## 1 Introduction  
Land Surface Temperature (LST), one of the most important key parameters in the water balance and energy exchange between the surface and atmosphere, is widely used in many applications [1; 2; 3; 4]. To obtain spatial and temporal records of LST, retrievals from satellite observations, such as geostationary and polar-orbit satellites, are of fundamental importance in global and regional studies [5; 6]. Due to technical constraints, it is difficult to acquire remotely sensed LST with high spatial, temporal and spectral resolutions simultaneously from a single sensor. For example, although the new Landsat 8 Thermal Infrared Sensor (TIRS) provides two thermal infrared bands, the revisit frequency of 16 days makes it difficult to apply in environmental studies that require more frequent information [6]. Low spectral information, which usually has only one thermal band for sensors with high spatial resolution, impedes the accurate atmospheric correction of satellite data because single-channel sensors observe much less information on atmospheric conditions than multi-channel sensors [7; 8; 9; 10]. The Chinese \"Environmental and Disaster Monitoring and Forecasting with a Small Satellite Constellation\" (HJ-1B for short for one of the satellites) have been widely used in various studies [11; 12], including those that use agricultural production analyses, vegetation indexes and evapotranspiration estimates. In addition, HJ-1B satellite could also be used in Urban Heat Island (UHI) studies and fragile ecosystem studies due to its relatively high temporal frequency and high spatial resolution, which can provide global coverage every two days with a spatial resolution of 300 m. However, the HJ-1B satellite has only one infrared sensor (IRS), and only single-channel methods can be used in LST estimation. Many studies of single-channel methods have been published. To solve the atmospheric correction problems associated with single-channel sensors, [PERSON] and [PERSON] [7] developed a mono-window algorithm for LST retrieval from Landsat TM6 data. [PERSON] and [PERSON] [13] developed a generalized single-channel method and applied to the Landsat Thematic Mapper (TM) for LST retrieval via Water Vapor Content (WVC) rather than atmospheric vertical profiles of air temperature and relative humidity obtained by radiosonde. [PERSON] and [PERSON] [14] also noted that the single-channel method provides similar or better results than the split-window for low atmospheric WVC conditions. The accuracy of atmospheric WVC is crucial because of its large impacts on atmospheric downwelling and upwelling radiances. [PERSON] [15] developed a single-channel algorithm based on a parametric model, which is more suitable for HJ-1B/IRS due to its better accuracy. [PERSON] _et al._[16] performed an error analysis of LST retrieval from HJ-1B/IRS data and confirmed that the LST retrieved from HJ-1B/IRS could not be higher than 1 K unless accurate atmospheric profile and land surface emissivity (LSE) data were applied. [PERSON] _et al._[17] used different sources of atmospheric profiles to retrieve LST with the single-channel method, and the result indicated that the retrieved LSTs based on radiosonde atmospheric profiles were better than that based on re-analysis profiles and satellite sounder products. Yet it is still difficult to obtain radiosonde data routinely. [PERSON] _et al._[18] improved a mono-window method for HJ-1B/IRS retrievals by considering the viewing zenith angle, yielding an accuracy better than 1 K.  
Although studies have been performed on HJ-1B LST retrieval, further studies of HJ-1B LST retrieval are still needed, especially in semi-arid regions where accurate land surface emissivity estimates are usually difficult to obtain. The objective of this paper is to develop and validate a single-channel algorithm by using different ancillary WVC data and single-channel thermal infrared data from the HJ-1B/IRS with a spatial resolution of 300 m in semi-arid regions in Heihe River Basin.