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Technical Note

ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data

by
Kathryn Elmer
1,
Raymond J. Soffer
2,
J. Pablo Arroyo-Mora
2 and
Margaret Kalacska
1,*
1
Applied Remote Sensing Lab, McGill University, Montreal, QC H3A-0B9, Canada
2
Flight Research Laboratory, National Research Council of Canada, Ottawa, ON K1A-0R6, Canada
*
Author to whom correspondence should be addressed.
Submission received: 23 August 2020 / Revised: 3 October 2020 / Accepted: 4 October 2020 / Published: 8 October 2020
(This article belongs to the Section Spatial Data Science and Digital Earth)

Abstract

Over the past 30 years, the use of field spectroscopy has risen in importance in remote sensing studies for the characterization of the surface reflectance of materials in situ within a broad range of applications. Potential uses range from measurements of individual targets of interest (e.g., vegetation, soils, validation targets) to characterizing the contributions of different materials within larger spatially mixed areas as would be representative of the spatial resolution captured by a sensor pixel (UAV to satellite scale). As such, it is essential that a complete and rigorous assessment of both the data acquisition procedures and the suitability of the derived data product be carried out. The measured energy from solar-reflective range spectroradiometers is influenced by the viewing and illumination geometries and the illumination conditions, which vary due to changes in solar position and atmospheric conditions. By applying corrections, the estimated absolute reflectance (Rabs) of targets can be calculated. This property is independent of illumination intensity or conditions, and is the metric commonly suggested to be used to compare spectra even when data are collected by different sensors or acquired under different conditions. By standardizing the process of estimated Rabs, as is provided in the described toolkit, consistency and repeatability in processing are ensured and the otherwise labor-intensive and error-prone processing steps are streamlined. The resultant end data product (Rabs) represents our current best effort to generate consistent and comparable ground spectra that have been corrected for viewing and illumination geometries as well as other factors such as the individual characteristics of the reference panel used during acquisition.
Keywords: spectral processing; reflectance; spectrometer; spectroradiometer spectral processing; reflectance; spectrometer; spectroradiometer

Share and Cite

MDPI and ACS Style

Elmer, K.; Soffer, R.J.; Arroyo-Mora, J.P.; Kalacska, M. ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data. Data 2020, 5, 96. https://doi.org/10.3390/data5040096

AMA Style

Elmer K, Soffer RJ, Arroyo-Mora JP, Kalacska M. ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data. Data. 2020; 5(4):96. https://doi.org/10.3390/data5040096

Chicago/Turabian Style

Elmer, Kathryn, Raymond J. Soffer, J. Pablo Arroyo-Mora, and Margaret Kalacska. 2020. "ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data" Data 5, no. 4: 96. https://doi.org/10.3390/data5040096

APA Style

Elmer, K., Soffer, R. J., Arroyo-Mora, J. P., & Kalacska, M. (2020). ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data. Data, 5(4), 96. https://doi.org/10.3390/data5040096

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