Estimation of regional evapotranspiration over the Southern Great Plains based on Penman-Monteith theory and the soil moisture estimates

Penman-Monteith (PM) theory is a classic method to calculate evapotranspiration (ET) of land surfaces. However, soil resistance, related to soil moisture, is always difficult to determine over a large region. In this study, we developed an ET estimation algorithm by incorporating a soil moisture index (SMI) derived from the improved surface temperature-vegetation cover feature space, denoted as the PM-SMI algorithm. The PM-SMI algorithm was compared with the triangle algorithm and another Penman-Monteith based algorithm (PM-Yuan) that calculated soil evaporation using relative humidity. The three ET algorithms are compared and validated by Bowen Ratio measurements at 12 sites in the Southern Great Plain (SGP) that were mainly covered by grassland and cropland with low vegetation cover. The results showed that the PM-SMI algorithm performs the best among the three ET algorithms both on the instantaneous scale with R² of 0.86, RMSE of 53.67 W/m², bias of 6.83 W/m² and the daily scale with R² of 0.87, RMSE of 39.07 W/m², and bias of -4.04 W/m². PM-Yuan algorithm significantly underestimates ET resulting from soil evaporation and compared to triangle ET algorithm, PM-SMI is more reliable for estimation of ET over regional scale.