. Many studies have developed approaches to derive spatially distributed surface energy fluxes from various satellite sensors with the help of field observations. s study, remote-sensing-based λE mapping was conducted using a Landsat Thematic Mapper (TM) image and an Enhanced Thematic Mapper Plus (ETM+) image. The remotely sensed data and field observations employed in this study were obtained from Watershed Allied Telemetry Experimental Research (WATER). A biophysics-based surface resistance model was revised to account for water stress and temperature constraints. The precision of the results was validated using ‘ground truth’ data obtained by eddy covariance (EC) system. effects play an important role, especially for parameter optimisation and validation of the latent heat flux (λE). After considering the footprint of EC, the λE derived from the remote sensing data was comparable to the EC measured value during the satelliteʹs passage. The results showed that the revised surface resistance parameterisation scheme was useful for estimating the latent heat flux over cropland in arid regions.