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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
ESTIMATING BIOCHEMICAL PARAMETERS OF TEA (CAMELLIA SINENSIS (L.)) 
USING HYPERSPECTRAL TECHNIQUES 
Meng Bian ab” Andrew K. Skidmore ^ Martin Schlerf ^, Yanfang Liu *, Tiejun Wang b 
? School of Remote Sensing and Information Engineering, Wuhan University, 129 LuoYuRoad, Wuhan, 430079, P.R. 
China - bian@whu.edu.cn 
? Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE, 
Enschede, The Netherlands - (skidmore, schlerf, tiejun)@itc.nl 
* School of Resource and Environmental Science, Wuhan University, 129 LuoYuRoad, Wuhan, 430079, P.R. China - 
yfliu610(2163.com 
Working Group, Theme or Special Session: VIII/6: Agriculture, Ecosystems and Bio-Diversity 
KEY WORDS: Agriculture, Quality, Hyper spectral, Estimation, Statistics 
ABSTRACT: 
Tea (Camellia Sinensis (L.)) is an important economic crop and the market price of tea depends largely on its quality. This research 
aims to explore the potential of hyperspectral remote sensing on predicting the concentration of biochemical components, namely 
total tea polyphenols, as indicators of tea quality at canopy scale. Experiments were carried out for tea plants growing in the field and 
greenhouse. Partial least squares regression (PLSR), which has proven to be the one of the most successful empirical approach, was 
performed to establish the relationship between reflectance and biochemical concentration across six tea varieties in the field. 
Moreover, a novel integrated approach involving successive projections algorithms as band selection method and neural networks 
was developed and applied to detect the concentration of total tea polyphenols for one tea variety, in order to explore and model 
complex nonlinearity relationships between independent (wavebands) and dependent (biochemicals) variables. The good prediction 
accuracies (r2 > 0.8 and relative RMSEP < 10 %) achieved for tea plants using both linear (partial lease squares regress) and 
nonlinear (artificial neural networks) modelling approaches in this study demonstrates the feasibility of using airborne and space- 
borne sensors to cover wide areas of tea plantation for in situ monitoring of tea quality cheaply and rapidly. 
1. INTRODUCTION 
Tea consumption is rising in recent years, for the special flavour 
and the possible beneficial effects on human body. 
Consequently, it has become increasingly important to be able 
to give reliable estimates of the tea quality (Yan, 2007). 
Traditional methods to determine tea quality is mainly handled 
by tea experts, which may bring inconsistent and subjective 
results, or based on wet chemical analysis, which is time and 
labour consuming. Being effective and quantitative, the 
development of new techniques using hyperspectral remote 
sensing data has offered possibilities to estimate and monitor 
vegetation quality in space and time (Knox et al, 2011; 
Mutanga and Kumar, 2007). 
Hyperspectral remote sensing techniques have been developed 
from a laboratory-based near infrared spectroscopy (NIRS) 
technique (Curran et al., 2001). The narrow sensitive band range 
(10 nm or less) makes it possible to detect subtle variations in 
the reflectance spectra, which are caused by differences in 
biochemical composition and physiology of vegetation (Davey 
et al., 2009; Schlerf et al., 2010). In recent years, researchers 
have extended the technique of reflectance spectroscopy to 
measure biochemical parameters of vegetation by field 
Spectrometer or airborne or spaceborne sensors, trying to 
explore the chemical variation of vegetation in a spatial context 
(Curran, 1989; Schlerf et al., 2010; Skidmore et al., 2010). 
Tea polyphenols compose of four main substances as catechins, 
flavonoids, anthocyanins and phenolic acids, accounting for 20- 
35% of the total dry matter. It contributes greatly to tea taste 
and quality. In practice, people only pluck the young tender 
buds and leaves for producing tea product with high-quality. 
Compared with older leaves, this part of tea plant contains the 
optimal ratio of polyphenols and amino acids, which forms the 
special taste of tea beverage (Mitscher and Dolby 1997). 
This research aims to estimate the concentrations of main tea 
quality-related compounds (total tea polyphenols) using 
reflectance spectroscopy for tea plants at canopy level. Both 
linear (partial least regression) and nonlinear (artificial neural 
network) regression methods have been attempted. To detect 
whether the spectral-chemical relationships exist for the whole 
tea species, partial least squares regression was performed to 
establish the relationship between reflectance and biochemical 
contents across different tea varieties. Furthermore, a hybrid 
approach of neural network and successive projection 
algorithms (variable selection) has been applied for the 
estimation of total tea polyphenols for one tea variety planting 
in a greenhouse.