Types of Sensor Resolutions Applicable to Remote Sensing Applications: Radiometric, Spatial, Spectral and Temporal (Especially for GATE-Geospatial 2022)

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Although most remote sensors collect their data using the basic principles described above, the format and quality of the resultant data vary widely. These variations are dependent upon the resolution of the sensor.

Types of Sensor Resolutions

There are four types of resolution that affect the quality and nature of the data a sensor collects: radiometric, spatial, spectral and temporal:

  • Radiometric resolution refers to the sensitivity of the sensor to incoming radiance (i.e.. , How much change in radiance must there be on the sensor before a change in recorded brightness value takes place?) . This sensitivity to different signal levels will determine the total number of values that can be generated by the sensor (Jensen, 1996) .
  • Spatial resolution is a measurement of the minimum distance between two objects that will allow them to be differentiated from one another in an image (Sabins, 1978; Jensen, 1996) . This is a function of sensor altitude, detector size, focal size and system configuration. For aerial photography, the spatial resolution is usually measured in resolvable line pairs per millimetre on the image. For other sensors, it is given as the dimensions, in meters, of the ground area which falls within the instantaneous field of view of a single detector within an array - or pixel size (Logion, 1997) . Below figure is a graphic representation showing the differences in spatial resolution among some well-known sensors.
  • Temporal resolution refers to the amount of time it takes for a sensor to return to a previously recorded location. This aspect of resolution becomes important when change detection is at the root of the research being done. Most orbital remote sensing platforms will pass over the same spot at regular time intervals that range from days to weeks depending on their orbit and spatial resolution. Data collected on multiple dates allows the scientist to chart changes of phenomena through time. Examples would include the expansion of urban areas, the declining forest cover, monitoring desertification, and, perhaps most common, the ever-changing weather.
  • Spectral resolution is determined by number and size of the bands which can be recorded by a sensor. Sensors also are unique about what portions of the electromagnetic spectrum they see. Different remote sensing instruments record different segments, or bands, of the electromagnetic spectrum. A sensor may be sensitive to a large portion of the electromagnetic spectrum but have a poor spectral resolution if its sensitivity is contained in a small number of wide bands. Another sensor that was sensitive to the same portion of the electromagnetic spectrum but had many small bands would have a greater spectral resolution. Like spatial resolution, the goal of finer spectral sampling is to enable the analyst, human or computer, to distinguish between scene elements. More detailed information about how individual elements in a scene reflect or emit electromagnetic energy increase the probability of finding unique characteristics for a given element, allowing it to be distinguished from other elements in the scene.
Remotely Sensed Data

By increasing one or any combination of these resolutions, a scientist will increase the chance of obtaining remotely sensed data about a target that contains accurate, realistic, and useful information. The downside to increased resolution is the need for increased storage space, more powerful data-processing tools (hardware and software) , and more highly trained individuals to perform or guide analysis (Jensen, 1996) . For these reasons, it is important to determine the minimum resolution requirements needed to accomplish a given task from the outset. This will avoid time wasted unnecessarily processing more data that is needed. It will also help to avoid the problem of too little data to allow completion of the task.

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