In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
2006) were proposed as an effective tool for the detection of 
canopy level N status because it also accounts for changes in 
canopy N concentration during the season. 
There are various methods of acquiring remote sensing data, all 
of which can be useful to farmers, depending on how the 
methods fit into their management operations. For example, it 
is possible to use satellite imagery or mount equipment on a 
light aircraft, to acquire imagery on-demand, but other means of 
data acquisition include tractor-mounted sensors for on-the-go 
sensing that can be used to control variable-rate equipment for 
precision nutrient inputs. All these approaches require the 
development of robust algorithms that are applicable across 
different scales/platforms so as to detect canopy nutrients 
independent of ground cover, water stress and other factors, 
such as solar zenith angle. Simultaneous and real-time ground 
based observations are highly beneficial for formulation and 
validation of such algorithms incorporating airborne or satellite 
remote sensing data for application from paddock to regional 
scale. 
Acquiring real-time ground based remote sensing data over a 
continuous period will enable ground observations to coincide 
exactly with other scales (airborne and satellite) of data 
acquisition. Deployment and maintenance of multiple ground 
based sensors at isolated field sites is a labour-intensive 
exercise. Development of a wireless sensor network (WSN) is 
considered as a reliable, efficient and cost effective solution to 
this problem. 
A WSN consists of sensor nodes distributed across a geographic 
area and each sensor node has wireless communication 
capability and some level of intelligence for signal-processing 
and networking of data (Li 2008). A WSN system is comprised 
of radio frequency (RF) transceivers, sensors, microcontrollers 
and power sources (Wang et al. 2006). WSN has diverse 
applications and allows Micro-Electro-Mechanical-System 
(MEMS) sensors to be integrated with signal-conditioning and 
radio units to form “motes”. A mote is a node in a WSN and 
normally consists of a processor, radio module and one or more 
sensors connected to it. This enables motes to acquire data from 
the sensors, process and communicate with other motes in the 
network. Motes promote large scale deployment owing their 
low cost, small size and low power requirement (Akyildiz et al. 
2002; Crossbow Technology Inc. 2007; Lopez Riquelme et al. 
2009; Wang et al. 2006) 
Wireless communication provides enormous flexibility in 
locating sensor installations, allowing deployment where wired 
connections are impractical or impossible. The ease with which 
widely spread sensors can be arranged results in significant 
reduction in the cost of data acquisition by avoiding installation 
and maintenance of costly transmission lines. This has led to a 
myriad of uses of this technology in diverse fields. WSNs have 
the potential for widespread application in precision agriculture, 
particularly in the areas of crop and irrigation management, 
variable rate chemical input application and modeling crop 
performance (Lopez Riquelme et al. 2009). 
A mobile field data acquisition system was developed (Gomide 
et al. (2001) in Wang et al. 2006) to collect data for crop 
management and spatial-variability studies. A ZigBee™ / 
Institute of Electrical and Electronics Engineers, IEEE 802.15.4 
(Baronti et al. 2007; IEEE 2003) wireless acquisition device 
network was established (Morais et al. 2008) for monitoring air 
and soil temperature, solar radiation and relative humidity for 
precision viticulture applications. Radio modules IEEE 
802.15.4 (IEEE 2006) were used in motes in the formation of 
wireless network for monitoring soil moisture, water quality and 
environmental conditions. Vellidis et al. (2008) developed and 
evaluated a real time, smart sensor array using Radio Frequency 
IDentification (RFID) tag for irrigation scheduling. 
Studies involving integration of hyperspectral or narrow band 
multispectral sensors into WSNs for real time monitoring of 
crop spectral characteristics are very limited. These data sets 
have been shown to be highly successful in monitoring 
chlorophyll content (Gitelson and Merzlyak 1994; Haboudane 
et al. 2002b; Penuelas et al. 1994), light use efficiency (Trotter 
et al. 2002), N status (Filella 1995; Fitzgerald et al. 2006a; 
Tarpley et al. 2000; Tilling et al. 2007) and disease conditions 
(Bravo et al. 2003; Devadas et al. 2009; Moshou et al. 2005). In 
this context, an experiment was carried out to establish a WSN, 
integrating seven narrow band sensors (470, 550, 670, 700, 720, 
750, 790 nm), critical for real time monitoring of N and water 
stress in crops (Fitzgerald et al. 2006a; Tilling et al. 2007). By 
establishing this WSN, the project aims to record in-situ ground 
based remote sensing data concurrently with aerial and satellite 
image acquisition. Airborne imagery at various spatial scales 
will form the bridge between ground and satellite remote 
sensing data. Through these simultaneously acquired remote 
sensing data from different spatial scales within a sampling 
framework, this project will attempt to quantify issues of scaling 
up-linked to the mapping of N and water stress in wheat. This 
paper illustrates the outcome of the first phase of the experiment 
involving instrumentation set up and analysis of spectral data 
recorded by the WSNs. 
2. MAIN BODY 
INSTRUMENTATION 
Instrumentation set up involved three main steps: 1) sensor 
development, 2) sensor integration with motes and, 3) 
establishment of the wireless network. 
2.1 Development of Sensor System 
The primary component of the sensor system was a 
combined silicon photo detector and optical interference filter 
(T-5) (Intor, Inc., NM, USA). The filters were 8.4mm in 
diameter by 7.07mm high. The seven specific filters had central 
wavelengths of 470, 550, 670, 700, 720, 750 and 790 nm with 
10 nm bandwidths. 
These optical filters were assembled into a custom designed 
light sensor multiplexer and amplifier board (Figure 1). 
Figure 1. Fully assembled sensor board. One of the seven 
optical filters is indicated in the figure. 
2.2 Housing of Sensor Boards and Calibration Set up 
To derive reflectance measurements directly, two sensor boards 
were designed for each node. One was directed upward, to 
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