Declaration 2 Publications and manuscripts
5.1 Introduction
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72 Borengasser et al., 2008; Nansen et al., 2009). Hyperpsctral data can be related to foliar nitrogen concentration (e.g., Mutanga et al., 2004; Wenjiang et al., 2004; Abdel-Rahman et al., 2008a) due to its sensitivity in detecting any small changes in the vegetation biochemical content (Kumar et al., 2003). Hyperspectral data can be acquired from handheld (e.g., Analytical spectral device; ASD), airborne (e.g., Airborne Visible/Infrared Imaging Spectrometer; AVIRIS and Hyperspectral Mapper; HyMap) and spaceborne (EO-1 Hyperion) sensors and used as a monitoring tool for scheduling N applications. The use of both airborne and spaceborne spectrometers could enable the estimation of within field leaf N variation and contribute to the concept of precision farming.
Hyperspectral data are extremely large and of high dimensionality (Shen, 2007) because of the quasi-continuous spectra captured (Jiang et al., 2004a). Many hyperspectral features are redundant due to the strong correlation between wavebands that are adjacent (Shen, 2007; Demir and Ertürk, 2008). Therefore, the analysis of hyperspectral data is complex and needs to be simplified by selecting the most relevant features. A random forest algorithm which was developed by Breiman (2001) has recently been used as a feature selection method to reduce the redundancy in hyperspectral data (e.g., Chan and Paelinkx, 2008; Ismail, 2009; Abdel-Rahman et al., 2009a). The usefulness of the technique was also explored to predict (e.g., Ismail, 2009) or classify (Pal, 2005; Gislason et al., 2006; Everingham et al., 2007b) features of interest using spectroscopic data.
Selected variables as input in the predictive regression models when using hyperspectral data can be the original or transformed spectral reflectance values or vegetation indices based on these spectral values. Vegetation indices are useful in reducing variations due to irradiance, canopy geometry, and shading and in minimising the effect of soil background on the canopy reflectance (Jackson and Huete, 1991). Studies have shown that canopy level vegetation indices yielded better relationships for estimating leaf nitrogen content than reflectance from single wavebands (Jain et al., 2007; Zhu et al., 2008).
73 Studies have shown that canopy level hyperspectral data can be successfully used to estimate nitrogen content in various field crops such as wheat (e.g., Hansen and Schjoerring, 2003; Jarmer et al., 2003; Oppelt and Mauser, 2004; Wenjiang et al., 2004;
Tilling et al., 2007; Zhu et al., 2008), rice (e.g., Inoue and Peñuelas, 2001; Yang, 2001;
Xue et al., 2004; Nguyen and Lee, 2006; Nguyen et al., 2006; Zhu et al., 2008), maize (e.g., Osborne et al., 2002; Strachan et al., 2002), potato (e.g., Jain et al., 2007) and cotton (e.g., Read et al., 2002).
Hansen and Schjoerring (2003) found that a two-band (440 and 573 nm) vegetation index at canopy level based on a normalised difference vegetation index (NDVI) equation explained 56% of the variance in wheat leaf nitrogen concentration at a fertilizer trial.
Jarmer et al. (2003) reported that the visible region (400–700 nm) of the spectrum had the highest influence on a partial least squares (PLS) regression model developed from canopy reflectance at the complete spectrum range (400–2500 nm) for predicting wheat nitrogen status. On the other hand, Oppelt and Mauser (2004) used AVIS data to estimate wheat leaf nitrogen content and found that chlorophyll absorption integral (CAI) index at wavebands 600 and 735 nm yielded R2 of 0.78.
Wenjiang et al. (2004) demonstrated that canopy reflectance between 1000 and 1140 nm can be related to foliar nitrogen content in wheat (R2 = 0.91) at the reviving growth stage, whereas Tilling et al. (2007) showed that canopy chlorophyll content index (CCCI) can explain 76% of wheat nitrogen status. In field experiments, Zhu et al. (2008) pointed out that ratio vegetation indices at 870 and 660 nm as well as 810 and 660 nm at canopy level were well correlated to leaf nitrogen accumulation in wheat (R2 = 0.85) and rice (R2
= 0.66). A relatively similar result was obtained by Xue et al. (2004) who concluded that a reflectance ratio index at 810 and 560 nm yielded R2 of 0.85 for predicting rice canopy nitrogen accumulation. Inoue and Peñuelas (2001) pointed out that the spectral range of 520–570 nm had high influence on multiple linear regression models for estimating N content in rice. Furthermore, Nguyen and Lee (2006) illustrated that PLS regression models developed from rice canopy reflectance at wavelengths between 300 and 1100 nm can be used to estimate leaf nitrogen status at different growth stages (R2 = 0.87). In
74 another study, Nguyen et al. (2006) found that PLS regression models generated from logarithm of reflectance at canopy level at the same wavebands range (300–1100 nm) used by Nguyen and Lee (2006) produced R2 of 0.86 for estimating rice nitrogen status.
For maize crop, Osborne et al. (2002) reported that a multiple linear regression model consisted of canopy reflectance values at eight wavelengths (600, 610, 625, 700, 805, 875, 975, and 980 nm) yielded R2 of 0.81 for predicting maize leaf nitrogen concentration, whereas, Strachan et al. (2002) recommended that canopy reflectance at red edge position can explain 81% of maize leaf nitrogen variability. For a potato crop, Jain et al. (2007) indicated that a canopy reflectance ratio index at 750 and 710 nm produced R2 of 0.55 for predicting potato leaf nitrogen content. Conducting pots experiment, Read et al. (2002) found that cotton leaf N content was best correlated with canopy reflectance at 755 and 695 nm.
The results of the above-mentioned studies demonstrated the potential use of canopy level spectroscopic data in detecting field crops’ N status. However, there was inconsistency in the spectral features used to construct models for estimating crop N status. That might be due to the different experimental conditions under which the above- mentioned studies were undertaken. With the exception of that of Jarmer (2003), these studies investigated the estimation of crop N content together with other factors such as plant density, sowing date, water stress, and cultivars. Other reason could be due to different spectral regions or vegetation indices that were employed in the above-cited studies to explore the use of spectroscopic data in predicting crop N content. In addition, with the exception of e.g., Oppelt and Mauser (2004), the above-mentioned studies used handheld sensors to collect spectral data to monitor crop N status. More studies to investigate the use of airborne or spaceborne sensors in detecting N status of crops under on-farm conditions are needed.
For sugarcane, Abdel-Rahman et al. (2008a, 2009d) have examined the potential of leaf level spectroscopic measurements for estimating nitrogen content under laboratory and in situ conditions. They found that a vegetation index based on NDVI (2200, 2025 nm)