Accurate identification of clear- and cloudy-sky conditions is essential for reliable aerosol and cloud retrievals from ground-based remote sensing observations. This study investigates the spectral characteristics of zenith radiances measured by SKYNET sky radiometers and evaluates their potential for distinguishing clear- and cloudy-sky conditions based on wavelength dependence. Analyses of normalized spectral zenith radiances at multiple sites representing diverse atmospheric environments, including urban, maritime, tropical continental, and polar regions, revealed systematic differences between clear- and cloudy-sky conditions identified using quality-controlled MODIS cloud mask products. The spectral slope derived from the logarithmic contrast between zenith radiances at 0.400 and 0.675 µm showed strong sensitivity to cloudy-sky conditions but limited capability for detecting clear-sky conditions. In contrast, slopes based on the 0.500 and 0.675 µm and 0.400 and 0.675 µm wavelength pairs exhibited more balanced detection performance for both clear- and cloudy-sky conditions. Based on detailed analyses of the dependence of these slope values on solar geometry, aerosol properties, and cloud properties, together with classification scores evaluated against MODIS cloud mask data, this study proposes a simple, efficient, and easy-to-use sky-state detection criterion that classifies observations into clear, cloudy, and undetermined categories. Independent validation using collocated direct normal irradiance measurements confirmed the physical consistency of the slope-based classification. Further comparison with the standard cloud-screening procedure showed very good agreement in clear-sky aerosol data extracted using the newly developed criterion across diverse atmospheric environments. Overall, these results demonstrate that spectral zenith radiances provide a simple and physically interpretable basis for clear- and cloudy-sky detection. Incorporating such spectral diagnostics into ground-based radiometric networks can improve cloud screening and enhance the reliability of long-term aerosol and cloud climatological analyses.