The representation separates space, time, and measured parameters into distinct axes, then aligns them in a unified grid that can accommodate a wide range of satellite and in situ measurements collected at different resolutions and times.

By treating each dimension as an independent but coordinated axis, the model supports consistent alignment and interpolation of heterogeneous data sources, making it easier to compare, merge, and analyze observations that previously did not fit neatly together.

This axis based approach is designed to scale to large, multi dimensional data cubes typical of modern earth system monitoring, where long time series, multiple variables, and overlapping sensor footprints must be handled without losing spatial or temporal fidelity.

According to the illustration, the grid model is suitable for applications that require systematic analysis of environmental change, climate variability, and other dynamic processes, because it provides a common reference for organizing and querying diverse observations.

The work, credited to Peter Baumann from Constructor University in Germany, is described in more detail in the journal Big Earth Data and is associated with a study that sets out a new global standard for earth data grids.

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