The land surface temperature (Ts) is an important parameter in land surface and atmosphere studies. A set of synchronously observed "ground-truth" temperature as training data is required for some empirical/semiempirical statistical and neural network methods for retrieving Ts from passive microwave (PMW) remote sensing data. To provide information for the choice of the most suitable dataset in Ts retrieval of PMWremote sensing, the spatiotemporal comparison between the moderate-resolution imaging spectroradiometer Ts (MODIS Ts), the meteorologically observed Ts (in situ Ts), themeteorologically observed near-surface air temperature (in situ Ta), and European Center for Medium-Range Weather Forecast reanalysis products, the ERA-Interim Ts (ERA Ts), in South China for each season's daytime and nighttime is conducted in this paper. Results show that a large discrepancy between the MODIS Ts and the in situ Ts exists, whereas the discrepancies between the MODIS Ts, the in situ Ta and the ERA Ts are relatively smaller in daytime. For nighttime period, the differences between each dataset are relatively much smaller. Because the MODIS Ts is representative at the satellite pixel scale, it has a smaller spatial-scale mismatch with PMW data compared to in situ meteorological observation. The MODIS Ts is suitable for both the daytime and the nighttime PMW Ts model calibration if it is synchronously observed under almost clear-sky condition. By contrast, for the PMW Ts model calibration within the daytime period, the synchronously obtained in situ Ts is not suitable to be used as training data. If the ground temperature of daytime period derived from PMW is required, but the MODIS Ts is unavailable, the in situ Ta should be selected as the "ground truth" for the model calibration. However, it should be noticed that the inversion results are the near-surface air temperature rather than the Ts. Remarkably, reanalysis products such as the ERA Ts presents an alternative choice for both the daytime and the nighttime Ts model calibration if there are no MODIS Ts products or in situ temperature available. After the comparison, an example of PMW Ts retrieval for nighttime period was given, showing a promising performance on deriving an applicable PMW Ts inversion model based on the selection of an appropriate training dataset.