## 1 Introduction  
As a major component of the surface energy balance and the water budget, evapotranspiration (ET) has substantial effects on global climate change, water management and crop yields [1; 2; 3]. ET is the water transferred from the land surface to the atmosphere, and the latent heat accompanying ET is LE, where L is the latent heat of vaporization. Satellite-based remote sensing has been identified as a more suitable mean of mapping the spatial distribution of ET or LE [4]. Nevertheless, the land surface is highly heterogeneous in terms of its geometric and physical aspects, and the transfer or exchange processes between the land surface and the atmosphere are likely to be nonlinear. Models and methods developed assuming a homogeneous land surface may confront serious problems in the estimation of ET or LE using medium- or coarse-resolution data [5]. Moderate Resolution Imaging Spectrometer (MODIS) data have a daily return time (one overpass per day at varying view angles). At present, such data are ideal for the simulative monitoring of regional-scale changes in surface energy and water fluxes. Studies have shown that a pixel resolution of 500 m or less is necessary for accurate assessment of surface energy balance and ET or LE changes [1; 6]. However, the spatial resolution of the MODIS thermal-infrared sensor (i.e., ~1000 m) is low, and the influence of the spatial scale (mixed-pixel) effect is substantial, especially where complex patterns of land use exist. Due to differences in the geometry of the underlying surface and land cover, it is difficult to use the mean or a single parameter to represent entire pixels. Moreover, farms and fields in many regions of the world are significantly smaller than individual pixels; in particular, in developing countries, fields may be less than 0.1 ha (30 m \(\times\) 30 m) in size [6]. Hence, coarse-resolution data cannot be used to estimate ET or LE from individual fields. Developing a model of heat fluxes for use with mixed pixels and using the spatial information of the study area to get more reliable results may represent a solution to this problem. Middle- or high-resolution data (e.g., from the Land remote-sensing satellite Thematic Mapper (Landsat TM/ETM+), the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and the Environment and Disaster Monitoring and Forecasting Satellite Constellation (Chinese HJ-1A/HJ-1B)) have high resolution but a low frequency of satellite coverage over regions of interest (e.g., approximately every 2 weeks for Landsat TM and ASTER; 4 days for the Chinese HJ-1A/HJ-1B). Moreover, cloud cover further reduces the number of useable observations of surface conditions resulting in high-resolution satellite imagery of a region typically being available once a month, which is not very useful for routine ET or LE monitoring [7]. To estimate ET or LE using surface energy balance models (e.g., the two-source model (TSM) [8] or the Surface Energy BALance (SEBAL) model [9]) and remote sensing data, the sensible heat flux (H) is the most important component, and it is also complex to calculate [10]. H is the transfer of turbulent heat between the surface and the atmosphere. It is driven by temperature differences and controlled by resistances that depend on local atmospheric conditions and land cover properties. In particular, the land surface temperature (LST) and the dynamic parameters (i.e., the zero plane displacement height and the roughness length) are more influential than other input parameters, because the LST provides an indication of the net effect of land-atmosphere interactions, which results in the process of evapotranspiration [2]. Sensitivity analysis has shown that uncertainty in LST has a significant impact on the estimation of H, and a 3 K change in LST yielded a change in H of up to 75% in TSM. A similar change in the difference between surface and air temperatures led to changes in H of up to 45% in SEBAL [11].