Estimation of water and ice layer properties on diffusely reflecting substrates by modelling a single NIR spectrum. (May 2021)

• Record Type: Journal Article
• Authors: Kuntz, Stefan; Baumgartner, Andreas; Scholle, Markus; Fella, Sina
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 266(2021)
• Publication Date: 2021-05
• Subjects: Diffuse spectroscopy -- Spectral model -- Water ice -- Layer thickness estimation -- Ice percentage; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2021.107576
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: Estimation of water layer thickness and ice content (percentage) on surfaces is important in several areas of science and engineering. Diffuse surfaces are commonly found in real-world applications. Diffuse spectroscopy requires little sample preparation and is non-destructive. Fitting a mathematical model to spectroscopic measurement allows for robust and accurate estimation of layer thickness and ice content even during freezing. The near-infrared wavelength range from 850 nm to 1650 nm is suitable for the measurement of a wide range of layer thicknesses from tens of micrometers to several centimeters. Abstract: We present a spectral model for optical non-invasive estimation of layer thickness and water/ice content on diffusely reflecting substrate materials based on a single near-infrared spectrum. Using the wavelength range from 850 nm to 1650 nm which is readily accessible with InGaAs detectors, a wide range of layer thicknesses from tens of micrometers to several centimeters can be determined. The model is fitted without dependence on absolute signal values and requires no additional surface model given an approximately linear spectrum of the diffuse substrate material. Validation measurements from 50 µm up to 22 mm of water and ice—including the freezing process—demonstrate an excellent agreement between ground truth values and the fitted parameters of our model.