The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
Leaf Area Index (LAI) and chlorophyll content are strongly related to plant development and productivity. Spatial and temporal estimates of these variables are essential for efficient and precise crop management. The availability of open-access data from the European Space Agency’s (ESA) Sentinel-2...
| 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/2072-4292/11/17/2050 |
| _version_ | 1828129786379108352 |
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| author | Andrew Revill Anna Florence Alasdair MacArthur Stephen P. Hoad Robert M. Rees Mathew Williams |
| author_facet | Andrew Revill Anna Florence Alasdair MacArthur Stephen P. Hoad Robert M. Rees Mathew Williams |
| author_sort | Andrew Revill |
| collection | DOAJ |
| description | Leaf Area Index (LAI) and chlorophyll content are strongly related to plant development and productivity. Spatial and temporal estimates of these variables are essential for efficient and precise crop management. The availability of open-access data from the European Space Agency’s (ESA) Sentinel-2 satellite—delivering global coverage with an average 5-day revisit frequency at a spatial resolution of up to 10 metres—could provide estimates of these variables at unprecedented (i.e., sub-field) resolution. Using synthetic data, past research has demonstrated the potential of Sentinel-2 for estimating crop variables. Nonetheless, research involving a robust analysis of the Sentinel-2 bands for supporting agricultural applications is limited. We evaluated the potential of Sentinel-2 data for retrieving winter wheat LAI, leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). In coordination with destructive and non-destructive ground measurements, we acquired multispectral data from an Unmanned Aerial Vehicle (UAV)-mounted sensor measuring key Sentinel-2 spectral bands (443 to 865 nm). We applied Gaussian processes regression (GPR) machine learning to determine the most informative Sentinel-2 bands for retrieving each of the variables. We further evaluated the GPR model performance when propagating observation uncertainty. When applying the best-performing GPR models without propagating uncertainty, the retrievals had a high agreement with ground measurements—the mean R<sup>2</sup> and normalised root-mean-square error (NRMSE) were 0.89 and 8.8%, respectively. When propagating uncertainty, the mean R<sup>2</sup> and NRMSE were 0.82 and 11.9%, respectively. When accounting for measurement uncertainty in the estimation of LAI and CCC, the number of most informative Sentinel-2 bands was reduced from four to only two—the red-edge (705 nm) and near-infrared (865 nm) bands. This research demonstrates the value of the Sentinel-2 spectral characteristics for retrieving critical variables that can support more sustainable crop management practices. |
| first_indexed | 2024-04-11T16:25:08Z |
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| id | doaj.art-02bc071194014c6cbef0bab86a006fb4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T16:25:08Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-02bc071194014c6cbef0bab86a006fb42022-12-22T04:14:12ZengMDPI AGRemote Sensing2072-42922019-08-011117205010.3390/rs11172050rs11172050The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and DevelopmentAndrew Revill0Anna Florence1Alasdair MacArthur2Stephen P. Hoad3Robert M. Rees4Mathew Williams5School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh EH9 3FF, UKCrop & Soils Systems, Scotland’s Rural College, Edinburgh EH9 3JG, UKSchool of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh EH9 3FF, UKCrop & Soils Systems, Scotland’s Rural College, Edinburgh EH9 3JG, UKCrop & Soils Systems, Scotland’s Rural College, Edinburgh EH9 3JG, UKSchool of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh EH9 3FF, UKLeaf Area Index (LAI) and chlorophyll content are strongly related to plant development and productivity. Spatial and temporal estimates of these variables are essential for efficient and precise crop management. The availability of open-access data from the European Space Agency’s (ESA) Sentinel-2 satellite—delivering global coverage with an average 5-day revisit frequency at a spatial resolution of up to 10 metres—could provide estimates of these variables at unprecedented (i.e., sub-field) resolution. Using synthetic data, past research has demonstrated the potential of Sentinel-2 for estimating crop variables. Nonetheless, research involving a robust analysis of the Sentinel-2 bands for supporting agricultural applications is limited. We evaluated the potential of Sentinel-2 data for retrieving winter wheat LAI, leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). In coordination with destructive and non-destructive ground measurements, we acquired multispectral data from an Unmanned Aerial Vehicle (UAV)-mounted sensor measuring key Sentinel-2 spectral bands (443 to 865 nm). We applied Gaussian processes regression (GPR) machine learning to determine the most informative Sentinel-2 bands for retrieving each of the variables. We further evaluated the GPR model performance when propagating observation uncertainty. When applying the best-performing GPR models without propagating uncertainty, the retrievals had a high agreement with ground measurements—the mean R<sup>2</sup> and normalised root-mean-square error (NRMSE) were 0.89 and 8.8%, respectively. When propagating uncertainty, the mean R<sup>2</sup> and NRMSE were 0.82 and 11.9%, respectively. When accounting for measurement uncertainty in the estimation of LAI and CCC, the number of most informative Sentinel-2 bands was reduced from four to only two—the red-edge (705 nm) and near-infrared (865 nm) bands. This research demonstrates the value of the Sentinel-2 spectral characteristics for retrieving critical variables that can support more sustainable crop management practices.https://www.mdpi.com/2072-4292/11/17/2050Sentinel-2 spectral analysisGaussian processes regressionmachine learningred-edge bandwinter wheat assessmentvegetation parameter retrieval |
| spellingShingle | Andrew Revill Anna Florence Alasdair MacArthur Stephen P. Hoad Robert M. Rees Mathew Williams The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development Remote Sensing Sentinel-2 spectral analysis Gaussian processes regression machine learning red-edge band winter wheat assessment vegetation parameter retrieval |
| title | The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development |
| title_full | The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development |
| title_fullStr | The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development |
| title_full_unstemmed | The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development |
| title_short | The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development |
| title_sort | value of sentinel 2 spectral bands for the assessment of winter wheat growth and development |
| topic | Sentinel-2 spectral analysis Gaussian processes regression machine learning red-edge band winter wheat assessment vegetation parameter retrieval |
| url | https://www.mdpi.com/2072-4292/11/17/2050 |
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