Drone-based hyperspectral and thermal imagery for quantifying upland rice productivity and water use efficiency after biochar application
(2021) In Remote Sensing 13(10).- Abstract
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... (More)
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.
(Less)
- author
- organization
- publishing date
- 2021-05-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Biochar, Gross primary productivity (GPP), Hyperspectral and thermal imagery, Unmanned aerial vehicle (UAV), Upland rice, Water use efficiency (WUE)
- in
- Remote Sensing
- volume
- 13
- issue
- 10
- article number
- 1866
- publisher
- MDPI AG
- external identifiers
-
- scopus:85106474512
- ISSN
- 2072-4292
- DOI
- 10.3390/rs13101866
- language
- English
- LU publication?
- yes
- additional info
- Funding Information: This research was funded by the Joint Call of the Water Joint Programming Initiative (Water JPI) and the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI) of the European Union and partner countries via the Agricultural Water Innovations in the Tropics (AgWIT) project. H.J. is jointly funded by Sino-Danish Center for Education and Research (SDC), Denmark. S.M. acknowledges support from the Swedish Research Council (Vetenskapsr?det), Formas, and Sida (VR 2016-06313), and by the Bolin Centre for Climate Research (Research Area 7). Funding Information: Funding: This research was funded by the Joint Call of the Water Joint Programming Initiative (Water JPI) and the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI) of the European Union and partner countries via the Agricultural Water Innovations in the Tropics (AgWIT) project. H.J. is jointly funded by Sino-Danish Center for Education and Research (SDC), Denmark. S.M. acknowledges support from the Swedish Research Council (Vetenskapsrådet), Formas, and Sida (VR 2016-06313), and by the Bolin Centre for Climate Research (Research Area 7). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- id
- e2d43bf4-3aa8-4762-a4f6-e7d863d64246
- date added to LUP
- 2021-12-28 13:23:51
- date last changed
- 2023-02-21 10:29:46
@article{e2d43bf4-3aa8-4762-a4f6-e7d863d64246,
abstract = {{<p>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.</p>}},
author = {{Jin, Hongxiao and Köppl, Christian Josef and Fischer, Benjamin M.C. and Rojas-Conejo, Johanna and Johnson, Mark S. and Morillas, Laura and Lyon, Steve W. and Durán-Quesada, Ana M. and Suárez-Serrano, Andrea and Manzoni, Stefano and Garcia, Monica}},
issn = {{2072-4292}},
keywords = {{Biochar; Gross primary productivity (GPP); Hyperspectral and thermal imagery; Unmanned aerial vehicle (UAV); Upland rice; Water use efficiency (WUE)}},
language = {{eng}},
month = {{05}},
number = {{10}},
publisher = {{MDPI AG}},
series = {{Remote Sensing}},
title = {{Drone-based hyperspectral and thermal imagery for quantifying upland rice productivity and water use efficiency after biochar application}},
url = {{http://dx.doi.org/10.3390/rs13101866}},
doi = {{10.3390/rs13101866}},
volume = {{13}},
year = {{2021}},
}