Simplifying the revised three-temperature model for remotely estimating regional evapotranspiration and its application to a semi-arid steppe

Abstract

Remote sensing is a feasible and economical way of mapping the spatial distribution of evapotranspiration (ET) at scales ranging from the regional to the global level. The revised three-temperature (3T-R) model, including land-surface temperature, reference temperature, and air temperature, is a promising approach for ET mapping, but aerodynamic resistance must be included in the model. The objectives of this study are to: (1) propose a simpler remote-sensing algorithm for estimating reference temperatures in the 3T-R model that would not depend on aerodynamic resistance; and (2) test the performance of the simplified 3T-R (3T-S) model. Assuming a pixel with the maximum surface temperature is a site without evaporation or transpiration, a method was proposed to replace the reference temperatures with paired maximum temperatures for soil and canopy, collected from a region with approximately equivalent solar radiation and terrain. A case study was conducted using Landsat Thematic Mapper (TM) images and synchronous ground observations performed in an Ecosystem Observation Station in northern China during the 2009 growing season. The results indicated that: (1) daily ET (ET_{3T_S}) estimated using the 3T-S model was close to the observational data, with a mean absolute error (MAE) of 0.24 mm d⁻¹ and a mean absolute percentage error (MAPE) of 9.78%; (2) the 3T-S model was much simpler than the 3T-R model with respect to its calculation procedures and data requirements, suggesting it might result in less error propagation, because the MAE and MAPE between ET_{3T_R} (daily ET estimated using the 3T-R model) and the observational data were 0.36 mm d⁻¹ and 14.71%, respectively. Therefore, it is concluded that the 3T-S model is a simpler method, but also an accurate way of estimating regional ET.

Acknowledgements

Thanks go to F. Xie, P. Wang, and Y.C. Feng for their fieldwork, to USGS for providing Landsat data, and to the anonymous referees for their valuable comments.

Funding

This study was supported by the National Natural Science Foundation of China [Project No. 41201433], [Project No. 30972421] and [Project No. 41101313]; the Specialized Research Fund for the Doctoral Programme of Higher Education of China [No. 20110171120001]; the National Basic Research Programme of China [Project No. 2009CB421308]; the Postdoctoral Science Foundation of China [Project No. 20090460795]; and the Financial Assistance Scheme for Young Teachers of Sun Yat-Sen University [Project No. 3126170].