Hongxiao Jin
Researcher
Drone-based hyperspectral and thermal imagery for quantifying upland rice productivity and water use efficiency after biochar application
Author
Summary, in English
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.
Department/s
- Dept of Physical Geography and Ecosystem Science
Publishing year
2021-05-02
Language
English
Publication/Series
Remote Sensing
Volume
13
Issue
10
Document type
Journal article
Publisher
MDPI AG
Topic
- Physical Geography
- Remote Sensing
Keywords
- Biochar
- Gross primary productivity (GPP)
- Hyperspectral and thermal imagery
- Unmanned aerial vehicle (UAV)
- Upland rice
- Water use efficiency (WUE)
Status
Published
ISBN/ISSN/Other
- ISSN: 2072-4292