ABSTRACTS

Oral Session 9 | Thursday, October 6, 10:20–10:40 | Abstract 634

QWIP Score: A Quantitative Metric for Quality Control of Aquatic Reflectance Spectral Shape using the Apparent Visible Wavelength

We present a simple metric developed from a global field dataset spanning blue, green and brown water types to assess the quality of a measured or derived aquatic spectrum. The Quality Water Index Polynomial (QWIP) is founded on the Apparent Visible Wavelength (AVW), a one-dimensional geophysical metric of color calculated as a weighted harmonic mean across visible wavelengths. The QWIP represents a polynomial relationship between the hyperspectral AVW (or approximated using sensor-specific extrapolations) and a Normalized Difference Index (NDI) of red and green wavelengths. The QWIP score represents the difference between a spectrum’s AVW and NDI and the QWIP polynomial with high quality data having scores less than or greater than 0.2. Per Dierssen et al. (2022), the original approach was tested extensively with both raw and quality controlled field data and remote sensing imagery from HICO and VIIRS to identify spectra that fall outside the general trends observed in aquatic optics. Here, we expand on this analysis to evaluate how the QWIP score can be used to identify optically shallow waters using the extensive NASA CORAL dataset collected with the skylight blocked approach across Pacific coral reefs. Additionally, we evaluate how the QWIP performs at nine AERONET-OC sites, as well as how it can be used on DESIS and WorldView-3 imagery to identify cloud shadows and other spectral outliers. The QWIP is being implemented into SeaDAS and other processing software to provide a level of uncertainty about spectra and to flag questionable or unusual spectra for further analysis.

Heidi Dierssen, University of Connecticut Avery Point, 0000-0003-4276-5530

Ryan Vandermeulen, NASA Goddard Space Flight Center

Dirk Aurin, NASA Goddard Space Flight Center

Rodrigo Garcia, University of Massachussetts Boston

Zhongping Lee, University of Massachussetts Boston

Alexandre Castagna, Ghent University

Brian Barnes, University of South Florida

Els Knaeps, VITO Remote Sensing

Quinten Vanhellemont, Royal Belgian Institute of Natural Sciences

Erin Hestir, University of California Merced