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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