The geographic distribution of vegetation over the Earth's land surface is traditionally described using classification schemes with discrete numbers of vegetation types. When such land cover data sets are used as boundary conditions in Earth system models, abrupt boundaries and unrealistic homogeneity are introduced into parameter estimates. This paper proposes an alternative approach to describe global land cover with continuous fields of vegetation characteristics. A linear mixture model is applied to 1-km advanced very high resolution radiometer data to estimate proportional cover for three important vegetation characteristics: life form (percent woody vegetation, percent herbaceous vegetation, and percent bare ground), leaf type (percent needleleaf and percent broadleaf), and leaf duration (percent evergreen and percent deciduous). Linear discriminants for input into the mixture model are derived from 30 metrics representing the annual phenological cycle. Through comparison with training data derived from a global network of Landsat multispectral scanner scenes, we conclude that the linear assumption implicit in the linear mixture model is not severely violated. The linear relationships between percent cover as determined from the training data and the linear discriminants are used to estimate end-member values, and the mixture model is applied to derive the seven layers of global continuous fields. The availability of Moderate Resolution Imaging Spectroradiometer data in the future holds promise for refining the simple technique used in this paper to derive improved global continuous fields.