首页|Estimating the water-leaving albedo from ocean color

Estimating the water-leaving albedo from ocean color

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  • NSTL
  • Elsevier
Water-leaving albedo (alpha w), defined as the ratio of water-leaving irradiance to downwelling irradiance just above the surface, is a major component of ocean surface albedo (alpha) but has long been ignored or underrepresented. A semi-analytical scheme based on inherent optical properties (IOPs), termed IOPs-alpha w, is proposed in this study to estimate spectral alpha w(lambda) from ocean color measurements. Evaluations with numerical simulations of radiative transfer show that IOPs-alpha w outperforms the conventional scheme based on chlorophyll-a (Chl) concentration. The median absolute percentage difference (MAPD) of derived alpha w(lambda) from IOPs-alpha w is generally less than 3% in the blue-green spectral domain, in comparison to MAPD of over 40% for estimated alpha w(lambda) from the Chl-based scheme. IOPs-alpha w is later implemented to monthly composite data of the Visible Infrared Imaging Radiometer Suite (VIIRS), where reasonable spatial distributions and seasonal patterns of alpha w(lambda) are obtained. In particular, broadband alpha w in the visible domain, termed alpha w_VIS, obtained via IOPs-alpha w is over 50% higher than the previous estimation by the Chl-based scheme in most oceanic waters. Furthermore, this study concludes that alpha w_VIS could contribute up to 20% to alpha in oceanic waters under low solar-zenith angles. Thus, we suggest that neither the spatial variability of alpha w_VIS nor the contribution of alpha w_VIS to alpha shall be neglected, and it is necessary to incorporate IOPs-alpha w into current parameterizations of alpha in coupled ocean-atmosphere and climate models.

EditorMarie WeissWater-leaving albedoOcean surface albedoInherent optical propertiesBRDFOcean colorVIIRSINHERENT OPTICAL-PROPERTIESATMOSPHERIC CORRECTIONDIFFUSE-REFLECTANCESURFACE ALBEDOPURE SEAWATERABSORPTION-COEFFICIENTSNATURAL-WATERSCASE-1 WATERSCHLOROPHYLLMODEL

Yu, Xiaolong、Lee, Zhongping、Shang, Shaoling、Wang, Menghua、Jiang, Lide

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Xiamen Univ

Univ Massachusetts

NOAA

2022

10.1016/j.rse.2021.112807

Remote Sensing of Environment

Remote Sensing of Environment

EISCI
ISSN：0034-4257
年,卷(期)：2022.269
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