The concept of temporal resolution of Earth observation data refers to the revisit time or period. This is the time, which is necessary for the sensor platform (e.g. a satellite) to complete one entire orbit cycle. During one orbit cycle, the surface of the earth is completely covered by the sensor once. Temporal resolution also means the ability of a sensor to detect changes over shorter or longer periods of time. The revisit time for Earth observation satellites is usually several days. Or to express it differently: The absolute temporal resolution of a sensor orbiting the Earth is the time required to image the exact same area at the same viewing angle a second time.
The satellite orbit itself depends on the radius of the Earth, the orbit altitude above the Earth’s surface and the gravitational acceleration at planet’s surface. The time required to complete on entire orbit cycle additionally depends on the swath width of the sensor, the overlap between adjacent swaths and the geographical location at the Earth’s surface. The repetition rate increases slightly from the equator towards the north and south, which means that the revisit time is increasing with latitude. As a result, areas located in North America or Australia, for example, are covered a little more frequently than areas in Africa or South America near the equator.
But there are satellite systems that allow the pointing of their sensor to image the same area between different satellite passes separated by periods from one to five days. Thus, the actual temporal resolution of a sensor depends on a variety of factors, including the satellite/sensor capabilities, the already mentioned swath width and overlap, and latitude.
The capability of a sensor or EO product to resolve anything is a function of its (spatial, temporal, spectral and radiometric) resolution and of the detail at which a geographic phenomenon of interest manifests itself in time and space. A geographic phenomenon can be named or described, georeferenced and provided with a time interval at which it exists. The geographic phenomenon of interest is the one of which a user needs information to help him make a decision. Therefore, the geographic phenomenon needs to be resolved with a low enough uncertainty and a high enough quality that allows the user to make a decision with confidence.
For example, let’s consider a helicopter pilot that wants to know whether a specific site is suitable for an emergency landing. The decision to perform an emergency landing may be supported with an EO-derived digital map of emergency landing sites that are flat enough (as well as large enough for the pilot’s helicopter and free of any obstacles on the surface and in the approach area). If we only focus on the flatness of the terrain, we need a digital elevation model (DEM) of high enough spatial resolution and accuracy in the Z dimension to calculate slope within acceptable levels of uncertainty. The pilot probably can tell us what degrees of slope are okay for his helicopter and tell us sites (e.g. football fields) where such a landing would succeed. However, this is only the input to an analysis of different DEMs to identify the minimum spatial resolution and accuracy in the Z dimension to model slope products and associated uncertainty to derive an emergency landing site product that fulfils the requirements. Thereby the capability of different DEMs to resolve emergency landing sites can be analysed.
Spatial resolution is a measure of the smallest angular or linear separation between two objects that can be resolved by the remote sensing system. A useful heuristic rule of thumb is that in order to detect a feature, the nominal spatial resolution of the sensor should be less than one-half the size of the feature measured in its smallest dimension.
Other types of resolution of an EO dataset are available that determine for various geographic phenomena under investigation whether it is possible to resolve them in the data. These are radiometric resolution, spectral resolution and temporal resolution. Radiometric resolution is defined as the sensitivity of a remote sensing detector to differences in signal strength as it records the radiant flux reflected, emitted, or back-scattered from the terrain. Spectral resolution is the number and dimension (size) of specific wavelength intervals (referred to as bands or channels) in the electromagnetic spectrum to which a remote sensing instrument is sensitive. The temporal resolution of a remote sensing system generally refers to how often the sensor records imagery of a particular area. For time-series analysis, the temporal resolution determines the time granularity for resolving processes that underlie the change that is observable between subsequent images.