Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques
Hyperspectral and multispectral imagery have been demonstrated to have a considerable potential for near real-time monitoring and mapping of grass quality indicators. The objective of this study was to evaluate the efficiency of remote sensing techniques for quantification of aboveground grass bioma...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/15/1835 |
| _version_ | 1819291463139196928 |
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| author | Mohammad Sadegh Askari Timothy McCarthy Aidan Magee Darren J. Murphy |
| author_facet | Mohammad Sadegh Askari Timothy McCarthy Aidan Magee Darren J. Murphy |
| author_sort | Mohammad Sadegh Askari |
| collection | DOAJ |
| description | Hyperspectral and multispectral imagery have been demonstrated to have a considerable potential for near real-time monitoring and mapping of grass quality indicators. The objective of this study was to evaluate the efficiency of remote sensing techniques for quantification of aboveground grass biomass (BM) and crude protein (CP) in a temperate European climate such as Ireland. The experiment was conducted on 64 plots and 53 paddocks with varying quantities of nitrogen applied. Hyperspectral imagery (HSI) and multispectral imagery (MSI) were analyzed to develop the prediction models. The MSI data used in this study were captured using an unmanned aircraft vehicle (UAV) and the satellite Sentinel-2, while the HSI data were obtained using a handheld hyperspectral camera. The prediction models were developed using partial least squares regression (PLSR) and stepwise multi-linear regression (MLR). Eventually, the spatial distribution of grass biomass over plots and paddocks was mapped to assess the within-field variability of grass quality metrics. An excellent accuracy was achieved for the prediction of BM and CP using HSI (RPD > 2.5 and <i>R</i><sup>2</sup> > 0.8), and a good accuracy was obtained via MSI-UAV (2 < RPD < 2.5 and <i>R</i><sup>2</sup> > 0.7) for the grass quality indicators. The accuracy of the models calculated using MSI-Sentinel-2 was reasonable for BM prediction and insufficient for CP estimation. The red-edge range of the wavelengths showed the maximum impact on the predictability of grass BM, and the NIR range had the greatest influence on the estimation of grass CP. Both the PLSR and MLR techniques were found to be sufficiently robust for spectral modelling of aboveground BM and CP. The PLSR yielded a slightly better model than MLR. This study suggested that remote sensing techniques can be used as a rapid and reliable approach for near real-time quantitative assessment of fresh grass quality under a temperate European climate. |
| first_indexed | 2024-12-24T03:39:02Z |
| format | Article |
| id | doaj.art-259a3f81f0704c4985b645add75c068f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T03:39:02Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-259a3f81f0704c4985b645add75c068f2022-12-21T17:16:58ZengMDPI AGRemote Sensing2072-42922019-08-011115183510.3390/rs11151835rs11151835Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing TechniquesMohammad Sadegh Askari0Timothy McCarthy1Aidan Magee2Darren J. Murphy3Department of Soil Science, Faculty of Agriculture, University of Zanjan, 45371-38791 Zanjan, IranNational Centre for Geocomputation, Maynooth University, Maynooth, Co. Kildare W23 F2K8, IrelandNational Centre for Geocomputation, Maynooth University, Maynooth, Co. Kildare W23 F2K8, IrelandTeagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Co. Cork P61 C996, IrelandHyperspectral and multispectral imagery have been demonstrated to have a considerable potential for near real-time monitoring and mapping of grass quality indicators. The objective of this study was to evaluate the efficiency of remote sensing techniques for quantification of aboveground grass biomass (BM) and crude protein (CP) in a temperate European climate such as Ireland. The experiment was conducted on 64 plots and 53 paddocks with varying quantities of nitrogen applied. Hyperspectral imagery (HSI) and multispectral imagery (MSI) were analyzed to develop the prediction models. The MSI data used in this study were captured using an unmanned aircraft vehicle (UAV) and the satellite Sentinel-2, while the HSI data were obtained using a handheld hyperspectral camera. The prediction models were developed using partial least squares regression (PLSR) and stepwise multi-linear regression (MLR). Eventually, the spatial distribution of grass biomass over plots and paddocks was mapped to assess the within-field variability of grass quality metrics. An excellent accuracy was achieved for the prediction of BM and CP using HSI (RPD > 2.5 and <i>R</i><sup>2</sup> > 0.8), and a good accuracy was obtained via MSI-UAV (2 < RPD < 2.5 and <i>R</i><sup>2</sup> > 0.7) for the grass quality indicators. The accuracy of the models calculated using MSI-Sentinel-2 was reasonable for BM prediction and insufficient for CP estimation. The red-edge range of the wavelengths showed the maximum impact on the predictability of grass BM, and the NIR range had the greatest influence on the estimation of grass CP. Both the PLSR and MLR techniques were found to be sufficiently robust for spectral modelling of aboveground BM and CP. The PLSR yielded a slightly better model than MLR. This study suggested that remote sensing techniques can be used as a rapid and reliable approach for near real-time quantitative assessment of fresh grass quality under a temperate European climate.https://www.mdpi.com/2072-4292/11/15/1835hyperspectralmultispectralfertilizationgrass biomasscrude protein |
| spellingShingle | Mohammad Sadegh Askari Timothy McCarthy Aidan Magee Darren J. Murphy Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques Remote Sensing hyperspectral multispectral fertilization grass biomass crude protein |
| title | Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques |
| title_full | Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques |
| title_fullStr | Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques |
| title_full_unstemmed | Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques |
| title_short | Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques |
| title_sort | evaluation of grass quality under different soil management scenarios using remote sensing techniques |
| topic | hyperspectral multispectral fertilization grass biomass crude protein |
| url | https://www.mdpi.com/2072-4292/11/15/1835 |
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