This paper describes the pipeline used to produce calibrated hyperspectral images from the two hyperspectral cameras deployed under field conditions. The pipeline implements radiometric calibration, reflectance calculation, bidirectional reflectance distribution function (BRDF) correction, soil and shadow masking, and image quality assessment. This was a true tour de force!
Calibrating these cameras was one of the project's most challenging efforts - the light environment is changing, and impacted not only by the sun but also by the big white box that both reflects and shades, the complex canopy, the slow moving camera and timing of calibration, not to mention broken cameras and the variety of radiometers and calibration targets that were put to use. The issues on GitHub include an interesting record of the discussions, challenges, and iterations of different approaches that we took over the years.
Sagan, Vasit, et al. "Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data." IEEE Transactions on Geoscience and Remote Sensing (2021).
