Soil-Permittivity Estimation Under Grassland Using Machine-Learning and Polarimetric Decomposition Techniques

Hänsch, Ronny and Jagdhuber, Thomas and Fersch, Benjamin (2020) Soil-Permittivity Estimation Under Grassland Using Machine-Learning and Polarimetric Decomposition Techniques. IEEE Transactions on Geoscience and Remote Sensing, pp. 1-11. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2020.3010104. ISSN 0196-2892.

Full text not available from this repository.

Item URL in elib:

https://elib.dlr.de/137906/

Document Type:

Article

Title:

Soil-Permittivity Estimation Under Grassland Using Machine-Learning and Polarimetric Decomposition Techniques

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD
Hänsch, Ronny	Ronny.Haensch (at) dlr.de	UNSPECIFIED
Jagdhuber, Thomas	Thomas.Jagdhuber (at) dlr.de	https://orcid.org/0000-0002-1760-2425
Fersch, Benjamin	KIT	UNSPECIFIED

Date:

6 August 2020

Journal or Publication Title:

IEEE Transactions on Geoscience and Remote Sensing

Refereed publication:

Yes

Open Access:

Gold Open Access:

In SCOPUS:

Yes

In ISI Web of Science:

Yes

DOI :

10.1109/TGRS.2020.3010104

Page Range:

pp. 1-11

Publisher:

IEEE - Institute of Electrical and Electronics Engineers

ISSN:

0196-2892

Status:

Published

Keywords:

Permittivity, Synthetic aperture radar, Maximum likelihood estimation, Soil moisture, Scattering

HGF - Research field:

Aeronautics, Space and Transport

HGF - Program:

Space

HGF - Program Themes:

Earth Observation

DLR - Research area:

Raumfahrt

DLR - Program:

R EO - Earth Observation

DLR - Research theme (Project):

R - Security-relevant Earth Observation

Location:

Oberpfaffenhofen

Institutes and Institutions:

Microwaves and Radar Institute > Reconnaissance and Security
Microwaves and Radar Institute > SAR Technology

Deposited By:

Jagdhuber, Dr Thomas

Deposited On:

25 Nov 2020 17:40

Last Modified:

25 Nov 2020 17:40

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