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Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions

1
Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall TR10 9FE, UK
2
Remote Sensing of Environmental Dynamics Laboratory, Department of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
3
Institute for Electromagnetic Sensing of the Environment, Italian National Research Council (IREA-CNR), Via Bassini 15, 20133 Milan, Italy
4
Institute of BioEconomy, Italian National Research Council (IBE-CNR), Via Caproni 8, 50145 Firenze, Italy
5
Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 514; https://doi.org/10.3390/rs12030514
Received: 9 December 2019 / Revised: 2 February 2020 / Accepted: 3 February 2020 / Published: 5 February 2020
Compact multi-spectral sensors that can be mounted on lightweight drones are now widely available and applied within the geo- and environmental sciences. However; the spatial consistency and radiometric quality of data from such sensors is relatively poorly explored beyond the lab; in operational settings and against other sensors. This study explores the extent to which accurate hemispherical-conical reflectance factors (HCRF) and vegetation indices (specifically: normalised difference vegetation index (NDVI) and chlorophyll red-edge index (CHL)) can be derived from a low-cost multispectral drone-mounted sensor (Parrot Sequoia). The drone datasets were assessed using reference panels and a high quality 1 m resolution reference dataset collected near-simultaneously by an airborne imaging spectrometer (HyPlant). Relative errors relating to the radiometric calibration to HCRF values were in the 4 to 15% range whereas deviations assessed for a maize field case study were larger (5 to 28%). Drone-derived vegetation indices showed relatively good agreement for NDVI with both HyPlant and Sentinel 2 products (R2 = 0.91). The HCRF; NDVI and CHL products from the Sequoia showed bias for high and low reflective surfaces. The spatial consistency of the products was high with minimal view angle effects in visible bands. In summary; compact multi-spectral sensors such as the Parrot Sequoia show good potential for use in index-based vegetation monitoring studies across scales but care must be taken when assuming derived HCRF to represent the true optical properties of the imaged surface. View Full-Text
Keywords: UAV; drone; multispectral; calibration; reflectance; NDVI; chlorophyll; vegetation; maize; Parrot Sequoia UAV; drone; multispectral; calibration; reflectance; NDVI; chlorophyll; vegetation; maize; Parrot Sequoia
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MDPI and ACS Style

Fawcett, D.; Panigada, C.; Tagliabue, G.; Boschetti, M.; Celesti, M.; Evdokimov, A.; Biriukova, K.; Colombo, R.; Miglietta, F.; Rascher, U.; Anderson, K. Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions. Remote Sens. 2020, 12, 514. https://doi.org/10.3390/rs12030514

AMA Style

Fawcett D, Panigada C, Tagliabue G, Boschetti M, Celesti M, Evdokimov A, Biriukova K, Colombo R, Miglietta F, Rascher U, Anderson K. Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions. Remote Sensing. 2020; 12(3):514. https://doi.org/10.3390/rs12030514

Chicago/Turabian Style

Fawcett, Dominic; Panigada, Cinzia; Tagliabue, Giulia; Boschetti, Mirco; Celesti, Marco; Evdokimov, Anton; Biriukova, Khelvi; Colombo, Roberto; Miglietta, Franco; Rascher, Uwe; Anderson, Karen. 2020. "Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions" Remote Sens. 12, no. 3: 514. https://doi.org/10.3390/rs12030514

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