5. OPERATIONALIZATION OF EARTH OBSERVATION
between what our models predict and what we observe. This process is
intrinsically quantitative and therefore demands that its input data be
quantitatively expressed in a way that is compatible with the way that these
process models express physical and biological phenomena. Secondly, as
operational uses of these systems develop, the routine screening and
preliminary analysis of the data/information will increasingly be done by
machines, owing to the huge volumes of data that it will be necessary to
handle. Thus, if we are to be successful in developing the information
processing structures to handle information in this way, it is essential that the
data be processed in a way that preserves the quantitative accuracy, be as
internally consistent as possible, be located as accurately as possible and that
the measurements be expressed as physical variables consistent with the
ways of expression within the process models.
We have characterized the information delivery process as consisting of
two principal steps: pre-processing and data to information conversion. As
stated above, the output of the first of these is a set of accurately calibrated,
accurately located physical measurements, in essence a set of numbers which
represent, to the greatest degree possible, the spatial distribution of the
physical variable(s) measured by a particular instrument at a particular time.
The case has already been made that in order for these data set to be useful
in an operational mode and hence have maximum intrinsic value, they must
be calibrated both in magnitude and position in as accurate a manner as
possible. This is achieved by modeling the physical process of reflectance or
emission of electromagnetic energy as accurately as possible. There are
several crucial considerations in being able to achieve maximum accuracy.
The complete geometry of the acquisition must be known, including:
– The position of the spacecraft
– The orientation of the spacecraft
– The time of the acquisition (hence the position of the Sun)
– The height and shape of the land (using a digital elevation model)
The state of the atmosphere at the time of acquisition must be known
including:
– A suitable model describing the absorption and scattering properties of
fixed and variable gases
– The quantity of variable gases present (principally ozone and water
vapor) at the time of acquisition
– The quantity and type of aerosols present at the time of acquisition
– Intrinsic surface reflectance model(s) for the surface(s) being
measured (Bi-directional Reflectance Distribution Function or BRDF).
These considerations affect not only the processing of the data, but the
design of the spacecraft instrument complement as well. The necessity to
acquire atmospheric information simultaneously with the primary data set is
39
between what our models predict and what we observe. This process is
intrinsically quantitative and therefore demands that its input data be
quantitatively expressed in a way that is compatible with the way that these
process models express physical and biological phenomena. Secondly, as
operational uses of these systems develop, the routine screening and
preliminary analysis of the data/information will increasingly be done by
machines, owing to the huge volumes of data that it will be necessary to
handle. Thus, if we are to be successful in developing the information
processing structures to handle information in this way, it is essential that the
data be processed in a way that preserves the quantitative accuracy, be as
internally consistent as possible, be located as accurately as possible and that
the measurements be expressed as physical variables consistent with the
ways of expression within the process models.
We have characterized the information delivery process as consisting of
two principal steps: pre-processing and data to information conversion. As
stated above, the output of the first of these is a set of accurately calibrated,
accurately located physical measurements, in essence a set of numbers which
represent, to the greatest degree possible, the spatial distribution of the
physical variable(s) measured by a particular instrument at a particular time.
The case has already been made that in order for these data set to be useful
in an operational mode and hence have maximum intrinsic value, they must
be calibrated both in magnitude and position in as accurate a manner as
possible. This is achieved by modeling the physical process of reflectance or
emission of electromagnetic energy as accurately as possible. There are
several crucial considerations in being able to achieve maximum accuracy.
The complete geometry of the acquisition must be known, including:
– The position of the spacecraft
– The orientation of the spacecraft
– The time of the acquisition (hence the position of the Sun)
– The height and shape of the land (using a digital elevation model)
The state of the atmosphere at the time of acquisition must be known
including:
– A suitable model describing the absorption and scattering properties of
fixed and variable gases
– The quantity of variable gases present (principally ozone and water
vapor) at the time of acquisition
– The quantity and type of aerosols present at the time of acquisition
– Intrinsic surface reflectance model(s) for the surface(s) being
measured (Bi-directional Reflectance Distribution Function or BRDF).
These considerations affect not only the processing of the data, but the
design of the spacecraft instrument complement as well. The necessity to
acquire atmospheric information simultaneously with the primary data set is
39