2. METHODS AND MATERIALS 
2.1 Study area and Data sets 
The research was conducted in the Huazhong Agriculture 
University in Wuhan, China (latitude 30°28'41"N, longitude 
114?21'48"E). Part of the data was colleted in the tea garden of 
the university, while another part of the data was colleted from a 
greenhouse experiment (Figure 1). 
  
  
Figure 1. Location of the Huazhong Agriculture University, 
Wuhan, China (left part of the figure). The right part shows the 
pictures of the tea garden (top) and the greenhouse setup before 
fertilization (bottom) in the university. 
Six different varieties of tea including Fuding dabai (FD), Fu 
yun 6 (FY), E cha 1 (EC), Tai cha 12 (TC), Huang dan (HD) 
and Mei zhan (MZ) in the tea garden were selected as study 
objects, to detect whether the modelling methods can be 
extended to various tea varieties. The tea bushes are so dense 
that soil background is barely seen from the canopy above. For 
each tea variety, eight samples were randomly collected. Thus, 
in a total, 48 (8x6) samples were obtained. 
For greenhouse experiment, young plants of Fuding Dabai tea 
were planted in the greenhouse under controlled conditions. To 
stretch the chemical variation in the sample, eight soil 
treatments with different levels of available soil nutrient were 
designed (Table 1) . Each soil treatment had eight repetitions, 
and a total of 64 samples (8*8) were collected for the 
greenhouse experiment. 
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 
  
Nutrient level Nitrogen Phosphorous Potassium 
Low level (L) 0.75 0.3 0.3 
High level (H) 7.5 3 3 
  
  
Table 1. Levels of available soil nutrient used for the 8 
treatments (unit: g/pot) 
2.2 Canopy spectral measurement 
On a cloud-free sunny day, canopy reflectance was measured 
using ASD FieldSpec Pro FR spectrometer (Analytical Spectral 
Devices). The spectrometer covers a range from 350-2500 nm 
with sampling intervals of 1.4nm between 350nm and 
1000 nm, and 2 nm between 1000 nm and 2500 nm. The fiber 
optic was handheld approximately 10-20 cm above the top of 
the canopy. To avoid bidirectional reflectance distribution 
function (BRDF), the pots were rotated 60° after every ninth 
measurement of the canopy. Before taking a canopy 
measurement, the radiance of a white spectralon panel was 
measured for normalization of the target reflectance. 
After the canopy measurements were finished, one bud with 
three or four leaves of tea bushes in the field were clipped. For 
tea plants growing in the greenhouse, four or five pots together 
were regarded as one observation and the tea leaves were 
plucked, to make sure enough tea leaves for chemical analysis 
in the laboratory. The weight of the fresh leaves for each sample 
unit has to been at least 40 grams to satisfy the need for wet 
chemistry analysis. 
2.3 Biochemical Assay 
Standard wet chemistry methods were used to determine the 
concentrations of total polyphenols. The leaves were steamed for 
three and a half minutes to destroy enzyme activity causing 
oxidation of the tea (Yamamoto et al. 1997) before drying in an 
oven at 80°C. Next, the dried leaves were ground using an 
electric mill. Total tea polyphenols were determined by the 
ferrous tartrate colorimetry method and spectrometry at 540nm 
(Iwasa and Torii 1962). 
2.4 Spectral pre-processing 
The bands regions 350 nm-400 nm, 1350-1420 nm, 1800-1970 
nm and 2300-2500 nm displayed high levels of noise due to 
atmospheric absorption, and were excluded from the data. 
Before data analysis, the reflected spectra of 64 observations 
were mean-centered by subtracting their means (Araujo et al, 
2001; Cho et al., 2007). 
2.5 Partial least square regression (linear regression 
approach) 
Partial least squares regression (PLSR) combines the features of 
principal component analysis and multiple regressions. It 
compresses a large number of variables to a few latent variables 
(PLS factors). It is particularly useful when the size of 
independent variables is much larger than that of dependent 
variables. PLSR reduces the problem of over fitting found with 
the multiple regression (Card et al., 1988; Curran, 1989). 
Partial least squares regression was performed to establish the 
relationship between reflectance and biochemical contents 
across different tea varieties. The 48 observations in the sample 
were randomly divided into training data (N=30) and test data 
(N=18). The training dataset were used to calibrate the partial 
least squares regression model, and the performance of the 
model was validated by comparing the model predictions of the 
test data to the observations. The performances of the PLSR 
models were assessed by the coefficient of determination (R2) 
and the root mean square error of prediction (RMSEP, Equation 
(1)) between predicted and measured concentrations on test data 
set. 
RMSEP= Equation (1) 
   
$410» 
n 
where n is the number of test data, Ji is the observed value of 
data point i and Yi is the estimated value based on the model 
calibrated by training data. 
  
   
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