Paper Info
Title
Comparison of Satellite-Derived Precipitable Water Vapor Through Near-Infrared Remote Sensing Channels
Abstract
The retrieval accuracies of three typical near-infrared (NIR) precipitable water vapor (PWV) products are thoroughly discussed in this article. The NIR PWV data are obtained from three satellite sensors: the Moderate-Resolution Imaging Spectroradiometer (MODIS)/Terra, the medium-resolution imaging spectrometer (MERIS)/Envisat, and the medium-resolution spectral imager (MERSI)/FY-3A. Collocated Global Positioning System (GPS) PWV data from GPS network are employed as the reference data set because of its high precision in water vapor measurement. Relative difference and root-mean-square (rms) difference are computed for “Clear,” “Cloudy,” and “All Weather” categories for each NIR water vapor product. The results reveal that PWV derived from NIR sensors tend to underestimate the water vapor values with the existence of cloud, as NIR signals cannot penetrate the cloud. Under “Clear” condition, the overall rms for remote sensors are close to the expected goal accuracies, namely, with root-mean-square-error (RMSE) of 5.480 mm for MODIS/Terra, 3.708 mm for MERIS/Envisat, 8.644 mm for MERSI/FY-3A. MERIS/Envisat has the highest PWV retrieval accuracy, while the MODIS/Terra PWV product has the best correlation with GPS PWV ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> is 0.951). The MODIS/Terra tends to overestimate PWV value, while MERSI/FY-3A tends to underestimate the PWV value. Moreover, a comprehensive comparison of seasonal variation and wet/dry variation for each NIR PWV product is also performed in this study. The results indicate that the RMSE increases significantly under wet conditions (PWV larger than 20 mm) than under dry conditions (PWV smaller than 20 mm) for all remote sensing PWV products.
Year
DOI
Venue
2019
10.1109/TGRS.2019.2932847
IEEE Transactions on Geoscience and Remote Sensing
Keywords
Field
DocType
Global Positioning System,Satellites,MODIS,Remote sensing,Sensors,Spatial resolution,Cloud computing
Precipitable water vapor,Satellite,Near-infrared spectroscopy,Remote sensing,Communication channel,Mathematics
Journal
Volume
Issue
ISSN
57
12
0196-2892
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Jia He1106.67
Zhizhao Liu2219.03