Evidence map›Paper›PMID 34435080›Full record

ArticleEarth and space science (Hoboken, N.J.)2021

Permafrost Dynamics Observatory-Part I: Postprocessing and Calibration Methods of UAVSAR L-Band InSAR Data for Seasonal Subsidence Estimation.

Roger J Michaelides, Richard H Chen, Yuhuan Zhao, Kevin Schaefer, Andrew D Parsekian, Taylor Sullivan, Mahta Moghaddam, Howard A Zebker, Lin Liu, Xingyu Xu and 1 more

Abstract read
In one paragraph

Article in Earth and space science (Hoboken, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Roger J MichaelidesDepartment of Geophysics Colorado School of Mines Golden CO USA.ORCID https://orcid.org/0000-0002-7577-6829
Richard H ChenJet Propulsion Laboratory California Institute of Technology Pasadena CA USA.ORCID https://orcid.org/0000-0001-8571-689X
Yuhuan ZhaoViterbi School of Engineering University of Southern California Los Angeles CA USA.
Kevin SchaeferNational Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado at Boulder Boulder CO USA.ORCID https://orcid.org/0000-0002-5444-9917
Andrew D ParsekianDepartment of Geology and Geophysics University of Wyoming Laramie WY USA.ORCID https://orcid.org/0000-0001-5072-9818
Taylor SullivanDepartment of Geology and Geophysics University of Wyoming Laramie WY USA.ORCID https://orcid.org/0000-0001-6293-3015
Mahta MoghaddamViterbi School of Engineering University of Southern California Los Angeles CA USA.
Howard A ZebkerDepartment of Geophysics Stanford University Stanford CA USA.
Lin LiuEarth System Science Programme Faculty of Science The Chinese University of Hong Kong Hong Kong China.
Xingyu XuEarth System Science Programme Faculty of Science The Chinese University of Hong Kong Hong Kong China.ORCID https://orcid.org/0000-0003-4651-4480
Jingyi ChenDepartment of Aerospace Engineering and Engineering Mechanics University of Texas Austin TX USA.ORCID https://orcid.org/0000-0001-8493-6938

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interferometric synthetic aperture radar (InSAR) has been used to quantify a range of surface and near surface physical properties in permafrost landscapes. Most previous InSAR studies have utilized spaceborne InSAR platforms, but InSAR datasets over permafrost landscapes collected from airborne platforms have been steadily growing in recent years. Most existing algorithms dedicated toward retrieval of permafrost physical properties were originally developed for spaceborne InSAR platforms. In this study, which is the first in a two part series, we introduce a series of calibration techniques developed to apply a novel joint retrieval algorithm for permafrost active layer thickness retrieval to an airborne InSAR dataset acquired in 2017 by NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar over Alaska and Western Canada. We demonstrate how InSAR measurement uncertainties are mitigated by these calibration methods and quantify remaining measurement uncertainties with a novel method of modeling interferometric phase uncertainty using a Gaussian mixture model. Finally, we discuss the impact of native SAR resolution on InSAR measurements, the limitation of using few interferograms per retrieval, and the implications of our findings for cross-comparison of airborne and spaceborne InSAR datasets acquired over Arctic regions underlain by permafrost.

Indexed as

active layer thicknessArctic and borealInSARpermafrostsynthetic aperture radarUAVSAR

Identifiers

PMID34435080
PMCPMC8365676

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.