Agriculture Reference
In-Depth Information
Fig. 4.1 Example of a passive RS system
the same crop in different conditions can vary across the pixels of an image. A more
appropriate approach is to consider the spectral response of a crop as a function of
the probability distribution of its spectral reflectance.
Figure 4.1 shows an example of a passive RS system. For an excellent review of
the spectral reflectance characteristics of vegetation, soil, water, snow, and clouds
see Hoffer ( 1978 ) and Renez and Ryerson ( 1999 ).
The wavelengths used in most agricultural remote sensing applications cover
only a small region of the electromagnetic spectrum (see Fig. 4.2 ). Wavelengths are
measured in micrometers (
m) or nanometers (nm). In remote sensing, we consider
radiation from ultraviolet (UV) (which has wavelengths from 10 to 400 nm) up to
radar wavelengths. The visible region of the electromagnetic spectrum is from
approximately 400 nm to about 700 nm. The green color associated with plant
vigor has a wavelength centered near 500 nm. The major parts of the electromag-
netic spectrum used for Earth resource sensing are the visible/infrared and the
microwave range.
Before describing how newly developed remote sensing technology has
influenced agricultural monitoring, we must introduce some definitions. First, we
discuss different resolution concepts. The resolution of a sensor is a measurement of
the capacity of an optical system to recognize signals spatially close or spectrally
similar. We consider four types of resolution: spectral, spatial, radiometric and
temporal (Jensen 2004 ).
The spectral resolution refers to the size and number of specific wavelength
ranges to which a sensor is sensitive. Different materials react differently to
electromagnetic radiation. So there is a specific spectral response for each different
object.
Thus, the bands are typically chosen to enhance the contrast between the
investigated object and its boundaries. According to the number of spectral bands
μ
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