Water vapor retrieval from MODIS NIR channels using ground-based GPS data

Abstract

A novel algorithm for water vapor retrieval from Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) channels is proposed in this research. In contrast to conventional retrieval algorithms based on radiative transfer methods, this algorithm uses the empirical regression functions to calculate precipitable water vapor (PWV). In this article, water vapor data observed from January 1, 2003, to December 31, 2017, from 464 GPS stations situated in western North America serve as reference data to determine the relationship between the transmittance of the water vapor absorption channels and atmospheric water vapor content. The model is trained on different subsets of the training data through the bootstrap resampling method. Validation results against PWV observations during the period 2010-2017 from five globally distributed GPS stations illustrate that the algorithm can significantly improve the accuracy of MODIS NIR water vapor data, with root-mean-square error (RMSE) reduction of 22.48% from 7.670 to 5.946 mm for two-channel ratio method and 21.69% from 7.670 to 6.006 mm for three-channel ratio method for MODIS/Terra satellite data, and RMSE reduction of 16.42% from 7.191 to 6.010 mm and 15.26% from 7.191 to 6.094 mm for PWV derived from two-channel and three-channel ratio methods from Aqua, respectively, for MODIS/Aqua satellite data.