Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application
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 quan...
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| Format: | Article |
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MDPI AG
2021-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/10/1866 |
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| author | Hongxiao Jin Christian Josef 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 Monica Garcia |
| author_facet | Hongxiao Jin Christian Josef 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 Monica Garcia |
| author_sort | Hongxiao Jin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:32:44Z |
| format | Article |
| id | doaj.art-78c1cf090bd3454abc51b4474e34464d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:32:44Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-78c1cf090bd3454abc51b4474e34464d2023-11-21T19:08:15ZengMDPI AGRemote Sensing2072-42922021-05-011310186610.3390/rs13101866Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar ApplicationHongxiao Jin0Christian Josef Köppl1Benjamin M. C. Fischer2Johanna Rojas-Conejo3Mark S. Johnson4Laura Morillas5Steve W. Lyon6Ana M. Durán-Quesada7Andrea Suárez-Serrano8Stefano Manzoni9Monica Garcia10Department of Environmental Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, DenmarkDepartment of Environmental Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, DenmarkDepartment of Physical Geography and Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, SwedenWater Resources Center for Central America and the Caribbean (HIDROCEC), Universidad Nacional de Costa Rica, 50101 Liberia, Costa RicaDepartment of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaCentre for Sustainable Food Systems, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaDepartment of Physical Geography and Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, SwedenAtmospheric, Oceanic and Planetary Physics Department & Climate System Observation Laboratory, School of Physics, University of Costa Rica, 11501-2060 San José, Costa RicaWater Resources Center for Central America and the Caribbean (HIDROCEC), Universidad Nacional de Costa Rica, 50101 Liberia, Costa RicaDepartment of Physical Geography and Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, SwedenDepartment of Environmental Engineering, Technical University of Denmark, 2800 Kgs. Lyngby, DenmarkMiniature 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.https://www.mdpi.com/2072-4292/13/10/1866unmanned aerial vehicle (UAV)hyperspectral and thermal imagerygross primary productivity (GPP)water use efficiency (WUE)biocharupland rice |
| spellingShingle | Hongxiao Jin Christian Josef 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 Monica Garcia Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application Remote Sensing unmanned aerial vehicle (UAV) hyperspectral and thermal imagery gross primary productivity (GPP) water use efficiency (WUE) biochar upland rice |
| title | Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application |
| title_full | Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application |
| title_fullStr | Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application |
| title_full_unstemmed | Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application |
| title_short | Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application |
| title_sort | drone based hyperspectral and thermal imagery for quantifying upland rice productivity and water use efficiency after biochar application |
| topic | unmanned aerial vehicle (UAV) hyperspectral and thermal imagery gross primary productivity (GPP) water use efficiency (WUE) biochar upland rice |
| url | https://www.mdpi.com/2072-4292/13/10/1866 |
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