IEEE Geoscience and Remote Sensing Magazine - June 2013 - 42

Point

(a)

Grid

Transect

Regular

Random

(b1)

(b2)

(c)

fIgure 3. Sampling scheme of field measurements: point (a), grid-regular (b1) or random (b2)-and transect (c).

an option for wide homogeneous areas. This approach is
commonly adopted for target such as artificial spectral targets (Figure 3b1) or field plots in agronomic experiment
(Figure 3b2). Multiple measurements along linear transects
is accomplished to account for the spatial variability of the
target. In case of long transects (Figure 3c) the acquisition
of automatic GPS measurements and digital photography
for each point can be helpful in the post processing and
analysis of the data.
2.3. Data management
The third thread of the operational workflow is the one
dealing with data management activities. With the aim
of providing the in situ measurements with exhaustive
documentation, and following a detailed evaluation of
the existing resources, we decided to adopt and include
in our workflow the metadata model provided by Bojinski et al. [31]. This model has been already employed in
the context of spectroradiometric field surveys, and implemented in the SPECCHIO database [14], [15]. Table 1 lists
the metadata variables borrowed from the SPECCHIO
database and here followed in order to fully document the
dataset and assist the data management and exploitation.
We added two entries to the original list-namely 'Hand
Sample' and 'Reference Inter-calibration'-according to
our fieldwork experience and purpose. Hand samples are
generally collected in the field in order to archive a sample
set for further analysis in the laboratory, especially in the
context of a geologic fieldwork. The entry 'Reference Intercalibration' has been included in order to avoid errors in
the instrumental calibration due to the presence of dirt on
the reference panel, which can affect the standard reflector
in the field.
Although, as mentioned by the authors, the metadata
definition currently implemented into the SPECCHIO
42

model is not fully validated by the scientific community
[14], we strongly believe that this model builds a solid base
for further standard definition. The evaluation of the metadata specifications is beyond the purpose of the present
work, but in Section 3 we will demonstrate with a few practical examples the usefulness of its fulfillment.
Flagging the measurements with a proper set of metadata has many advantages:
◗ It allows to quickly display the spectral signature of a specific target sample, and its variability as a function of time,
illumination conditions, homogeneity of the surface.
◗ It allows to reproduce the measurements. In principle,
the knowledge of the spatial information, geometry of
acquisition, target definition and characteristics (e.g.,
target type, name and pictures) will allow scientists to
recognize the specific target measured, especially in the
case of multiple surveys in the same area (such as in the
case of multitemporal studies).
◗ It ensures that the data acquired in the field are compatible to each other and to datasets acquired by different operators at different target sites. In addition,
they can be fairly compared with the measurements
belonging to different datasets, acquired under similar
conditions. This enforces the interoperability of data
collection and sharing.
◗ It allows to identify pseudo-invariant features and evaluate their radiometric spatial uniformity and temporal
stability.
◗ It allows to statistically evaluate the effects due to illumination conditions and target homogeneity at the close
view scale.
◗ It allows to organize and manage the measurement
results. When suitably filled in, each table entry
represent a unique variable through which querying
the database. We built the database of measurements
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Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - June 2013