In monsoonal ecosystems, vegetation phenology is generally modulated by the timing and intensity of seasonal precipitation. Seasonal precipitation is often characterized by substantial interannual variability in both space and time. A rigorous quantitative understanding of the ecology of the landscape requires spatially explicit information regarding the strength of the relationship between seasonal precipitation and vegetation phenology, as well as the interannual variability of the system. For this information to be accurately estimated, it must be based on spatially and temporally consistent measurements. The optical satellite image archive can provide these measurements. Satellite imagery offers observations of both a) atmospheric parameters such as the timing and spatial extent of monsoon cloud cover; and, b) phenological parameters, such as the timing and spatial extent of vegetation green-up and senescence. This work presents a method to capture both atmospheric and phenological parameters from an optical image time series. The method uses Empirical Orthogonal Function (EOF) analysis of a single spectral index for unified characterization of the spatiotemporal dynamics of both monsoon cloud cover and vegetation phenology. This is made possible by leveraging well-understood differences in the visible and near infrared reflectance of green vegetation, soil, and clouds. Image time series are transformed into a temporal feature space (TFS) that is comprised of low-order Principal Components. The structure of the temporal feature space reveals spatiotemporally distinct annual cycles of both cloud cover and vegetation phenology. In order to illustrate this technique, we apply it to the retrospective analysis of a seasonal cloud forest in the Dhofar Mountains of the southern Arabian Peninsula. Our results quantify known (but previously unmapped) local gradients in monsoon duration and vegetation community response. Individual ecological subsystems are also clearly distinguishable from each other, and consistent elevation gradients emerge within each subsystem. Novel observations also emerge, such as regreening/early greening events and spatial patterns in cloud duration. The method is conceptually straightforward and could be applied to characterize other monsoon environments anywhere on Earth.