Landsat Collection 2 Level-2 Surface Temperature (LC2L2ST) was formally released in December 2020 by the U.S. Geological Survey (USGS). However, there are few reports on this new land surface temperature (LST) product. As this product will be the only LST data provided by the USGS starting in 2022, it is necessary to evaluate the product timely. Among various satellite LST products, the quality of the MODIS LST product is well recognized, and widely used. Therefore, this paper, for the first time, performed a cross-comparison between the new Landsat LST product and the MODIS LST product to examine the quality of the new product. Different regions in China (Fuzhou, Taihu, Yinchuan and Dunhuang) were selected as the test areas, and 20 pairs of LC2L2ST and MODIS LST synchronous images were used for the comparison. The images cover different land types, such as vegetation, water, town and deserts across different seasons. A total of 560 homogeneous regions of interest (ROI) were selected from the images of the test areas. The regression analysis was carried out to examine the fit of the ROIs and the quantitative relationship between the two LST products. The conversion model between them was also developed. The results showed that the new LC2L2ST product is highly correlated with the MODIS LST product. Each of the four test areas can achieve a coefficient of determination (R2) greater than 0.98. Integrating the 560 samples from the four test areas also obtain an R2 close to 0.98. Nevertheless, differences between the two products have also been founded. The LC2L2ST is 0.90℃ averagely higher than the MODIS LST (RMSE = 2.29 ℃). However, LC2L2ST can be slightly lower than MODIS LST in late fall and winter seasons but significantly higher than extremely hot summer seasons with a bias close to 7 ℃. The analysis revealed that the differences were related to spatial resolution, sensor viewing angles, land cover types and seasons. In general, the new LC2L2ST product strongly correlates with the MODIS LST, but significant differences were also observed in the summer months. Therefore, the new Landsat LST product must be further tested with in-situ measured LST data. Due to the differences in this paper, the two LST data products need to be converted when they must be collaboratively used. This study developed the conversion equation between the two LSTs based on the 560 ROIs. The verification found that the differences between the two data after conversion were greatly reduced. It is conducive to the cooperative use of the two LST data and providing continuous remote sensing data for long-term LST monitoring.