Spectral derivative analysis of solar spectroradiometric measurements: Theoretical basis

Abstract

Spectral derivative analysis, a commonly used tool in analytical spectroscopy, is described for studying cirrus clouds and aerosols using hyperspectral, remote sensing data. The methodology employs spectral measurements from the 2006 Biomass-burning Aerosols in Southeast Asia field study to demonstrate the approach. Spectral peaks associated with the first two derivatives of measured/modeled transmitted spectral fluxes are examined in terms of their shapes, magnitudes, and positions from 350 to 750 nm, where variability is largest. Differences in spectral features between media are mainly associated with particle size and imaginary term of the complex refractive index. Differences in derivative spectra permit cirrus to be conservatively detected at optical depths near the optical thin limit of~0.03 and yield valuable insight into the composition and hygroscopic nature of aerosols. Biomass-burning smoke aerosols/cirrus generally exhibit positive/negative slopes, respectively, across the 500-700 nm spectral band. The effect of cirrus in combined media is to increase/decrease the slope as cloud optical thickness decreases/increases. For thick cirrus, the slope tends to 0. An algorithm is also presented which employs a two model fit of derivative spectra for determining relative contributions of aerosols/clouds to measured data, thus enabling the optical thickness of the media to be partitioned. For the cases examined, aerosols/clouds explain~83%/17% of the spectral signatures, respectively, yielding a mean cirrus cloud optical thickness of 0.08 ± 0.03, which compared reasonably well with those retrieved from a collocated Micropulse Lidar Network Instrument (0.09 ± 0.04). This method permits extracting the maximum informational content from hyperspectral data for atmospheric remote sensing applications.

Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing

“…In order to be able to fully take advantage of hyperspectral data's potential for more accurate retrievals of biophysical properties (such as phytoplankton functional types [138]), it is important to develop appropriate atmospheric correction methods. One possibility is to make use of spectroscopic techniques such as derivative analysis [253].…”

“…A sampling window determines how much detail is taken from the signal. Derivative spectroscopy is a well-established method in the domains of pharmacology and medical biology [253]. An early attempt to apply this technique to atmospheric correction of remotely-sensed imagery was limited by the fact that it sought to develop an algorithm that would be completely insensitive to atmospheric effects [22,254].…”

“…A number of recent works have demonstrated the potential for this technique to analyse complex coastal waters [259,260] and atmospheric effects such as thin cirrus clouds and aerosols [253]. Tufillaro and Davis [260] demonstrated image enhancement and feature selection applications of derivative spectroscopy applied to HICO for turbid coastal waters with riverine inputs.…”

“…In an image of the Yangtze River meeting the East China Sea (Shanghai, China), the second derivative was used to highlight features related to sediment reflectance (peak at 800 nm) and algal blooms (peak at 710 nm), the latter of which is not easily detectable in the original signal [260]. A recent study that applied derivative analysis of hyperspectral data to study atmospheric particles (cirrus clouds and aerosols) provides a glimpse into how this technique could improve the atmospheric correction process [253]. First and second derivatives of measured (ground-based) and modelled spectral signals displayed distinct spectral signatures, which could be used to classify aerosol types [253].…”

“…A recent study that applied derivative analysis of hyperspectral data to study atmospheric particles (cirrus clouds and aerosols) provides a glimpse into how this technique could improve the atmospheric correction process [253]. First and second derivatives of measured (ground-based) and modelled spectral signals displayed distinct spectral signatures, which could be used to classify aerosol types [253]. [260].…”