Improving MODIS-IR precipitable water vapor based on the FIDWFT model

Abstract

The monitoring of precipitable water vapor (PWV) is of significant importance for meteorological research and rainstorm prediction. The moderate resolution imaging spectroradiometer (MODIS) is one of the most widely used remote sensing PWV instruments aboard the Terra and Aqua satellites. The main objective of this study is to develop a fused inverse distance weighting and the Fourier transform model (FIDWFT) to calibrate MODIS Infrared (IR) PWV. In this research, one year of ground observation data from 17 GPS stations were utilized to evaluate the accuracy of MODIS IR PWV in Hong Kong. Initially, the PWV obtained by one radiosonde was used to verify the GPS PWV. Comparison between GPS and radiosonde showed good agreement with an R-squared of 0.97. After the estimation of GPS PWV in the study area was evaluated, MODIS IR products in the study area were extracted for evaluation. The comparison between MODIS and GPS showed that the R-squared is 0.81 and the mean bias (MB) is −1.60 mm. Considering the spatial interpolation and calibration problems of MODIS products, new model used an inverse distance weighting method to make MODIS IR PWV and GPS PWV completely coincide in space, and fifteen MODIS pixel points were selected for interpolation. Then the MODIS IR PWV was calibrated by GPS PWV using Fourier transform model. Results showed that compared with the MODIS IR PWV before calibration, the R-squared increases from 0.81 to 0.94, the root mean square error decreases from 8.72 mm to 4.98 mm, and the MB decreases from −1.60 mm to −0.83 mm. Therefore, the method is feasible to estimate MODIS IR water vapor, and it achieves better results.