Total suspended matter (TSM) is one of the important parameters of water environment assessment and an important index of remote sensing water retrieval. GF-1/WFV and GF-6/WFV are free and open satellite data of the gaofen series, which are widely used in remote sensing monitoring. However, there are few studies on the applicability of the new bands of GF-6/WFV in water quality parameter inversion. This study takes Dianchi Lake in Yunnan province as the research area, based on the testing data synchronization with water transit (or similar) of the phase of GF-1/WFV and GF-6/WFV remote sensing image using statistics analysis method to the same band (blue, green, red and near-infrared) consistency analysis, regression method based on using the experience of the TSM inversion models of the two kinds of data, respectively, The model with GF-6/WFV added bands were compared with the model constructed by GF-1/WFV. The optimal model was applied to six GF-6/WFV images in 2020 to obtain the TSM distribution map of Dianchi Lake. The results show that the correlation coefficients of GF-1/WFV and GF-6/WFV in blue, green, red and near infrared bands are 0.98, 0.98, 0.97 and 0.99, respectively. The apparent reflectance of the two kinds of data is highly consistent. The inversion accuracy of GF-1/WFV difference model “B2+B4-B1” based on blue, green and near-infrared red bands is high, and the root means square error of model inversion is 6.35 mg·L-1, and the average absolute percentage error is 23.60%. The ratio model “1/B5+B6” constructed by GF-6/WFV based on near-infrared, red-edge 1 and red-edge 2 bands has a high inversion accuracy. Model inversion’s root mean square error (RMSE) is 3.07 mg·L-1, and the mean absolute percentage error (MAPE) is 20.65%. By comparing the difference model “B1-B4” constructed by GF-1/WFV with “B5-B4” constructed by GF-6/WFV, it is found that the root means square error of the latter is reduced by 2.61 mg·L-1, and the average absolute percentage is reduced by 32.33%. The experiment shows that the inversion effect of the model with the red-edge band is better than other models. The TSM distribution map of Dianchi Lake in 2020 was obtained using the modeling formula. The TSM in Dianchi Lake varied from 4 to 45 mg·L-1, with an average value of 18.23 mg·L-1. The overall spatial distribution showed a trend of heavy distribution in the north and light distribution in the south, and the time distribution of TSM in Dianchi Lake showed an upward and downward trend. This study can not only provide a reference for the sensor band setting of lake water quality monitoring but also provide technical support for water quality remote sensing monitoring by the water resources supervision department of Dianchi Lake.