As the second generation polar-orbiting meteorological satellite in China, FY-3 has been in operation for more than 7 years. Among the several Earth observing instruments, FY-3 is equipped with the Micro-Wave Radiation Imager (MWRI) and the Visible and Infrared Radiometer (VIRR), which can observe the global atmosphere, land, and ocean. A combined use of the multi-sensor remote sensing observations from FY-3 will enable us to extract comprehensive information of an Earth target. The multi-channel MWRI has a good ability to detect clouds and precipitation, although its spatial resolution changes with frequency. All VIRR channels have a high spatial resolution, but they can only obtain the information on the surface of a target. To get microwave signal at each VIRR pixel and enhance the calculated resolution of MWRI, in this study, a method is developed based on the principle of multi-information collocating, so that brightness temperature at each MWRI channel is collocated to a VIRR pixel. Based on their principle and applicability, the inverse distance weighted (IDW) and the nearest neighbor interpolation (NNI) methods were respectively tested in the process of collocating two kinds of data. After comparing the errors of the two methods by self-check analysis, IDW method was selected to collocate VIRR and MWRI signals, which results in a relatively small error. The result shows that IDW can enhance the calculated resolution of MWRI, while the error is less than 1 K at low frequency channels, and less than 3 K at high frequency channels. By analyzing the microwave brightness temperatures before and after the collocation at each MWRI channel, the spatial distribution of the collocated microwave brightness temperatures is found to have similar overall and detailed characteristics to the original data. The averaged deviation between before and after the collocation is small. The 89H channel has the largest difference of 2.41 K, whose relative deviation is less than 1.01%. Reverse information analysis shows that correlation coefficient between the scaled-up and original brightness temperatures is higher than 0.98 for every channel, and the standard deviation of their difference is between 0.4?4 K. The above results confirm the reliability of the IDW method in this collocation problem. As an application, land surface temperature of the Tibetan Plateau was retrieved by microwave signal in the collocated data. Clear-sky and cloudy regions were identified by comparing the land surface temperature and the thermal infrared brightness temperature. Snow and cloud phase were identified by using the infrared temperature in the collocated data. Because of the combination of FY-3 microwave and VIS/IR information, the error in snow retrieval using microwave data will be reduced, and the problem associated with visible and infrared being unable to penetrate through clouds can also be solved. Furthermore, cloud parameters can be retrieved. Using the retrieval results from the collocated data, cloud and surface features can be analyzed comprehensively, which can improve the ability in identifying snow and retrieving cloud parameters. Finally, the improved retrievals can provide observational facts for disaster warning and climate change assessment.