Land surface temperature (LST) is a significant parameter in surface and atmospheric systems which can be used to describe the physical processes of energy and water exchange. There are several ways to retrieve land surface temperature using satellite imagery. The purpose of the current study is to compare the accuracy of different LST retrieval algorithms, including 11 types of split-window algorithms, two mono-window algorithms, and three single-channel algorithms. The validation of LST retrieval algorithms was done using temperature-based and cross-validation methods. In this study, 20 images from Landsat 8 satellite related to 2017 and 2018 were used to retrieve LST in different ways. The temperatures obtained from different methods were compared in one day, and the average of LST of the different methods was about 5 ℃ different, which indicates the importance of the existing validation methods. Using Sentinel-2 image classification, 20 points in homogeneous areas with pure pixels were selected to measure the LST using a thermometer for land validation. Results showed that in a split window algorithm which was applied on the water vapor image, the RMSE error has been reduced by 2 ℃. In cross-validation, MODIS LST images were used, and RMSE temporal image was prepared, and classified into five groups including less than 2, 2–3, 3–4 and more than 5 ℃ .Comparing the area of each of these groups in different methods showed that MW2 and SW1 methods have less accuracy than the other methods. The SW9 algorithm can retrieve temperature with an error of less than 2 ℃ in 80% of the area. The results of cross-validation showed that the SC2 algorithm had the highest accuracy among the algorithms that use a thermal band for LST retrieval with a RMSE spatial of 5.3 °C and the SW10 and SW11 have shown the highest accuracy among the algorithms that use two thermal bands with RMSE less than 5 ℃. In general, the algorithms that use atmospheric transmittance coefficients and water vapor as inputs are more accurate than the others.

地表温度（LST）是地表和大气系统中的重要参数，可用于描述能量和水交换的物理过程。有几种使用卫星图像检索地表温度的方法。当前研究的目的是比较不同LST检索算法的准确性，包括11种类型的分割窗口算法，两种单窗口算法和三种单通道算法。LST检索算法的验证是使用基于温度和交叉验证的方法完成的。在这项研究中，使用了来自Landsat 8卫星的2017年和2018年相关的20张图像以不同的方式检索了LST。比较一天中从不同方法获得的温度，不同方法的LST平均值相差约5℃，这表明了现有验证方法的重要性。使用Sentinel-2图像分类，选择具有纯像素的均质区域中的20个点，以使用温度计进行土地测量来测量LST。结果表明，在应用于水汽图像的分割窗口算法中，RMSE误差降低了2℃。在交叉验证中，使用MODIS LST图像，并准备了RMSE时间图像，并将其分为5个组，包括低于2、2–3、3–4和高于5℃。不同的方法表明MW2和SW1方法的准确性低于其他方法。SW9算法可以在80％的区域中以小于2℃的误差检索温度。交叉验证的结果表明，在使用热带进行LST检索且RMSE空间为5.3°C的算法中，SC2算法具有最高的准确性，而在使用两种方法的算法中，SW10和SW11则显示出最高的准确性。 RMSE低于5℃的热带。通常，使用大气透射系数和水蒸气作为输入的算法比其他算法更准确。