Spatial resolution
Spatial resolution is the measure of smallest object that can be detected by a satellite sensor. It represents area covered by a pixel on the ground. Mostly, it is measured in meters.
For example, CARTOSAT-1 sensor has a spatial resolution of 2.5x2.5 m , IRS P6 LISS IV sensor has a spatial resolution of 5.6x5.6 m for its multispectral bands and LISS III has spatial resolution of 23.5x23.5 m in its first three bands. The smaller the spatial resolution, the greater the resolving power of the sensor system.
That's why one can detect even a car in the satellite image acquired by IKONOS (spatial resolution 1x1 m) but can see hardly even a village in a satellite image acquired by AVHRR (spatial resolution 1.1x1.1 km).
Spectral resolution
Spectral resolution refers to the specific wavelength intervals in the electromagnetic spectrum for which a satellite sensor can record the data. It can also be defined as the number and dimension of specific wavelength intervals in the electromagnetic spectrum to which a remote sensing instrument is sensitive. For example, band 1 of the Landsat TM sensor records energy between 0.45 and 0.52 µm in the visible part of the spectrum.The spectral channels containing wide intervals in the electromagnetic spectrum are referred to as coarse spectral resolution and narrow intervals are referred to as fine spectral resolution. For instance the SPOT panchromatic sensor is considered to have coarse spectral resolution because it records EMR between 0.51 and 0.73 µm. on the other hand; band 2 of the ASTER sensor has fine spectral resolution because it records EMR between 0.63 and 0.69 µm.
For example, CARTOSAT-1 sensor has a spatial resolution of 2.5x2.5 m , IRS P6 LISS IV sensor has a spatial resolution of 5.6x5.6 m for its multispectral bands and LISS III has spatial resolution of 23.5x23.5 m in its first three bands. The smaller the spatial resolution, the greater the resolving power of the sensor system.
That's why one can detect even a car in the satellite image acquired by IKONOS (spatial resolution 1x1 m) but can see hardly even a village in a satellite image acquired by AVHRR (spatial resolution 1.1x1.1 km).
Spectral resolution
Spectral resolution refers to the specific wavelength intervals in the electromagnetic spectrum for which a satellite sensor can record the data. It can also be defined as the number and dimension of specific wavelength intervals in the electromagnetic spectrum to which a remote sensing instrument is sensitive. For example, band 1 of the Landsat TM sensor records energy between 0.45 and 0.52 µm in the visible part of the spectrum.The spectral channels containing wide intervals in the electromagnetic spectrum are referred to as coarse spectral resolution and narrow intervals are referred to as fine spectral resolution. For instance the SPOT panchromatic sensor is considered to have coarse spectral resolution because it records EMR between 0.51 and 0.73 µm. on the other hand; band 2 of the ASTER sensor has fine spectral resolution because it records EMR between 0.63 and 0.69 µm.
Radiometric resolution
Radiometric resolution defined as the sensitivity of a remote sensing detector to differentiate in signal strength as it records the radiant flux reflected or emitted from the terrain. It refers to the dynamic range, or number of possible data-file values in each band. This is referred to by the number of bits into which the recorded energy is divided. For instance, ASTER records data in 8-bit for its first nine bands, it means the data file values range from 0 to 255 for each pixel, while the radiometric resolution of LISS III is 7-bit, here the data file values for each pixel ranges from 0 to 128.
Temporal Resolution
The temporal resolution of a satellite system refers to how frequently it records imagery of a particular area. For example, CARTOSAT-1 can acquire images of the same area of the globe every 5 days, while LISS III doest it every 24 days.
The temporal resolution of a satellite sensor is very much helpful in change detection. For instance, agricultural crops have unique crop calendars in each geographic region. To measure specific agricultural variables it is necessary to acquire remotely sensed data at critical dates in the phenological cycle. Analysis of multiple-date imagery provides information on how the variables are changing through time. Multi-date satellite images are also used to detect change in forest cover.
1 comment:
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