An Improved MODIS NIR PWV Retrieval Algorithm Based on an Artificial Neural Network Considering the Land-Cover Types

Estimating precipitable water vapor (PWV) with high accuracy and spatial resolution is important in many disciplines. Water vapor absorption and non-absorption channels can be observed in the near-infrared (NIR) ray of the Moderate Resolution Imaging Spectroradiometer (MODIS), which can be used to retrieve PWV. However, traditional algorithms overestimate the NIR PWV in North America. This study proposes a novel NIR retrieval algorithm based on machine learning that considers land-cover types to estimate high-accuracy PWV. To do this, nonlinear models between MODIS NIR transmittance, based on the two-and three-channel ratio, and global navigation satellite system (GNSS) PWV, recorded by the SuomiNet GNSS network, are established using a backpropagation neural network (BPNN). Verification shows that the root mean square error (RMSE)/standard deviation (STD)/bias of the two-channel ratio PWV is 1.29/1.29/0.02 mm, respectively, and the improvements of RMSE and STD are 66.32% and 37.98%, respectively. The RMSE/STD/bias values of the three-channel ratio PWV are 1.29/1.29/0.02 mm, respectively, and the improvements in RMSE and STD are 68.67% and 42.31%, respectively. In addition, the surface verification of the proposed method in six land-cover types shows that both the two-and three-channel ratio methods can yield satisfactory PWV estimates. Compared with the MODIS PWV products, the proposed method yields remarkable progress.

Automated summary

This study proposes a novel near-infrared detection algorithm based on machine learning to estimate high-accuracy precipitable water vapor (PWV) by considering land cover types. The verification results show significant improvements in PWV estimation compared to traditional algorithms, and the method is suitable for different land cover types.