A high-precision retrieval method for methane vertical profiles based on dual-band spectral data from the GOSAT satellite

Satellite remote sensing has become an important method of monitoring methane concentrations. The full physics algorithm is widely used for methane vertical profile retrieval, but the method is relatively timeconsuming. Most current studies have focused on the retrieval of methane vertical profiles from thermal infrared (TIR) spectral data only, resulting in low accuracy in the near-surface retrieval results. To obtain the atmospheric methane vertical profile from satellite sounding data quickly and accurately, this paper proposes a high-precision retrieval method based on the ResNet18 model, a conventional and effective deep convolutional neural network, using TIR band and shortwave infrared (SWIR) band spectral data from the GOSAT. The model performs well on the test set, with mean absolute error (MAE) below 12.78 ppb and root mean square error (RMSE) below 23.71 ppb with pressure greater than 150 hPa. The accuracy of the retrieval results and GOSAT CH4 vertical profile products are validated using airborne measurement data. In the mid-latitude region, using NOAA airborne measurement data as reference values, the average of MAE and RMSE of the retrieval results between 500 hPa and 900 hPa are reduced by 12.05 ppb and 14.07 ppb, respectively, compared to the GOSAT CH4 vertical profile product. In the low-latitude region, using CARIBIC airborne measurement data as reference values, the average of MAE and RMSE between 300 hPa and 600 hPa are reduced by 11.18 ppb and 12.20 ppb, respectively. The validation results show that the methane vertical profile can be quickly and accurately retrieved based on the ResNet18 model, combined with dual-band spectral data, temperature, and surface pressure.

Automated summary

A high-precision retrieval method based on a deep convolutional neural network and satellite remote sensing data is proposed to obtain accurate methane vertical profiles.