Harmful Cyanobacterial Bloom Detection using Deep Learning and Sentinel-2 Imagery

Schloer, Conrad and Hänsch, Ronny (2025) Harmful Cyanobacterial Bloom Detection using Deep Learning and Sentinel-2 Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, pp. 1-11. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2025.3629586. ISSN 1939-1404.

PDF - Published version
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Item URL in elib:

https://elib.dlr.de/219745/

Document Type:

Article

Title:

Harmful Cyanobacterial Bloom Detection using Deep Learning and Sentinel-2 Imagery

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Schloer, Conrad	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Hänsch, Ronny	UNSPECIFIED	https://orcid.org/0000-0002-2936-6765	UNSPECIFIED

Date:

2025

Journal or Publication Title:

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Refereed publication:

Yes

Open Access:

Yes

Gold Open Access:

Yes

In SCOPUS:

Yes

In ISI Web of Science:

Yes

DOI:

10.1109/JSTARS.2025.3629586

Page Range:

pp. 1-11

Publisher:

IEEE - Institute of Electrical and Electronics Engineers

ISSN:

1939-1404

Status:

Published

Keywords:

Cyanobacteria;Spatial resolution;Training;Accuracy;Monitoring;Deep learning;Satellite images;Lakes;Sea measurements;Data models;Machine Learning;Cyanobacteria;Harmful Algae Blooms;Multispectral Imagery;Satellite Imagery

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 - Artificial Intelligence

Location:

Oberpfaffenhofen

Institutes and Institutions:

Microwaves and Radar Institute > SAR Technology

Deposited By:

Hänsch, Ronny

Deposited On:

26 Nov 2025 16:29

Last Modified:

01 Dec 2025 10:35

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