Exploring the combination of remote sensing and machine learning to correct Cosmic Ray Neutron Sensing soil moisture signals for variable biomass effects

Knieß, Jakob (2026) Exploring the combination of remote sensing and machine learning to correct Cosmic Ray Neutron Sensing soil moisture signals for variable biomass effects. Master's, University of Augsburg.

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Item URL in elib:

https://elib.dlr.de/215293/

Document Type:

Thesis (Master's)

Title:

Exploring the combination of remote sensing and machine learning to correct Cosmic Ray Neutron Sensing soil moisture signals for variable biomass effects

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Knieß, Jakob	jakob.kniess (at) uni-a.de	UNSPECIFIED	UNSPECIFIED

DLR Supervisors:

Contribution	DLR Supervisor	Institution or E-Mail	DLR Supervisor's ORCID iD
Thesis advisor	Fluhrer, Anke	Anke.Fluhrer (at) dlr.de	https://orcid.org/0000-0002-1188-5313
Thesis advisor	Jagdhuber, Thomas	Thomas.Jagdhuber (at) dlr.de	https://orcid.org/0000-0002-1760-2425

Date:

2026

Open Access:

Status:

Accepted

Keywords:

Remote Sensing, Sentinel-1, Biomass correction, CRNS, soil moisture

Institution:

University of Augsburg

Department:

Faculty of Applied Computer Science

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

Deposited By:

Fluhrer, Anke

Deposited On:

21 Jul 2025 09:16

Last Modified:

02 Dec 2025 10:01

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