The path followed by a satellite is referred to as its orbit (Figure 4.7).
Satellites that view the same portion of the earth’s surface at all times have geostationary orbits. Weather and communication satellites commonly have these types of orbits. Many satellites are designed to follow a north–south orbit, which, in conjunction with the earth’s rotation (west–east), allows them to cover most of the earth’s surface over a period of time. These orbits are termed near-polar orbits. Many satellite orbits are also sun synchronous, that is, satellites cover each area of the world at a constant local time of day. Near-polar orbits also mean that satellites travel northward on one-half of the earth and southward on the other half. These are called ascending and descending passes. As a satellite revolves around the earth, the sensor sees a certain portion of the earth’s surface. The area imaged is referred to as the swath. The surface directly below the satellite is called the nadir point. Steerable sensors on satellites can view an area (off nadir) before and after the orbits pass over a target. The data collected by each satellite sensor can be described in terms of spatial, spectral, and temporal resolution.
Figure 4.7 Orbit of a satellite
Source <www. nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/1124>
Spatial Resolution
The spatial resolution (also known as ground resolution) is the ground area imaged for the instantaneous field of view (IFOV) of the sensing device. Spatial resolution may also be described as the ground surface area that forms one pixel in the satellite image. The IFOV or ground resolution of the Landsat Thematic Mapper (TM) sensor, for example, is 30 m. The ground resolution of weather satellite sensors is often larger than a square kilometre. There are satellites that collect data at less than 1 m ground resolution, but these are classified military satellites or very expensive commercial systems.
The measure of how closely lines can be resolved in an image is called spatial resolution, and it depends on the properties of the system creating the image, not just the pixel resolution in pixels per inch (ppi).
For all practical purposes, the clarity of the image is decided by its spatial resolution, not the number of pixels in an image. In effect, spatial resolution refers to the number of independent pixel values per unit length.
The spatial resolution of computer monitors is generally 72–100 lines per inch, corresponding to the pixel resolutions of 72—100 ppi. With scanners, optical resolution is sometimes used to distinguish spatial resolution from the number of pixels per inch.
In remote sensing, spatial resolution is typically limited by diffraction, as well as by aberrations, imperfect focus, and atmospheric distortion.
The ground sample distance of an image, or the pixel spacing on the earth’s surface, is typically considerably smaller than the resolvable spot size.
In stereoscopic three-dimensional images, spatial resolution can be defined as the spatial information recorded or captured by two viewpoints of a stereo camera (left and right camera). The effects of spatial resolution on the overall perceived resolution of an image on a person’s mind are not yet fully documented. It could be argued that such
“spatial resolution” can add an image that would not depend solely on pixel count or dots per inch, when classifying and interpreting overall resolution of a given photographic image or video frame.
Temporal Resolution
Temporal resolution is a measure of the repeat cycle or frequency with which a sensor revisits the same part of the earth’s surface. The frequency will vary from several times per day, for a typical weather satellite, to 8—20 times a year, for a moderate ground resolution satellite,
such as the Landsat TM. Temporal resolution refers to the precision of a measurement with respect to time. Often there is a trade-off between the temporal resolution of a measurement and its spatial resolution. In some contexts, such as particle physics, this trade-off can be attributed to the finite speed of light and the fact that it takes a certain period of time for the photons carrying information to reach the observer. In this time, the system might have undergone changes itself. Thus the longer the light has to travel, the lower is the temporal resolution.
This reasoning is subject to contention, however, challenged by the teaser posed in the first few chapters of Stephen Hawking’s A Brief History of Time. From Newton’s concept, it can be seen that gravitational effects do not appear to be subject to this time delay. The discovery of gravitational waves could, however, throw more light on this concept.
In another context, there is often a trade-off between temporal resolution and computer storage. A transducer may be able to record data every millisecond, but available storage space may not allow this.
In the case of four-dimensional positron emission tomography imaging, the resolution may be limited to several minutes.
In some applications, temporal resolution may instead be expressed through its inverse, the refresh rate, or update frequency in hertz, of a television, for example. The frequency characteristics will be determined by the design of the satellite sensor and its orbit pattern.
Spectral Resolution
The spectral resolution of a sensor system is the number and width of spectral bands in the sensing device. The spectral resolution of a spectrograph or, more generally, of a frequency spectrum is a measure of its ability to resolve features in the electromagnetic spectrum. The simplest form of spectral resolution is a sensor with one band only, which senses visible light. An image from this sensor would be similar in appearance to a black and white photograph from an aircraft. A sensor with three spectral bands in the visible region of the electromagnetic spectrum would collect information similar to that by the human vision system. The Landsat TM sensor has seven spectral bands located in the visible and near-to-mid infrared parts of the spectrum. Colour images distinguish light of different spectra. Multispectral images resolve even finer differences of spectrum or wavelength than is needed to reproduce colour. That is, multispectral images have higher spectral resolution than normal colour images.