In non-terrestrial network （NTN） scenarios， to overcome the effect of large Doppler frequency offset on the communication， a channel estimation method based on global information super resolution denoising neural network （GSRDnNet） is proposed. This method considers the channel estimation matrix at the pilot obtained by the least square（LS） estimation algorithm as a low-resolution small-size image and takes it as the input to the neural network. The input data is then processed by the GSRDnNet network to obtain a more accurate high-resolution image with a complete channel response matrix for the time-frequency resource block. Four NTN-tapped delay line （TDL） A，B，C and D channel models are used for simulation verification. Simulation results indicate that GSRDnNet improves mean squared error （MSE） performance by 3.37—8.83 dB compared to the traditional LS algorithm. Compared with the practical channel estimation（PCE） algorithm， the MSE is improved by 2.11—6.06 dB， and compared with the SRCNN+DnCNN method， which requires pre-interpolation processing， the MSE is improved by 1.37—4.40 dB. And compared with super resolution convolutional neural network （SRCNN）+denoising convolutional neural network （DnCNN） ，the input of GSRDnNet network model only considers the channel estimation matrix at the pilot， so it not only has higher estimation accuracy， but also reduces the computational complexity by about 84%.