Retrieval of Water Vapor Content in Near- infrared Bands around 1 μm from MODIS Data by Using RM-NN

An algorithm based on radiance transfer model (RM) and a dynamic learning neural network (NN) for retrieving water vapor content from Moderate Resolution Imaging Spectrometer (MODIS) 1B data is developed in this paper. The MODTRAN4 are used to simulate the process of Sun-surface-sensor with different conditions. The dynamic learning neural network is used to estimate water vapor content. The analysis of simulation data indicate that the mean and the standard deviation of estimation error are under 0.06 and 0.08 gcm . The comparison analysis indicates that the retrieval result by RM-NN is obviously larger than NASA product (MYD05_L2) when the value of water vapor content are over 3.5 gcm and below 0.7 gcm . The mean and the standard deviation of retrieval error are about 0.56 gcm and 0.68 relative to the MYD05_L2. Finally, the comparison of estimation results with ground measurement data at Aerosol Robotic Network stations shows that the RM-NN can be used to accurately retrieve water vapor content from MODIS 1B data. 2 − 2 −