Agriculture Reference
In-Depth Information
different regions), and various other optional optical components (lenses, collimators, beam-
splitters, integrating spheres, optical fibers, etc.). The radiation source is usually a tungsten
halogen light bulb when recording spectra under laboratory conditions, and the sun when
recording spectra in the field. These sensors are generally periodically calibrated to provide
the radiance values but in practice, the reflectance is calculated against a white reference
target (Spectralon) that is assumed to reflect 100% of the radiation in the sample geometric
configuration. There are point and image sensors. The point sensor records a single
spectrum of the target (with a line-array detector) while averaging the mixed information of
all components within the sensor's field of view. The size of the single pixel being measured
depends upon the optics and the distance from the target. The image sensor provides data
similar to the point sensor except that an area-array detector is used that enables the
acquisition of spectral-cube information constructed from spatial and spectral views of the
area (target) in question. Image sensors usually have lower spectral resolution as well as
lower signal-to-noise ratio, as the integration time over the targets is less than in the point
spectrometer measurements. Laboratory sensors are more accurate, with higher spectral
resolution and a better signal-to-noise ratio: the integration time is long and the geometric
scheme is constant. They usually include a sample holder and a stable light source. Field
sensors are generally battery-operated and are less accurate than laboratory sensors. They
rely on the sun's radiation and the geometric measurement is not fixed. Nonetheless, they
provide flexibility and in-situ measurement capabilities that cannot be achieved under
laboratory conditions. The field sensors are a valuable tool for recording reference spectra
for space- or airborne remote sensing imagery which will be later used for calibration,
validation and quality control (Brook & Ben-Dor 2011). Image sensors can be operated from
both ground and air (space) domains. They can be integrated with the aircraft's GPS/INS
system and produce multi or hyperspectral data cubes that can be georeferenced; thus each
pixel in the image corresponds to a single area unit on the ground and is represented by a
spectrum. The signal-to-noise ratios of air- and spaceborne sensors are lower than those
operated on the ground and are governed by many factors (e.g. pixel size, integration time,
frame rate, electronic noise, IFOV and atmosphere attenuation). A comprehensive review of
image-based sensors for soil application can be found in (Ben-Dor et al. 2008).
2. Quantitative applications of spectroscopic data
Spectroscopic data are multivariate in nature. There are two approaches: 1) the
chromophore absorption band in question is known and under saturation (supervised), and
2) the chromophore absorption band is unknown and is affected by several factors
(unsupervised). Whereas the first approach is valid for the IR region by using selected
wavebands and pretreatment to prevent saturation, the second represents the case of
reflectance spectroscopy across the VNIR-SWIR region. For that purpose, multivariate
statistical techniques (also called chemometrics) are required to extract the information
about the quality attributes that is hidden within the spectral information. Essentially, this
involves regression techniques coupled with spectral preprocessing.
2.1 Preprocessing
Spectral preprocessing techniques are used to remove any irrelevant information which
cannot be handled properly by the modeling techniques. The preprocessing techniques
include
averaging,
centering,
smoothing,
standardization,
normalization
and
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