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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/21/2541 |
| _version_ | 1818958093171556352 |
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| author | Marion Pause Filip Raasch Christopher Marrs Elmar Csaplovics |
| author_facet | Marion Pause Filip Raasch Christopher Marrs Elmar Csaplovics |
| author_sort | Marion Pause |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T11:20:16Z |
| format | Article |
| id | doaj.art-940a932a0cb848ed8a4a7f2a3e63e136 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T11:20:16Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-940a932a0cb848ed8a4a7f2a3e63e1362022-12-21T19:42:31ZengMDPI AGRemote Sensing2072-42922019-10-011121254110.3390/rs11212541rs11212541Monitoring Glyphosate-Based Herbicide Treatment Using Sentinel-2 Time Series—A Proof-of-PrincipleMarion Pause0Filip Raasch1Christopher Marrs2Elmar Csaplovics3Faculty of Environmental Sciences, Technical University Dresden, 01062 Dresden, GermanyFaculty of Environmental Sciences, Technical University Dresden, 01062 Dresden, GermanyFaculty of Environmental Sciences, Technical University Dresden, 01062 Dresden, GermanyFaculty of Environmental Sciences, Technical University Dresden, 01062 Dresden, GermanyIn 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.https://www.mdpi.com/2072-4292/11/21/2541ndviglyphosateherbicidesentinel-2broadband spectral indicesvegetation traitsprecision farmingtime-seriesroundup<sup>®</sup>insectsbiodiversitysoil health monitoring |
| spellingShingle | Marion Pause Filip Raasch Christopher Marrs Elmar Csaplovics Monitoring Glyphosate-Based Herbicide Treatment Using Sentinel-2 Time Series—A Proof-of-Principle Remote Sensing ndvi glyphosate herbicide sentinel-2 broadband spectral indices vegetation traits precision farming time-series roundup<sup>®</sup> insects biodiversity soil health monitoring |
| title | Monitoring Glyphosate-Based Herbicide Treatment Using Sentinel-2 Time Series—A Proof-of-Principle |
| title_full | Monitoring Glyphosate-Based Herbicide Treatment Using Sentinel-2 Time Series—A Proof-of-Principle |
| title_fullStr | Monitoring Glyphosate-Based Herbicide Treatment Using Sentinel-2 Time Series—A Proof-of-Principle |
| title_full_unstemmed | Monitoring Glyphosate-Based Herbicide Treatment Using Sentinel-2 Time Series—A Proof-of-Principle |
| title_short | Monitoring Glyphosate-Based Herbicide Treatment Using Sentinel-2 Time Series—A Proof-of-Principle |
| title_sort | monitoring glyphosate based herbicide treatment using sentinel 2 time series a proof of principle |
| topic | ndvi glyphosate herbicide sentinel-2 broadband spectral indices vegetation traits precision farming time-series roundup<sup>®</sup> insects biodiversity soil health monitoring |
| url | https://www.mdpi.com/2072-4292/11/21/2541 |
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