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Depositordc.contributorRevill, Andrew
Funderdc.contributor.otherBBSRC - Biotechnology and Biological Sciences Research Councilen_UK
Funderdc.contributor.otherNERC - Natural Environment Research Councilen_UK
Spatial Coveragedc.coverage.spatialScottish Borders, Scotland, UKen_UK
Spatial Coveragedc.coverage.spatialGifford, East Lothian, Scotland, UK.en_UK
Spatial Coveragedc.coverage.spatialUKen
Spatial Coveragedc.coverage.spatialUNITED KINGDOMen
Time Perioddc.coverage.temporalstart=2018-01-01; end=2018-09-01; scheme=W3C-DTFen
Data Creatordc.creatorRevill, Andrew
Data Creatordc.creatorMacArthur, Alasdair
Data Creatordc.creatorWilliams, Mathew
Data Creatordc.creatorFlorence, Anna
Data Creatordc.creatorHoad, Stephen
Data Creatordc.creatorRees, Robert
Date Accessioneddc.date.accessioned2020-07-29T13:28:19Z
Date Availabledc.date.available2020-07-29T13:28:19Z
Citationdc.identifier.citationRevill, Andrew; MacArthur, Alasdair; Williams, Mathew; Florence, Anna; Hoad, Stephen; Rees, Robert. (2020). ATEC manuscript 2 - supporting data: "Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations", 2018 [dataset]. School of GeoSciences. University of Edinburgh.. https://doi.org/10.7488/ds/2889.en
Persistent Identifierdc.identifier.urihttp://hdl.handle.net/10283/3713
Persistent Identifierdc.identifier.urihttps://doi.org/10.7488/ds/2889
Dataset Description (abstract)dc.description.abstractLeaf 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 (CIred-edge) 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 CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge 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 R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 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.en_UK
Languagedc.language.isoengen_UK
Publisherdc.publisherSchool of GeoSciences. University of Edinburgh.en_UK
Relation (Is Referenced By)dc.relation.isreferencedbyhttps://doi.org/10.3390/rs12111843en_UK
Rightsdc.rightsCreative Commons Attribution 4.0 International Public Licenseen
Subjectdc.subjectSentinel-2en_UK
Subjectdc.subjectUAV multispectral dataen_UK
Subjectdc.subjectLAI retrievalen_UK
Subjectdc.subjectWinter wheaten_UK
Subjectdc.subjectMachine learningen_UK
Subject Classificationdc.subject.classificationPhysical Sciencesen_UK
Titledc.titleATEC manuscript 2 - supporting data: "Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations"en_UK
Typedc.typedataseten_UK

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