## 1 Introduction  
Land surface temperature is a key parameter in the physics of land surface processes at regional and global scales; it is a metric representative of surface-atmosphere interactions and energy fluxes between the atmosphere and the ground surface [1]. Currently, satellite data offer the possibility to map land surface temperature over the entire globe effectively, with sufficiently high spatial and temporal resolution [2]. The spatially distributed land surface temperature estimated from thermal infrared (TIR) remote sensing data is widely used in studies of evapotranspiration, climate change and the hydrological cycle [3; 4; 5; 6], soil moisture estimation [7; 8], forest fire detection [9; 10], vegetation water stress [11], urban heat island monitoring [12] and many other environmental monitoring applications. Passive microwave (PW) remote sensing can also provide land surface temperature observations [13], which is unaffected by clouds. However, the satellite based PW sensors have very coarse spatial resolutions compared with the thermal infrared (TIR) remote sensing. In addition, estimation of land surface temperature from the PW have limitations associated with restricted response depth and producing more uncertainties over barren and sparse vegetation covered land surfaces compared to TIR, which is commonly used in modeling evapotranspiration [14]. In a heterogeneous and non-isothermal pixel, however, the radiance observed by the remote sensor at the top of atmosphere (TOA) is the ensemble radiance of several components (_i.e._, sunlit and shaded soil and vegetation) [2] so the pixel-average temperature cannot reveal the real temperature of each component, particularly in the sparse vegetation-covered arid and semi-arid areas where the soil and vegetation component temperatures are usually significantly different. Additionally, the component temperatures of a mixed pixel encapsulate more physical information than the composite temperature that relate to soil and vegetation heat fluxes and hydrologic states [2]. By deriving component soil and vegetation temperatures, the accuracy of surface fluxes estimated by two-source (soil + vegetation) energy balance models should improve capabilities to monitor crop water stress and drought, estimate surface soil moisture and result in more accurate weather forecasts [5; 8; 11; 15; 16]. The task of retrieving the component temperatures, however, is difficult, due to the variability in the distribution of internal components and the definition of effective emissivity and thermal anisotropic radiation regimes in forward processes of TIR remote sensing data. Currently, studies of soil and vegetation component temperatures retrieval have concentrated on two aspects: multi-angular observation and multi-visible and near infrared (VNIR) and TIR band optimization. The multi-angular method generally establishes a model for reflecting the thermal infrared radiation in a continuous vegetation distribution but non-isothermal mixed pixel according to the thermal anisotropic radiation regimes. It then applies the Monte Carlo method to simulate the relationship between the component effective emissivities and variables. Through the aforementioned step, at least two equations including the parameters of soil and vegetation temperatures are established, and the soil and vegetation component temperatures are retrieved combining thermal infrared data observed by multi-angular sensors such as the Along Track Scanning Radiometer (ATSR), ATSR2, Advanced ATSR, and some airborne multi-angular thermal infrared systems [11, 15, 17, 18, 19]. However, the multi-angular methods may not provide accurate enough component temperatures for reliable component flux estimation. In addition, the different viewing zenith angles correspond to different pixel resolution, which means the various viewing angles are sampling a different size footprint of the land surface [2, 20].