In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B
191
Further, Analysis of Variance (ANOVA) between spectral data
from WSN and ASD for all the seven wavelengths were carried
out. Analysis showed that there were no significant differences
in the spectral measurements for all the wavebands except for
790nm. At the 790nm wavelength, the WSN data were
significantly higher (p<=0.05) compared to the ASD data.
Estimated standard error value for the wavelength 790nm was
0.101 where for the rest of the wavebands it varied from 0.0033
to 0.0075 (Table 13).
Wavelength (nm)
Standard Error
470
.0033
550
.0046
670
.0075
700
.0047
720
.0045
750
.0044
790
.0101
Table 1. Standard error in percentage of reflectance between
WSN and ASD for different wavelengths.
Analysis of the data obtained from the WSN clearly indicated
the possibility of employing such sensor network for observing
crop spectral characteristics, concurrent with airborne and
satellite data acquisitions.
3. CONCLUSIONS
This paper has presented the first of phase of the experiment
aimed at the development of a WSN for real time acquisition of
spectral data for in-situ calibration and validation of aerial and
satellite images. Comparisons were made with standard in situ
field-based verification technology (ASD FieldSpec®
spectroradiometer) and WSN. Data analysis showed that the
WSN can record spectral data with reliable quality continuously
for a reasonably long period of time. WSN is unique with its
ability to control the acquisition of a real-time spatially
distributed field data set from an office computing system. The
capability of the WSN to operate with minimum disturbance to
its surroundings (i.e. with high finesse, minimizing perturbation
of the variable of interest) brings enormous flexibility of
deployment. The technology is cost effective as it reduces the
need for logistically expensive field visits.
In the next phase of the project, spectral data from different
platforms/scales and crop biophysical data could be utilized for
the formulation of robust algorithms for effective and real-time
monitoring of N and water stress in crops. The study will also
attempt to compare different sampling strategies for
optimization of mapping of crop spectral characteristics at
paddock level. This experiment is a part of larger Australian
calibration and validation processes of satellite data products,
under the Terrestrial Environmental Research Network
AusCover (Jones et al. 2010).
Acknowledgment
This project was supported by Australian Research Council
linkage grant (LP0776656) and was implemented in
collaboration with the Department of Primary Industries (DPI),
Vitoria, Australia.
Authors express their profound gratitude to Mr. Robin Bendle
and Mr. Josh Walter of Mumong Farming at Inverleigh, VIC,
3321 and Andrew Whitlock (www.precisionagriculture.com.au)
for providing the logistics for the deployment of WSN.
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