Land surface temperature (LST) as an important environmental variable provides valuable information for earth environmental system modelling. Currently, LST is obtained through satellite thermal sensors at various spatial and temporal resolutions. Although spatially continuous satellite-based LST measurements are intended to overcome the shortcomings of sparse ground-based LST measurements, LST images often contain anomalous values due to the existence of clouds or sensor malfunctioning. The problem becomes more serious where the users deal with high spatial resolution characterized by low temporal resolution. This study examines the capability of a newly developed graph signal processing (GSP) method using two-dimensional single-date thermal data. For this purpose, four Landsat/TIRS datasets are analyzed. The data of five elliptical regions on thermal images are eliminated and then reconstructed through the GSP method and using the LST values of the enclosing rectangles containing the ellipsoids. The results indicate that the temperature variation determined by the GSP method generally conforms to the original image LST values. According to a correlation test conducted on the original image LST and those obtained through the GSP method, the values vary from 58% to 95%, which is an above-the-average rate (RMSE from 0.69 to 2.27). The statistical analysis of the original image LST in both the elliptical regions and the enclosing rectangles containing the ellipsoids indicates that an increase in the variance of LST data causes an increased error in the calculation of temperature by the GSP method, and vice versa. The results of the analysis of variance (ANOVA) and Duncan test indicated that an increase in the number of the non-zero spectral bins would result in increased RMSE values for all the dates and the regions. Moreover, the model errors were significant at the 0.05 level across all the image date and five elliptical study regions. Based on the results, the use of this method is recommended for the reconstruction of LST missing values, where dissimilarity of atmospheric conditions limits the use of other methods that depend on the time series data of various dates and a great deal of data calculation.

地表温度（LST）作为重要的环境变量，为地球环境系统建模提供了有价值的信息。当前，LST是通过卫星热传感器以各种空间和时间分辨率获得的。尽管基于空间连续卫星的LST测量旨在克服基于地面的LST测量稀疏的缺点，但是LST图像由于云的存在或传感器故障而经常包含异常值。当用户处理以低时间分辨率为特征的高空间分辨率时，问题变得更加严重。这项研究检查了使用二维单日期热数据的最新开发的图形信号处理（GSP）方法的功能。为此，分析了四个Landsat / TIRS数据集。消除热图像上五个椭圆区域的数据，然后通过GSP方法并使用包含椭圆体的矩形的LST值进行重建。结果表明，由GSP方法确定的温度变化通常符合原始图像LST值。根据对原始图像LST和通过GSP方法获得的图像进行的相关性测试，该值从58％到95％不等，这是一个高于平均水平的值（RMSE从0.69到2.27）。对椭圆区域和包含椭圆形的封闭矩形中的原始图像LST的统计分析表明，LST数据方差的增加导致通过GSP方法计算温度时误差增加，反之亦然。方差分析（ANOVA）和Duncan检验的结果表明，非零频谱仓数量的增加将导致所有日期和区域的RMSE值增加。此外，在所有图像日期和五个椭圆研究区域中，模型误差在0.05水平上均很显着。根据结果​​，建议使用此方法来重建LST缺失值，因为大气条件的差异限制了其他方法的使用，这些方法依赖于各种日期的时间序列数据和大量数据计算。所有图像日期和五个椭圆研究区域的水平均为05。根据结果​​，建议使用此方法来重建LST缺失值，因为大气条件的差异限制了其他方法的使用，这些方法依赖于各种日期的时间序列数据和大量数据计算。所有图像日期和五个椭圆研究区域的水平均为05。根据结果​​，建议使用此方法来重建LST缺失值，因为大气条件的差异限制了其他方法的使用，这些方法依赖于各种日期的时间序列数据和大量数据计算。