Abstract: 【Objective】 With a large number of independently developed satellites launched, China has made tremendous advancements in remote sensing. The successful launch of the GF-1 satellite indicates that the observation of earth has entered the “high-resolution epoch” in China. This study was conducted to monitor the snow hydrological process of forests based on data from this Chinese satellite. 【Methods】The accuracy of forest snow characteristics based on the Chinese GF-1 satellite data was estimated by comparison with Landsat-8 satellite data. The research site was the forest of the northern Daxing’an Mountains. Based on the two methods of PLS(partial least squares)and BP(back propagation)neural networks, the linear and nonlinear SWE(snow water equivalent)inversion models were built using synchronous ground SWE field observation data. The models were evaluated through the indicators of E^-_RMSE, r^-_RMSE and average estimation accuracy. The best models based on the two types of remote sensing data were used to determine the inverse of the distribution characteristics of the SWE in the study area.【Results】The results showed that the accuracy of the models established using GF-1 data was slightly lower than that of those established using Landsat-8 data. The highest accuracy of a GF-1 model was 80.3%, which was 1.6% lower than that of the equivalent Landsat-8 model. The inverse SWE values based on the data of GM-1 were little higher than that of Landsat-8. The inversed SWE from the two types of remote sensing data showed the same spatial distributions, which showed that the SWE is highly correlated with topography, vegetation and land use type. Both terrain and vegetation are complicated in mountain forests. Snow ablation is relatively fast in spring. Owing to a higher spatial and time resolution than that of Landsat-8, the GF-1 satellite is more efficient for monitoring snow hydrological processes. 【Conclusion】Our results indicated that data from the GF-1 satellite can replace Landsat-8 data in monitoring snow hydrological processes in the northern Daxing’an Mountains.