Remote Sensing Resolutions

Aaron Moody,
Department of Geography,
University of North Carolina

Remote Sensing Resolutions:

The goal of this module is to contribute to a common understanding of remote sensing through a presentation of remote sensing resolutions.

The concept of resolutions provides a framework for characterizing remote sensing devices and remotely sensed data.

Moreover, resolution characteristics directly govern the types and nature of information that can be derived using data from a given sensor.

A discussion of remote sensing resolutions also provides a convenient framework for introducing or elaborating on other important remote sensing concepts that are discussed elsewhere in the RSCC. These include:

This Module is organized into five main sections as follows:

Spectral Resolution:

Spectral resolution refers to the number, spacing, and width of the sampled wavelength bands along the electromagnetic spectrum.

Figure 1. Spectral Characteristics of Some Common Sensors

Technically, spectral resolution is controlled by the beam splitting and focusing apparatus in the sensor, by the design of the detector arrays, and by the specific sensitivities of the photon detectors, or photovoltaic elements in the detector arrays.

Figure 2. MODIS and its Optical Surfaces

It is known and easily observable that different surface objects have unique, or characteristic spectral signatures.

Figure 3. Spectral Signatures for Some Common Materials

The higher the spectral resolution, the more completely and precisely the spectral signature of each individual IFOV will be sampled, and the more readily different scene elements can be classified or discriminated based on those signatures. The following figure shows how a common sensor, Landsat Thematic Mapper, samples the spectra of three common scene elements.

Figure 4. Multispectral Image (TM) and Class Spectra

Spectral band width and band placement are typically chosen so as to:

The dramatic differential between red (0.63 to 0.69 um on TM) and near-infrared (0.76 to 0.90 um) reflectance seen in Figure 5 is a well recognized spectral feature for vegetation. Most sensors sample in these two wavelength bands. For an example, go back to red edge. "Vegetation Indices" are designed to capitalize on the information contained in reflectance measurements made in these portions of the electromagnetic spectrum to indicate the abundance or physiological characteristics of the vegetation on the surface.

Figure 7. The Red Edge

An important consideration in sensor design is balancing the objectives of keeping the sampled spectral bands narrow, while maintaining a high signal/noise ratio. The narrower the wavelength band, the less total radiant energy will be incident upon the detector element. This is because a smaller "slice" of the total radiant flux is being sampled. As a result, the strength of the signal will be reduced relative to the magnitude of the background noise of the sensor. In order to maintain high image quality, the signal/noise ratio must be kept large.

Several ways to improve the signal/noise ratio are to:

  • broaden the sampled wavelength bands,
  • increase the spatial resolution (size of the IFOV),
  • increase the dwell time for each pixel.

Each of these options has its drawbacks, and this sets up some natural trade-offs among the different sensor resolutions. These tradeoffs will be discussed in more detail at the end of this module.

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Spatial Resolution:

Spatial resolution usually refers to the size of the instantaneous field of view (IFOV) or the ground pixel. Although alternative definitions exist (Mather, 1987), this is the meaning that will be used here.

The IFOV is the area on the ground that is viewed by the sensor at a given instant in time. As such, it usually represents the ground area that is represented by each pixel in a remotely sensed image. The IFOV can be indicated as the ground dimensions of each pixel, or as the instantaneous angular measurement of the sensor field of view.

Figure 8. Spatial Resolutions of Some Common Sensors

Spatial resolution is controlled by the scan rate, the dimensions of the detector array, and the rate at which the analog output, produced by each detector element as the scanning mirror sweeps across the surface, is integrated and sampled for conversion to digital output.

Spatial resolution is usually given as "nominal" spatial resolution which refers to the resolution for a sample obtained from the nadir viewing position at the specified altitude of the satellite.

Figure 9. Nadir and View Geometry

For some sensors, the actual resolution can vary considerably across the scan line. This is especially problematic for the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA series of meteorological satellites.

Due to the wide swath width of the AVHRR instrument (+/- 56 degrees from the surface normal or about 3000 km) AVHRR pixel dimensions become increasingly distorted away from nadir as view zenith angles increase. At the extreme edges of the scan, the nominal AVHRR resolution of 1.1 km is distorted to 2.4 km in the along track direction, and 6.5 km in the across track direction (Goward et al. 1991).

