Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations
Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibrat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/11/1843 |
| _version_ | 1797565912754159616 |
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| author | Andrew Revill Anna Florence Alasdair MacArthur Stephen Hoad Robert Rees Mathew Williams |
| author_facet | Andrew Revill Anna Florence Alasdair MacArthur Stephen Hoad Robert Rees Mathew Williams |
| author_sort | Andrew Revill |
| collection | DOAJ |
| description | Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CI<sub>red-edge</sub>) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CI<sub>red-edge</sub> values increased with growth stage—R<sup>2</sup> ranged from 0.32 (stem elongation) to 0.75 (milk development). The CI<sub>red-edge</sub> variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R<sup>2</sup> = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R<sup>2</sup> = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types. |
| first_indexed | 2024-03-10T19:19:37Z |
| format | Article |
| id | doaj.art-2c699ac308ba46d395acd7b3c99fc01d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:19:37Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2c699ac308ba46d395acd7b3c99fc01d2023-11-20T03:04:29ZengMDPI AGRemote Sensing2072-42922020-06-011211184310.3390/rs12111843Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV ObservationsAndrew Revill0Anna Florence1Alasdair MacArthur2Stephen Hoad3Robert 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) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CI<sub>red-edge</sub>) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CI<sub>red-edge</sub> values increased with growth stage—R<sup>2</sup> ranged from 0.32 (stem elongation) to 0.75 (milk development). The CI<sub>red-edge</sub> variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R<sup>2</sup> = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R<sup>2</sup> = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.https://www.mdpi.com/2072-4292/12/11/1843Sentinel-2LAI retrievalUAV multispectral datawinter wheatGaussian processes regression |
| spellingShingle | Andrew Revill Anna Florence Alasdair MacArthur Stephen Hoad Robert Rees Mathew Williams Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations Remote Sensing Sentinel-2 LAI retrieval UAV multispectral data winter wheat Gaussian processes regression |
| title | Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations |
| title_full | Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations |
| title_fullStr | Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations |
| title_full_unstemmed | Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations |
| title_short | Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations |
| title_sort | quantifying uncertainty and bridging the scaling gap in the retrieval of leaf area index by coupling sentinel 2 and uav observations |
| topic | Sentinel-2 LAI retrieval UAV multispectral data winter wheat Gaussian processes regression |
| url | https://www.mdpi.com/2072-4292/12/11/1843 |
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