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Article

Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application

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Department of Environmental Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
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Sino-Danish Center for Education and Research (SDC), 8000 Aarhus C, Denmark
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Department of Physical Geography and Ecosystem Science, Lund University, 22362 Lund, Sweden
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Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden
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Department of Earth Sciences, Uppsala University, 75105 Uppsala, Sweden
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Water Resources Center for Central America and the Caribbean (HIDROCEC), Universidad Nacional de Costa Rica, 50101 Liberia, Costa Rica
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Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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Centre for Sustainable Food Systems, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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School of Environment and Natural Resources, Ohio State University, Ohio, OH 43210, USA
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Atmospheric, Oceanic and Planetary Physics Department & Climate System Observation Laboratory, School of Physics, University of Costa Rica, 11501-2060 San José, Costa Rica
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Center for Geophysical Research, University of Costa Rica, 11501-2060 San José, Costa Rica
*
Author to whom correspondence should be addressed.
Academic Editor: Juan Manuel Sánchez Tomás
Remote Sens. 2021, 13(10), 1866; https://doi.org/10.3390/rs13101866
Received: 8 April 2021 / Revised: 5 May 2021 / Accepted: 8 May 2021 / Published: 11 May 2021
(This article belongs to the Special Issue Ecohydrological Remote Sensing)
Miniature hyperspectral and thermal cameras onboard lightweight unmanned aerial vehicles (UAV) bring new opportunities for monitoring land surface variables at unprecedented fine spatial resolution with acceptable accuracy. This research applies hyperspectral and thermal imagery from a drone to quantify upland rice productivity and water use efficiency (WUE) after biochar application in Costa Rica. The field flights were conducted over two experimental groups with bamboo biochar (BC1) and sugarcane biochar (BC2) amendments and one control (C) group without biochar application. Rice canopy biophysical variables were estimated by inverting a canopy radiative transfer model on hyperspectral reflectance. Variations in gross primary productivity (GPP) and WUE across treatments were estimated using light-use efficiency and WUE models respectively from the normalized difference vegetation index (NDVI), canopy chlorophyll content (CCC), and evapotranspiration rate. We found that GPP was increased by 41.9 ± 3.4% in BC1 and 17.5 ± 3.4% in BC2 versus C, which may be explained by higher soil moisture after biochar application, and consequently significantly higher WUEs by 40.8 ± 3.5% in BC1 and 13.4 ± 3.5% in BC2 compared to C. This study demonstrated the use of hyperspectral and thermal imagery from a drone to quantify biochar effects on dry cropland by integrating ground measurements and physical models. View Full-Text
Keywords: unmanned aerial vehicle (UAV); hyperspectral and thermal imagery; gross primary productivity (GPP); water use efficiency (WUE); biochar; upland rice unmanned aerial vehicle (UAV); hyperspectral and thermal imagery; gross primary productivity (GPP); water use efficiency (WUE); biochar; upland rice
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MDPI and ACS Style

Jin, H.; Köppl, C.J.; Fischer, B.M.C.; Rojas-Conejo, J.; Johnson, M.S.; Morillas, L.; Lyon, S.W.; Durán-Quesada, A.M.; Suárez-Serrano, A.; Manzoni, S.; Garcia, M. Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application. Remote Sens. 2021, 13, 1866. https://doi.org/10.3390/rs13101866

AMA Style

Jin H, Köppl CJ, Fischer BMC, Rojas-Conejo J, Johnson MS, Morillas L, Lyon SW, Durán-Quesada AM, Suárez-Serrano A, Manzoni S, Garcia M. Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application. Remote Sensing. 2021; 13(10):1866. https://doi.org/10.3390/rs13101866

Chicago/Turabian Style

Jin, Hongxiao, Christian J. Köppl, Benjamin M.C. Fischer, Johanna Rojas-Conejo, Mark S. Johnson, Laura Morillas, Steve W. Lyon, Ana M. Durán-Quesada, Andrea Suárez-Serrano, Stefano Manzoni, and Monica Garcia. 2021. "Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application" Remote Sensing 13, no. 10: 1866. https://doi.org/10.3390/rs13101866

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