Variability of the reflectance coefficient of skylight from the ocean surface and its implications to ocean color

Abstract

The value and spectral dependence of the reflectance coefficient (ρ) of skylight from wind-roughened ocean surfaces is critical for determining accurate water leaving radiance and remote sensing reflectances from shipborne, AERONET-Ocean Color and satellite observations. Using a vector radiative transfer code, spectra of the reflectance coefficient and corresponding radiances near the ocean surface and at the top of the atmosphere (TOA) are simulated for a broad range of parameters including flat and windy ocean surfaces with wind speeds up to 15 m/s, aerosol optical thicknesses of 0-1 at 440nm, wavelengths of 400-900 nm, and variable Sun and viewing zenith angles. Results revealed a profound impact of the aerosol load and type on the spectral values of ρ. Such impacts, not included yet in standard processing, may produce significant inaccuracies in the reflectance spectra retrieved from above-water radiometry and satellite observations. Implications for satellite cal/val activities as well as potential changes in measurement and data processing schemes are discussed.

Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity

“…Glint, a common issue for L8 and S2, could contribute additional scattered light to the satellite-observed signal and is currently not corrected for over turbid waters by most standard approaches. Glintremoval methods are predominantly developed for clear-water marine systems yet can profoundly influence R rs estimates (Gilerson et al, 2018). For water retrievals, glint corrections can cause up to 43% MAPD in R rs , significantly impacting the resulting Chl-a retrievals (Garaba et al, 2015).…”

Error Budget in the Validation of Radiometric Products Derived from OLCI around the China Sea from Open Ocean to Coastal Waters Compared with MODIS and VIIRS

“…The calculated error budget showed that the iterative model used in the atmospheric correction algorithm of OLCI has a smaller impact on the error of R rs compared to the NASA iterative model used to process the MODIS and VIIRS data over the open ocean around China. The main influencing factors on the error of R rs for OLCI, MODIS, and VIIRS are the aerosol LUTs and the Rayleigh-corrected radiance (the error of system vicarious calibration, the error of Rayleigh scattering, whitecaps, gas and glint correction algorithm, and surface effects [51,66]) used in the atmospheric correction algorithm over the open ocean and coastal waters around the China Sea.…”

“…Based on this threshold, 18 stations were removed. The surface-reflected radiance (mainly due to sky glint because direct sun glint is usually weak with measurement geometry) is recognized as a major sources of uncertainty [50,51]; thus, a station The surface-reflected radiance (mainly due to sky glint because direct sun glint is usually weak with measurement geometry) is recognized as a major sources of uncertainty [50,51]; thus, a station was discarded when the ρL sky /L t exceeded 50%, [50,52]. Based on this threshold, eight stations were removed.…”

“…The absolute difference between the in situ R rs and the modeled R rs is calculated as The surface-reflected radiance (mainly due to sky glint because direct sun glint is usually weak with measurement geometry) is recognized as a major sources of uncertainty [50,51]; thus, a station The coefficients of the model presented in Equation 22are from the GSM model [55,56]. If di f f was larger than 30%, the corresponding in situ measurement was discarded.…”

Glint Removal Assessment to Estimate the Remote Sensing Reflectance in Inland Waters with Widely Differing Optical Properties

“…where ρ (Equation (2)) (defined as the ratio of the surface-reflected radiance at the specular direction corresponding to the downwelling sky radiance from one direction) is the water-surface reflected factor used to correct the surface radiance reflection or glint effect-one of the main constraints of above-water measurements [9]. The ρ values depend on the viewing geometry of the sensor, environmental conditions (incident irradiance, solar zenith angle-SZA, wind speed), and atmospheric composition (aerosol, molecules, cloud coverage) [7,10,11]. Besides, the more accurate estimate of ρ, the more accurate are the R rs datasets for atmospheric correction validations, vicarious calibration of sensors, and bio-optical models [5,12].…”