Applying the New MODIS-Based Precipitable Water Vapor Retrieval Algorithm Developed in the North Hemisphere to the South Hemisphere

Jia He, Zhizhao Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

A new algorithm to retrieve water vapor from Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) channels using the ensemble-based empirical regression model, which was developed based on the North Hemisphere (western North America) data, was for the first time applied and validated to the South Hemisphere, mainly the Australia and its surrounding regions. By employing the empirical regression algorithm to retrieve water vapor from MODIS Level 1 reflectance data, the wet bias of MODIS product has been significantly reduced. Validation against global positioning system (GPS) water vapor observations over the period January 1, 2017 to December 31, 2019 in and around Australia shows that the root mean square error (RMSE) of water vapor data obtained from MODIS/Terra has reduced by 58.53% from 5.712 to 2.369 mm when using two-channel ratio transmittance and has reduced by 56.14% to 2.505 mm when using three-channel ratio transmittance. For the data obtained from MODIS/Aqua, the RMSE has reduced by 49.17% from 5.170 to 2.628 mm using two-channel ratio transmittance and has reduced by 46.60% to 2.761 mm using three-channel ratio transmittance, respectively. In addition, validations of the retrieved water vapor results over such a large research area (0°-55°S in latitude and 95°-180°E in longitudes) also show no temporal or spatial dependence, implying that the algorithm is homogeneous, accurate, and robust.

Original languageEnglish
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume60
DOIs
Publication statusPublished - 2022

Keywords

  • Australia
  • Moderate Resolution Imaging Spectroradiometer (MODIS)
  • global positioning system (GPS)
  • water vapor

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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