Aiming at GF-1 WFV2 sensor, a method for deriving Tasseled Cap Transformation (TCT) coefficients was proposed in this paper. Based on 18 GF-1 images and 5 synchronous Landsat 8 images of different regions throughout the country, a large number of typical samples covering different ground types were selected, and the spectral characteristics of these samples were then analyzed. On this basis, the wetness component of the TCT was retrieved by the regression using three Landsat 8 and WFV2 synchronous images. The orthogonal brightness and greenness components were then derived with the Gram-Schmidt orthogonalization method. The results showed that the derived first two components can explain more than 97% of total variation, and the first three components can explain more than 99% of the total variation. The experimental results from different regions showed that each component had good stability, and the scatter-plots of different components also exhibited similar distribution patterns. This indicates that the derived coefficients have good applicability. Compared with the validation images, the results of TCT are well correlated with corresponding Landsat 8 components. Although these two sensors have different band settings and different spectral responses, their correlation coefficient is still as high as 0.9. The application results for GF-1 WFV1, WFV3 and WFV4 showed that the coefficients derived from WFV2 sensor can also be used for WFV3 and WFV1 sensors, but they are not recommended for WFV4 sensor.