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 ( ET d ). 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 ET d 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 ET d 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 ET d 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 ET d . 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. [ABSTRACT FROM AUTHOR]