<p>Thermal and multispectral imagery can provide users with
insights into the water stress status and evapotranspiration demand of crops.
However, traditional platforms, such as satellites, for these thermal and
multispectral sensors are limited in their usefulness due to low spatial and
temporal resolution. Small unmanned aircraft system (UAS) have the potential to
have similar sensors installed and provide canopy temperature and reflectance
information at spatial and temporal resolutions more useful for crop
management; however, most of the existing research on the calibration or the estimation
of water status were established based on the satellite platforms either for
the sensors calibration or water status quantification. There is, therefore, a
need to develop methods specifically for UAS-mounted sensors. In this research,
a pixel-based calibration and an atmospheric correction method based on
in-field approximate blackbody sources were developed for an uncooled thermal
camera, and the higher accurate vegetative temperature acquired after
calibration was used as inputs to an algorithm developed for high-resolution
thermal imagery for calculating crop latent heat flux. At last, a thermal index
based on the Bowen ratio is proposed to quantify the water deficit stress in a
crop field, along with this, a method for plot-level analysis of various
vegetation and thermal indices have been demonstrated to illustrate its broad
application to genetic selection. The objective was to develop a workflow to
use high-resolution thermal and multispectral imagery to derive indices that
can quantify crops water status on a plot level which will facilitate the
research related to breeding selection.</p>
<p>The camera calibration method can effectively reduce the
root mean square error (RMSE) and variability of measurements. The pixel-based
thermal calibration method presented here was able to reduce the measurement
uncertainty across all the pixels in the images, thus improving the accuracy
and reducing the between-pixel variability of the measurements. During field
calibration, the RMSE values relative to ground reference targets for two
flights in 2017 were reduced from 6.36°C to 1.24°C and from 4.56°C to 1.32°C,
respectively. The latent heat flux estimation algorithm yields an RMSE of 65.23 W/m<sup>2</sup>
compared with the ground reference data acquired from porometer. The Bowen
ratio has a high correlation with drought conditions quantified using the soil
moisture index, stomatal conductance, and crop water stress index (CWSI), which
indicates the potential of this index to be used as a water deficit stress
indicator. The thermal and multispectral indices on a plot level displayed will
facilitate the breeding selection.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/19357967 |
| Date | 20 April 2022 |
| Creators | Yan Zhu (12238322) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/CROPS_WATER_STATUS_QUANTIFICATION_USING_THERMAL_AND_MULTISPECTRAL_SENSING_TECHNOLOGIES/19357967 |
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