Integrating InSAR and deep-learning for modeling and predicting subsidence over the adjacent area of Lake Urmia, Iran-Reference-Cited by-同舟云学术

Integrating InSAR and deep-learning for modeling and predicting subsidence over the adjacent area of Lake Urmia, Iran

Published:2021-10-19 Issue:8 Volume:58 Page:1413-1433
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Radman Ali¹,Akhoondzadeh Mehdi¹^ORCID,Hosseiny Benyamin¹^ORCID

Affiliation:

1. Photogrammetry and Remote Sensing Department, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2021.1991689

Reference60 articles.

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3. Beaudoing, H., and M. Rodell 2020b. “GLDAS VIC Land Surface Model L4 Monthly 1.0 X 1.0 Degree V2.1, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC).” NASA/GSFC/HSL. doi:10.5067/VWTH7S6218SG.

4. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms

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