In times of global change, we must closely monitor the state of the planet in order to understand the full complexity of these changes. In fact, each of the Earth’s subsystems – i.e., the biosphere, atmosphere, hydrosphere, and cryosphere – can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region. Although such approaches are also used in other fields of science, they are rarely used to describe land surface dynamics. Here, we propose a robust method to create global indicators for the terrestrial biosphere using principal component analysis based on a high-dimensional set of relevant global data streams. The concept was tested using 12 explanatory variables representing the biophysical state of ecosystems and land–atmosphere fluxes of water, energy, and carbon fluxes. We find that three indicators account for 82% of the variance of the selected biosphere variables in space and time across the globe. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. The third indicator represents mostly changes in surface albedo. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heat wave (2010). The anomalies also allow us to interpret the impacts of these events. The indicators can also be used to detect and quantify changes in seasonal dynamics. Here we report, for instance, increasing seasonal amplitudes of productivity in agricultural areas and arctic regions. We assume that this generic approach has great potential for the analysis of land surface dynamics from observational or model data.