The importance of land surface temperature (LST) is increasingly recognized, and various methodologies have been proposed for the retrieval of LST using space-borne thermal infrared data. However, the selection of LST retrieval from Thermal Infrared Sensor (TIRS) of Landsat-8 based on different methods and the readily available MODIS LST products is still a challenging topic for local and global environmental studies. In this study, the potential of three different methods for retrieving LST using Landsat-8 TIRS data, including Radiative Transfer Equation (RTE), Single Channel (SC), and Split Window (SW) method in comparison with MODIS MOD11A1 LST product was evaluated. For accuracy assessment, 0 cm ground surface temperature (LST_GST) data was used. Our results almost showed same accuracy for RTE_B10 with RMSE = 0.35 °C, followed by MODIS with RMSE = 0.36 °C, and SC_B10 with RMSE = 0.38 °C. Secondly, SC_mean (Mean of B₁₀ and B₁₁), and RTE_mean (Mean of B₁₀ and B₁₁) generate nearly the same accuracy with RMSE = 0.53 °C, and RMSE = 0.54 °C, respectively. The other methods viz., SC_B11, RTE_B11, and SW method slightly showed lower accuracy with RMSE = 0.87 °C, RMSE = 0.88 °C, and RMSE = 0.91 °C, respectively. We found all the methods highly accurate and can be used successfully by climatologists, environmentalists, hydrologists, and urban planners concerning planning, and monitoring of the ever-increasing LST at local and global scale studies.

陆面温度（LST）的重要性已得到越来越多的认识，并且已经提出了各种使用星载热红外数据检索LST的方法。但是，基于不同的方法和现成的MODIS LST产品从Landsat-8的热红外传感器（TIRS）中选择LST检索对于本地和全球环境研究仍然是一个具有挑战性的主题。在这项研究中，与MODIS MOD11A1 LST产品相比，评估了使用Landsat-8 TIRS数据检索LST的三种不同方法的潜力，包括辐射传递方程（RTE），单通道（SC）和拆分窗口（SW）方法。 。为了进行准确性评估，使用了0 cm地面温度（LST _GST）数据。我们的结果几乎表明RTE的准确性相同_B10的RMSE = 0.35°C，其次是MODIS的RMSE = 0.36°C，SC _B10的RMSE = 0.38°C。其次，SC_均值（B ₁₀和B _{11的平均值}）和RTE_均值（B ₁₀和B _11的平均值）产生的精度几乎相同，分别为RMSE = 0.53°C和RMSE = 0.54°C。其他方法，SC _B11，RTE _B11，以及SW方法分别在RMSE = 0.87°C，RMSE = 0.88°C和RMSE = 0.91°C时显示出较低的精度。我们发现所有这些方法都是高度准确的，并且可以被气候学家，环保主义者，水文学家和城市规划人员成功地用于规划和监测在本地和全球范围内不断增长的LST。