IEEE Geoscience and Remote Sensing Magazine - June 2013 - 50

such as goniometers for measuring multiangular sunsensor acquisition geometries over the same target, and
such instrumentation is already supported by our data
management schema (see Table 1), which is able to handle multiannual datasets and exploit information coming
from them. Other specific features requested by different
in situ targets can be the need for multiscale spatial sampling to take into account pixel and sub-pixel variability of
land cover in highly heterogeneous areas, as well as continuous sampling in situ of vegetation and atmospheric
parameters for agricultural applications; again, such features can efficiently tackled through measurement planning and collection comprising specific instrumentation
and sampling strategies, and can be stored easily through
our data management approach, as the level of detail of
Table 1 metadata fields shows.
Spectroradiometric field campaign are performed for a
wide range of domains related to hyperspectral and multispectral remote sensing techniques and applications, and
our operational workflow can be exploited for such targets
and adapted to their particular features. Those remote sensing domains are briefly summarized as:
◗ sensor or instrument calibration, as well as intercalibration of multisensor dataset;
◗ spectral data radiometric calibration, as well as data
fusion from different sensors/platforms (e.g. optical,
SAR, Lidar, DTM);
◗ validation of processed spectral data (atmospherically
corrected ground reflectance data coming from aerial
survey are compared to ground reflectance spectra, to
assess quality of atmospheric effect correction adopted);
◗ spectral data resampling and simulation (sensor
response simulation for new platforms testing and commitment, as well as performing remote sensing sensitivity studies to environmental parameters change).
As a main example of the importance of spectroradiometric field quality data, we can look at vicarious
calibration of satellite or aerial multispectral sensors.
Vicarious calibration is an important tool for monitoring sensor performances throughout the operation time
and allowing high quality data to be acquired. According to reflectance-based methods, it is possible to estimate the at-sensor radiance over selected test sites on
the Earth's surface starting from in situ measurements
of the spectral reflectance of ground targets and accurate
measurements of the optical depth and other meteorological parameters, at the time of the sensor overpass.
The reflectance-based validation provides a common
approach to monitor and verify the accuracy of both
pre-flight calibration and atmospheric correction via
radiative transfer model. The level of agreement between
ground-based and airborne hyperspectral data allows for
the estimation of data accuracy. Of course, all this needs
that the quality of spectroradiometric data acquired in
situ is as high as possible, and that those data are well
documented in order understand calibration results and
50

to individuate and remove possible bias factors due to
nonoptimal measurement conditions. Field measurements acquired according to our proposed workflow are
well suited to satisfy those needs for high quality standards, as the demonstration case and lessons learnt presented in the previous section show.
As a future perspective in spectroscopy applied to
geology mapping, for example, we can foresee that, for
the next decade, the remote exploration of Earth and
other planets (Mars, first of all) as well will be based on
highly advanced technologies to accomplish: reconnaissance, global mapping and detailed geological-environmental analyses at regional to outcrop scales. Therefore,
both predicting the capabilities and information potential of data from future instruments and establishing the
requirements to design instruments for detailed investigations are critical issues. In situ studies and multi-scale
approach to data collection play a crucial role to face
this challenge. To this end, the definition of operational
standards, protocols and workflows, to build compliance
among data will soon be mandatory. Our approach is one
step forward this, and we hope other proposals will follow, thus to build a proper standard protocol for merging
together the different datasets coming from field spectroradiometric surveys. This will help improving effectiveness of remote sensing of land processes, starting from in
situ data quality improvement.
5. acknowledgments
The spectroradiometric activities described here are coming from the 20 years field activities experience of Institute
for Electromagnetic Sensing of the Environment (CNRIREA), proximal remote sensing group, and in particular
thanks to the efforts and knowledge of C. Giardino. In
particular, actual field campaign described as demonstration case in section 3 have been carried out with the technical support of M. Bresciani and M. Musanti (CNR-IREA.
Milan section).
references
[1] E. J. Milton, M. E. Schaepman, K. Anderson, M. Kneubühler, and
N. Fox, "Progress in field spectroscopy," Remote Sens. Environ.,
vol. 113, suppl. 1, pp. S92-S109, Sept. 2009.
[2] M. E. Schaepman, S. L. Ustin, A. J. Plaza, T. H. Painter, J. Verrelst,
and S. Liang, "Earth system science related imaging spectroscopy: An assessment," Remote Sens. Environ., vol. 113, suppl. 1,
pp. S123-S137, Sept. 2009.
[3] M. Dinguirard and P. N. Slater, "Calibration of space-multispectral imaging sensors: A review," Remote Sens. Environ., vol. 68,
no. 3, pp. 194-205, June 1999.
[4] A. F. H. Goetz, "Three decades of hyperspectral remote sensing
of the Earth: A personal view," Remote Sens. Environ., vol. 113,
suppl. 1, pp. S5-S16, Sept. 2009.
[5] G. Ferrier and N. S. Trahair, "Evaluation of apparent surface
reflectance estimation methodologies," Int. J. Remote Sens.,
vol. 16, no. 12, 1995.
ieee Geoscience and remote sensinG maGazine

june 2013

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - June 2013