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This article presents comprehensive validation of specific sea state parameters (SSPs) and synthetic aperture radar (SAR)-derived wind speeds (<inline-formula><tex-math notation="LaTeX">${{u}_{\text{SAR}}}$</tex-math></inline-formula>). The article introduces a novel approach to retrieving roughness length (<inline-formula><tex-math notation="LaTeX">${{z}_0}$</tex-math></inline-formula>) based on wave steepness, following the retrieval of the short wavelengths necessary to estimate <inline-formula><tex-math notation="LaTeX">${{z}_0}$</tex-math></inline-formula>. The SAR onboard the Sentinel-1 (S1) satellite that was used specifically in the interferometric wide swath mode (IW) data. The data were processed using the extended version of CWAVE (CWAVE_EX) algorithm for SSPs and CMOD5 for <inline-formula><tex-math notation="LaTeX">${{u}_{\text{SAR}}}$</tex-math></inline-formula>. CWAVE_EX was developed especially for coastal waters; the processing chain includes steps for SAR image denoising and eliminating image artifacts. SAR S-1 data inherently exhibit a substantial azimuthal cutoff length due to the data&#x0027;s high satellite altitude and SAR IW resolution. That complicates the retrieving of short wavelengths prevalent in coastal zones and needed to retrieve <inline-formula><tex-math notation="LaTeX">${{z}_0}$</tex-math></inline-formula>. The article focuses on the coastal seas of the USA, benefiting from the presence of an extensive network of ocean buoys for validation purposes. The complete SAR S1 A&#x002F;B archive from 2014 to 2022 was first processed to retrieve SSPs and <inline-formula><tex-math notation="LaTeX">${{u}_{\text{SAR}}}$</tex-math></inline-formula>. The validation for significant wave height (<inline-formula><tex-math notation="LaTeX">${{H}_s}$</tex-math></inline-formula>), second moment wave period (<inline-formula><tex-math notation="LaTeX">${{T}_{m2}}$</tex-math></inline-formula>), and <inline-formula><tex-math notation="LaTeX">${{u}_{\text{SAR}}}$</tex-math></inline-formula> was performed using in-situ measurements with about 6000 collocations. <inline-formula><tex-math notation="LaTeX">${{H}_s}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">${{T}_{m2}}$</tex-math></inline-formula> were compared against the corresponding parameters from hindcast spectral numerical model data with about 380 000 collocations. The comparisons between the retrieved <inline-formula><tex-math notation="LaTeX">${{H}_s}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">${{T}_{m2}}$</tex-math></inline-formula> against the in-situ observations and hindcast wave model data yielded a root mean square error (RMSE) of 0.46&#x2013;0.50 m and 0.9&#x2013;1.1 s. The RMSE of <inline-formula><tex-math notation="LaTeX">${{u}_{\text{SAR}}}$</tex-math></inline-formula> against in-situ observation was about 2 m&#x002F;s with a bias of 0.78 m&#x002F;s. The estimated <inline-formula><tex-math notation="LaTeX">${{z}_0}$</tex-math></inline-formula> values from satellite-driven wave parameters were highly correlated with the <inline-formula><tex-math notation="LaTeX">${{z}_0}$</tex-math></inline-formula> estimated from the in-situ observations, with an RMSE of 0.04 &#x00D7; 10^&#x2212;3 m and a bias of &#x2212;0.01 &#x00D7; 10^&#x2212;3 m. The article highlights the possibility of using SAR remote sensing data for global mapping of <inline-formula><tex-math notation="LaTeX">${{z}_0}$</tex-math></inline-formula>, including coastal effects of local variability in sea state and wind field gustiness.