QWIP: A quantitative metric for quality control of aquatic reflectance spectral shape using the Apparent Visible Wavelength
Dierssen, H.M.; Vandermeulen, R.A.; Barnes, B.B.; Castagna, A.; Knaeps, E.; Vanhellemont, Q. (2022). QWIP: A quantitative metric for quality control of aquatic reflectance spectral shape using the Apparent Visible Wavelength. Front. Remote Sens. 3: 869611. https://dx.doi.org/10.3389/frsen.2022.869611
In: Frontiers in Remote Sensing. Frontiers Media S.A.: Lausanne. e-ISSN 2673-6187, more
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remote sensing reflectance, ocean color, hyperspectral remote sensing, hydrologic optics, water quality, QA/QC - quality assurance/quality control, water-leaving reflectance spectra |
| Authors | | Top | Datasets |
- Dierssen, H.M., more
- Vandermeulen, R.A.
- Barnes, B.B.
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| Abstract |
The colors of the ocean and inland waters span clear blue to turbid brown, and the corresponding spectral shapes of the water-leaving signal are diverse depending on the various types and concentrations of phytoplankton, sediment, detritus and colored dissolved organic matter. Here we present a simple metric developed from a global 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 that is inherently correlated to spectral shape calculated as a weighted harmonic mean across visible wavelengths. The QWIP represents a polynomial relationship between the hyperspectral AVW and a Normalized Difference Index (NDI) using red and green wavelengths. The QWIP score represents the difference between a spectrum’s AVW and NDI and the QWIP polynomial. The approach is tested extensively with both raw and quality controlled field data to identify spectra that fall outside the general trends observed in aquatic optics. For example, QWIP scores less than or greater than 0.2 would fail an initial screening and be subject to additional quality control. Common outliers tend to have spectral features related to: 1) incorrect removal of surface reflected skylight or 2) optically shallow water. The approach was applied to hyperspectral imagery from the Hyperspectral Imager for the Coastal Ocean (HICO), as well as to multispectral imagery from the Visual Infrared Imaging Radiometer Suite (VIIRS) using sensor-specific extrapolations to approximate AVW. This simple approach can be rapidly implemented in ocean color processing chains to provide a level of uncertainty about a measured or retrieved spectrum and flag questionable or unusual spectra for further analysis. |
| Datasets (3) |
- TIMBERS: Praet, N.; Vandorpe T.; Ollevier, A.; Dierssen, H.; Flanders Marine Institute (VLIZ): Belgium; University of Connecticut: USA; (2022): TIMBERS in-situ sensor dataset. Marine Data Archive., more
- Vansteenwegen, D.; Vanhellemont, Q.; Flanders Marine Institute (VLIZ): Belgium; Royal Belgian Institute for Natural Sciences (RBINS): Belgium; (2024): PANTHYR hyperspectral water radiometry Blue Accelerator Platform 2022. Marine Data Archive., more
- Vansteenwegen, D.; Vanhellemont, Q.; Flanders Marine Institute (VLIZ): Belgium; Royal Belgian Institute for Natural Sciences (RBINS): Belgium; (2024): PANTHYR hyperspectral water radiometry Blue Accelerator Platform 2023. Marine Data Archive., more
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