  The use of small unmanned aircraft systems (sUAS) for applications in the
field of precision agriculture has demonstrated the need to produce temporally
consistent imagery to allow for quantitative comparisons. In order for these
aerial images to be used to identify actual changes on the ground, conversion
of raw digital count to reflectance, or to an atmospherically normalized space,
needs to be carried out. This paper will describe an experiment that compares
the use of reflectance calibration panels, for use with the empirical line
method (ELM), against a newly proposed ratio of the target radiance and the
downwelling radiance, to predict the reflectance of known targets in the scene.
We propose that the use of an on-board downwelling light sensor (DLS) may
provide the sUAS remote sensing practitioner with an approach that does not
require the expensive and time consuming task of placing known reflectance
standards in the scene. Three calibration methods were tested in this study:
2-Point ELM, 1-Point ELM, and At-altitude Radiance Ratio (AARR). Our study
indicates that the traditional 2-Point ELM produces the lowest mean error in
band effective reflectance factor, 0.0165. The 1-Point ELM and AARR produce
mean errors of 0.0343 and 0.0287 respectively. A modeling of the proposed AARR
approach indicates that the technique has the potential to perform better than
the 2-Point ELM method, with a 0.0026 mean error in band effective reflectance
factor, indicating that this newly proposed technique may prove to be a viable
alternative with suitable on-board sensors.
