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Atmospheric Remote Sensing

Remote sensing can be broadly defined as the process of collecting data through means that do not involve physical contact. For example, the process of reading this text is an example of remote sensing, since your eye is gathering information about written letters on the page. A more technical definition of remote sensing describes the process of gathering data about a physical object using radiant energy, wave fields, or particles emanating from or interacting with the physical world. The NASA Remote Sensing Tutorial offers an appropriate definition:

A technology for sampling electromagnetic radiation to acquire and interpret non-contiguous geospatial data from which to extract information about features, objects, and classes on the Earth's land surface, oceans, and atmosphere (and, where applicable, on the exteriors of other bodies in the solar system, or, in the broadest framework, celestial bodies such as stars and galaxies). (http://rst.gsfc.nasa.gov, “Introduction: The Concept of Remote Sensing,” I-2)

Atmospheric remote sensing applies remote sensing techniques for data collection and analysis of physical parameters or variables associated with the atmosphere such as temperature, moisture, clouds, precipitation, aerosols, chemical constituents, wind, or weather systems.

Figure 1 The electromagnetic spectrum

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Source: NASA (http://rst.gsfc.nasa.gov).

Characteristics of ATmospheric Remote Sensing

Active Versus Passive Instruments

Instruments used in atmospheric remote sensing can be characterized as active or passive. An active system generates and transmits an electromagnetic signal that interacts with a medium through reflection, absorption, or scattering. Examples of active systems include Doppler radar (radio detection and ranging) and optical lidar (light detection and ranging). A passive system does not transmit a signal but makes a measurement by receiving scattered, reflected, or emitted radiation. Examples of passive systems include visible cameras, infrared radiometers, and pyranometers. Figure 1 shows the electromagnetic spectrum.

Resolution

Atmospheric remote sensing highly depends on understanding spatial, temporal, spectral, and radiometric resolution. Such factors determine what component or constituent of the atmosphere can effectively be measured or quantified. Spatial resolution represents the small area or pixel resolvable by the remote sensor. Smaller areas represent higher spatial resolution. For example, a 10-mega-pixel digital camera has higher resolution than a 5-megapixel digital camera because more small pixels can be resolved in the image. In the same manner, a 1-km (kilometer) satellite field of view (1-km × 1-km pixel) is higher in spatial resolution than a 10-km satellite field of view (10-km × 10-km pixel). Temporal resolution represents the time interval between measurements by the remote sensor. Spectral resolution represents the wavelength or frequency intervals of the electromagnetic spectrum that can be applied to make a measurement. Atmospheric remote sensing instruments can use one wavelength interval or many intervals (e.g., multispectral). Many advanced sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) (http://modis.gsfc.nasa.gov) are multispectral. Some instruments are even classified as hyperspectral, if they can use an extremely large number of spectral bands. The width of the interval is a measure of the relative magnitude of the spectral resolution. Radiometric resolution represents the smallest quantization of steps of the measurements or the number of bits. It can be useful for characterizing the precision of the instrument measurement.

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