## 6 Conclusions  
Developing approaches for separating soil and vegetation component temperatures from multiple or single view angle TIR observations sensors is seen as a major advantage for monitoring plant water use and stress. Multi-VNIR, TIR optimization method, and multi-angle methods have been developed. These methods do not appear capable of providing reliable component temperatures except under certain (limited) conditions. The former method always leads to ill-posed nature equations. Although the later method can remove the ill-posed nature equations during retrieval of the component temperatures, it is far from satisfactory considering the coarse accuracy and complex process and so on. This study proposed using _T-a_ space to separate the soil and vegetation component temperatures according to the SEB-1S modeling approach using remote sensing imagery data obtained from satellite sensor with 90 m TIR pixel resolution (ASTER). Finally, the soil and vegetation component temperatures were successfully separated using _T-a_ space with analytical geometry with the defined seven end-members  
Figure 7: Evaluations in separated soil temperatures and vegetation temperatures at the water stress iso-line at the ASTER overpass times.  
and determining the slopes of the soil moisture iso-lines in the space. This approach is robust, and provides accurate component temperatures at the regional scale, even though the TIR remote sensing data products are mainly from single view angle observation sensors, such as ASTER, Landsat, MODIS, environmental and disaster monitoring and forecasting satellite constellation (HJ-1 Satellite) and so on. The validation in the HiWATER study area demonstrates that reasonable soil and vegetation component temperatures have been derived using the _T_-\(\alpha\) space derived from ASTER observations. However, several improvements are foreseen to optimize the separated results and to extend its applicability: (1) there is a considerable need for ground-based soil and vegetation component temperatures at pixel scale to validate the satellite-derived component temperatures; (2) although the methodology using _T_-\(\alpha\) space can be operationally applied to irrigated regions surrounded by an arid landscape (oasis areas) using the remote sensing image data, applications for more heterogeneous semiarid areas containing natural vegetation should be examined to see if additional parameterizations are required to obtain reliable results, for example, by introducing the aerodynamic resistance to constrain the minimum vegetation temperature; (3) achieving an accuracy of 1.0 K for land surface temperature from the satellite image data will improve component temperature separation.  
The _T_-\(\alpha\) space approach derived according to the SEB-1S model was used to estimate soil and vegetation component temperatures from the ASTER TIR and VNIR data. The validations demonstrate a good agreement between the estimated and measured/interpolated soil and vegetation temperatures. For the soil temperatures, the average bias and RMSE values were \(-\)0.26 K and 0.83 K, respectively, while for the separated vegetation temperatures, the average bias and RMSE values were \(-\)1.29 K and 1.64 K respectively. The derived soil and vegetation temperatures along the selected soil moisture iso-line are equal, though with slight fluctuations. In addition, the derived vegetation temperatures are extremely close to the measured near surface (5 m, above ground level) air temperatures when the remote sensing pixels were nearly full vegetation cover and soil are well. These validations means the proposed _T_-\(\alpha\) space approach is appropriate to derive soil and vegetation temperatures from single view angle observations which could improve in estimating the soil evaporation and vegetation transpiration, monitoring crop drought and soil moisture and forecasting climate change at the regional scale.