Elmer, K.; Soffer, R.J.; Arroyo-Mora, J.P.; Kalacska, M. ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data. Data2020, 5, 96.
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.
Elmer, K.; Soffer, R.J.; Arroyo-Mora, J.P.; Kalacska, M. ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data. Data2020, 5, 96.
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.
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 etc.), 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-reflected 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 best current effort to generate consistent and comparable ground spectra which 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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
