An integrated algorithm for estimating regional latent heat flux and daily evapotranspiration

Using remote-sensing data and ground-based data, we constructed an integrated algorithm for estimating regional surface latent heat flux (LE) and daily evapotranspiration (ETd). In the algorithm, we first used trapezoidal diagrams relating the surface temperature and fractional vegetation cover (f(C)) to calculate the surface temperature-vegetation cover index, a land surface moisture index with a range from 0.0 to 1.0. We then revised a sine function to assess ETd from LE estimated for the satellite's overpass time. The algorithm was applied to farmland in the North China Plain using Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) data and synchronous surface-observed data as inputs. The estimated LE and ETd were tested against measured data from a Bowen Ratio Energy Balance (BREB) system and a large-scale weighing lysimeter, respectively. The algorithm estimated LE with a root mean square error (RMSE) of 50.1 W m(-2) as compared to measurements with the BREB System, and ETd with an RMSE of 0.93 mm d(-1) as compared with the measurement by the lysimeter. Sensitivity analysis showed that changing meteorological variables have some influence on LE, while variation of f(C) has little effect on LE. The test of the model in the study indicated that the improved algorithm provides an accurate and easy-to-handle approach for assessing regional surface LE and ETd. Further improvement can be achieved in the assessments if we increase the accuracy of some key parameters on a large regional scale, such as the minimum stomatal conductance and the atmospheric vapour pressure deficit.