Figure 10. AVHRR: Resolution Dependence on View Angle

Spatial resolution has important implications for:

The higher (or finer) the spatial resolution, the more completely and precisely the shapes of objects are sampled, the more readily objects can be identified based on their shape, and the more accurately the precise location, extent and area of objects can be determined.

Figure 14. Don Pedro Reservoir at Various Resolutions

As such, the spatial resolution of the data must be able to support the objectives of a given remote sensing project. Clearly, mapping the boundaries of the reservoir shown in Figure 14 cannot be adequately achieved using 1 km resolution data.

Frequently, an important consideration in sensor design is balancing the objectives of attaining high spatial resolution, while maintaining a high signal/noise ratio. The smaller the IFOV, the less total radiant energy will be incident upon the detector element. As a result, the signal/noise ratio will suffer. This can be counterbalanced by either increasing the dwell time, or increasing the spectral bandwidth.

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Temporal Resolution:

Temporal resolution refers to the temporal frequency with which a given ground location will be sampled by an individual sensor. It is controlled by the orbital characteristics of the satellite and the swath width of the instrument.

Figure 15. Temporal Resolutions of Some Common Sensors

Many ground objects have characteristic temporal signatures. This refers to how the reflectance for a given object in a given waveband, or band combination, changes through time.

Figure 16. Temporal Signatures

For vegetated surfaces, the temporal signature is related to vegetation phenology, or the physiological changes of the surface vegetation over time as it goes through its seasonal cycles.

Because of this, there is potential to discriminate surface types based on their temporal behavior as it is manifested spectrally and sampled remotely by satellite.

The higher the temporal resolution, the more completely and precisely the temporal signatures of objects are sampled, and the more readily scene elements can be discriminated based on their temporal behavior.

High temporal resolution, such as that provided by AVHRR, also improves the likelihood of acquiring clear images, especially for areas that experience frequent and persistent cloud cover. Likewise, high temporal frequency data allow imaging of areas that are in the process of undergoing rapid change produced, for example, by fire.

Note that, while temporal resolution can be increased with a wider swath, this results in problems with spatial resolution as the view angle increases off nadir. This distortion of the IFOV is illustrated above in Figure 10. The highest temporal frequency obtainable from AVHRR (on the order of 12 hours at the equator) also involves sampling an area from several different view angles and directions. This has considerable implications for radiometric and geometric consistency when comparing data from the same area across time periods.

A wider swath will also decrease the dwell time, thus reducing the strength of the signal and, consequently, reducing the signal/noise ratio. This can be accomodated (i. e. a high signal/noise ratio can be maintained) by either enlarging the IFOV, broadening the sampled wavelength bands, or both; any of which will increase the strength of the signal.

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Radiometric Resolution:

Radiometric resolution refers to the dynamic range, or the number of different output levels used to record the radiant energy for a single measurement.

Figure 17. Radiometric Resolution of Some Common Sensors

The number of levels is governed by the byte size, or number of bits used by the recording medium to represent the signal generated by a single detector element for a given IFOV, or pixel.

The dynamic range is determined as 2^n, where n are the number of bits per byte, or pixel.

Note that the detector response to radiant energy is analog and, therefore, any incremental change in flux will initiate a change in the detector response. However, this analog response must be discretized for digital storage, transmission and analysis. The number of discrete levels is governed by the radiometric resolution.

Figure 18. Radiance-In/DN-Out Graph

The greater the radiometric resolution, the more accurately the remotely sensed data can represent variations in surface leaving radiance.

Figure 19. Digital Image at Various Dynamic Ranges

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Directional Resolution:

Directional resolution refers to the range of viewing angles from which the IFOV is, or can be, sampled.

Figure 20. Something Directional

[Link here to a table with many sensors summarized in terms of directional resolution]

Objects have characteristic directional signatures, or BRDFs.

Figure 21. Example BRDF's

If adequately sampled, the BRDF can be used to discriminate between objects and infer surface properties.

Figure 22. Something Directional

The higher the directional resolution, the more completely and precisely the directional signature, or BRDF, of the surface is sampled.

Directional resolution is governed by: <>

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