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

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

Effect Of Wind Direction On Sea Surface Reflectance

The slope distribution of the sea surface varies with the speed as well as the direction of the wind. However, the dependence on wind direction is frequently ignored in the studies of the sea surface reflectance. In this study, we investigate the effect of wind directions on the sea surface reflectance (ρs). Zhang et al. 2017 sea surface reflectance model is followed where the sea surface in our study is modeled using the Cox and Munk (1954) anisotropic model. The Cox and Munk model has an inherent uncertainty relating to the distribution of capillary wave facets and wind speeds, which affects the estimate of surface reflectance. This leads to an inherent uncertainty in estimating surface reflectance of 5-20%, depending on the Sun-viewing geometry and wind speeds. For a typical setup of sensors measuring the sea surface reflectance, where sensor viewing angle(θsensor) = 40° and sensor azimuth angle (φsensor) = 45° to 90° relative to the Sun direction, we found the wind direction would either enhance or diminish Sun glint by up to a factor of 10, whereas its effect on skylight glint is less than 5%. The effect on total sea surface reflectance, including both Sun and skylight glints, therefore depends on the relative importance of Sun glint and the exact direction of the wind. In general, the effect of wind directions is less than the inherent uncertainty of the Cox and Munk model and hence can be ignored when Sun zenith angle (θSun) is greater than 40°. When θSun \u3c 40°, the effect varies with the exact Sun-viewing geometry and the wind direction. In particular, when θSun \u3c 20° and wind speed \u3e 7.5 m s-1, the maximum effect of ignoring the wind direction could reach up to 35%

Satellite Ocean Colour: Current Status and Future Perspective

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and interannual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided