A Back Propagation Neural Network-Based Algorithm for Retrieving All-Weather Precipitable Water Vapor From MODIS NIR Measurements-Reference-Cited by-同舟云学术

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A Back Propagation Neural Network-Based Algorithm for Retrieving All-Weather Precipitable Water Vapor From MODIS NIR Measurements

Published:2022 Issue: Volume:60 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Xu Jiafei¹^ORCID,Liu Zhizhao¹^ORCID

Affiliation:

1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

Funder

Research Grants Council of the Hong Kong Special Administrative Region, China

Emerging Frontier Area (EFA) Scheme of Research Institute for Sustainable Urban Development (RISUD) of The Hong Kong Polytechnic University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09939035.pdf?arnumber=9939035

Reference51 articles.

1. Refining MODIS NIR Atmospheric Water Vapor Retrieval Algorithm Using GPS-Derived Water Vapor Data

2. Water Vapor Retrieval From MODIS NIR Channels Using Ground-Based GPS Data

3. MODIS: advanced facility instrument for studies of the Earth as a system

4. Retrieving Liquid Wat0er Path and Precipitable Water Vapor From the Atmospheric Radiation Measurement (ARM) Microwave Radiometers

5. Operational Retrieval of Atmospheric Temperature, Moisture, and Ozone from MODIS Infrared Radiances

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