Publikationen
Monitoring Glyphosate-Based Herbicide Treatment Using Sentinel-2 Time Series—A Proof-of-Principle
In this paper we aim to show a proof-of-principle approach to detect and monitor weed management using glyphosate-based herbicides in agricultural practices. In a case study in Germany, we demonstrate the application of Sentinel-2 multispectral time-series data. Spectral broadband vegetation indices were analysed to observe vegetation traits and weed damage arising from herbicide-based management. The approach has been validated with stakeholder information about herbicide treatment using commercial products. As a result, broadband NDVI calculated from Sentinel-2 data showed explicit feedback after the glyphosate-based herbicide treatment. Vegetation damage could be detected after just two days following of glyphosate-based herbicide treatment. This trend was observed in three different application scenarios, i.e., during growing stage, before harvest and after harvest. The findings of the study demonstrate the feasibility of satellite based broadband NDVI data for the detection of glyphosate-based herbicide treatment and, e.g., the monitoring of latency to harvesting. The presented results can be used to implement monitoring concepts to provide the necessary transparency about weed treatment in agricultural practices and to support environmental monitoring.
Herausgeber
- Pause, Marion
- Csaplovics, Elmar
- Marrs, Christopher
Typ der Veröffentlichung
E-Publ. Buchherausgeberschaft
Schlagwörter
remote sensing, agricultural management, Sentinel-2
Verlag
MDPI - Multidisciplinary Digital Publishing Institute
Verlagsort
Journal: Remote Sensing
Jahrgang/Erscheinungsjahr
2019
Vermerk
Neuerscheinung
Band/Vol.
11
Heftnummer/Issue
21
Seiten
14
Referiert
Nein
Open Access
Nein
ISSN
2072-4292
Url/Urn/Doi
10.3390/rs11212541
Zugeordnete Forschungsschwerpunkte
- Integration hoch- und höchstauflösender Satellitenbilder in multisensorale Bilddatensätze und in GIS
- Aufbau integrierter geographischer Informationssysteme -Modellierung hybrider Datenbestände
- Umweltinformationssysteme
Berichtsjahr
2019
Export