A Method of Using WRF-Simulated Surface Temperature to Estimate Daily Evapotranspiration.

Surface temperature is one of the key parameters for estimating regional evapotranspiration (ET) based on the Surface Energy Balance System (SEBS) model using remote sensing data. However, continuous daily remote sensing surface temperature data are often not available due to the weather and environmental conditions. This paper proposed a scheme to obtain reliable ET that estimating ET using WRF-simulated surface skin temperature (TSK) and then modifying the deviation using the normalized difference vegetation index (NDVI). This study aims to explore whether the model data can be a viable option when the continuous-time-series remote sensing surface temperature is missing for estimating ET. Comparison results show that the correlation between WRF TSK and the measured temperature of the 2-cm soil (Ts) is better than MODIS land surface temperature (LST) in the study area, while the correlation between MODIS LST and the measured surface radiation temperature (IRT) is better than WRF TSK. The MODIS LST is significantly higher than Ts, and the WRF TSK is closer to Ts than MODIS LST. However, the ET calculated using WRF TSK was not good, exhibiting relatively high ET in the whole area and a poor correlation with the measurements, whereby R2, RMSE, and the percent bias (PBIAS) were equal to 0.1256, 5.2783 mm, and −202.17%, respectively. According to the principle of land surface process simulation in WRF, this paper proposes using NDVI to modify ET calculated using TSK. The comparison between the modified ET and the measurements exhibited a relatively good correlation, with R2 = 0.7532, RMSE = 1.0993 mm, and PBIAS = −17.9%. Therefore, the model surface temperature data can be used to estimate continuous-time-series regional ET when NDVI is used to modify the deviation, which indicates the surface temperature data simulated by the WRF Model can become the optional data for estimating ET and compensate for the shortcoming of poor time continuity of remote sensing data, further expanding the application prospects of meteorological model data in the remote sensing field. [ABSTRACT FROM AUTHOR]