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In this study, the utility of satellite-based whitecap fraction (<i>W</i>) data for the prediction of sea spray aerosol (SSA) emission rates is explored. More specifically, the study aims at evaluating how an account for natural variability of whitecaps in the <i>W</i> parameterization would affect SSA mass flux predictions when using a sea spray source function (SSSF) based on the discrete whitecap method. The starting point is a data set containing <i>W</i> data for 2006 together with matching wind speed <i>U</i>₁₀ and sea surface temperature (SST) <i>T</i>. Whitecap fraction <i>W</i> was estimated from observations of the ocean surface brightness temperature <i>T</i>_B by satellite-borne radiometers at two frequencies (10 and 37 GHz). A global-scale assessment of the data set yielded approximately quadratic correlation between <i>W</i> and <i>U</i>₁₀. A new global <i>W</i>(<i>U</i>₁₀) parameterization was developed and used to evaluate an intrinsic correlation between <i>W</i> and <i>U</i>₁₀ that could have been introduced while estimating <i>W</i> from <i>T</i>_B. A regional-scale analysis over different seasons indicated significant differences of the coefficients of regional <i>W</i>(<i>U</i>₁₀) relationships. The effect of SST on <i>W</i> is explicitly accounted for in a new <i>W</i>(<i>U</i>₁₀, <i>T</i>) parameterization. The analysis of <i>W</i> values obtained with the new <i>W</i>(<i>U</i>₁₀) and <i>W</i>(<i>U</i>₁₀, <i>T</i>) parameterizations indicates that the influence of secondary factors on <i>W</i> is for the largest part embedded in the exponent of the wind speed dependence. In addition, the <i>W</i>(<i>U</i>₁₀, <i>T</i>) parameterization is able to partially model the spread (or variability) of the satellite-based <i>W</i> data. The satellite-based parameterization <i>W</i>(<i>U</i>₁₀, <i>T</i>) was applied in an SSSF to estimate the global SSA emission rate. The thus obtained SSA production rate for 2006 of 4.4  ×  10¹² kg year⁻¹ is within previously reported estimates, however with distinctly different spatial distribution.