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.