38, 2012 
5 were counted, 
neasured. After 
sured, and 15 
tent rate, were 
AD values, and 
round the same 
measurement, 
The number of 
it weights were 
1 weight was 
leaf nitrogen 
wheat samples 
which is during 
s, grain weight, 
5, including the 
selection, (2) 
tion accuracy 
ent. Regarding 
'Ssion was used 
  
ocessing 
netric and 
spheric 
actions) 
  
  
Y 
:quation 
Known 
ex, NDSI, 
sion, and 
'ssion) 
  
  
  
n Map | 
ment | 
  
ine correlations 
it are related to 
nditions were 
ling stage were 
d and airborne 
eat data of the 
1e correlations, 
heat yield and 
n, (1) known 
spectral index 
ession analysis 
ion, (1) known 
n analysis, and 
Using these 
he reflectance 
    
  
  
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 
spectra measured using the FieldSpec and the HyMap, and the 
selected estimation items were examined. Finally, an estimation 
equation was derived from the analysis results. 
43 Estimation Accuracy Verification 
For estimation items that implied a relatively high degree of 
conformance, the estimated values and actual measured values 
were compared, and the estimation accuracy was verified. 
44 Estimation Map Development 
For estimation items that implied a relatively high degree of 
conformance, estimation maps that cover the entire study area 
were developed based on the HyMap hyperspectral data. 
5. RESULTS AND DISCUSSION 
5.1 Selection of Estimation Items 
51.1 Later Grain Filling Stage 
Table 2 shows correlation coefficients between the sample 
wheat data. For the grain weight, which is related to the wheat 
yield, an extremely high correlation between the dry stem 
weight and biomass (the total dry weight of head and stem) was 
determined (R: 0.9 or higher) The correlation between the 
grain weight and the number of heads was also determined (R: 
0.76). For the grain nitrogen content rate, which is related to the 
wheat quality, a weak correlation with LAI was determined (R: 
0.59). For the head moisture, which is related to the growth 
conditions, correlations with other estimation items were not 
determined. Biomass and LAI were also selected as estimation 
items, and the estimation methods using hyperspectral data 
were examined for these selected items (Figure 4). 
Table 2: Correlations between Estimation Items 
for Later Grain Fi 
The 
Number 
of Heads 
  
       
    
    
   
     
     
      
  
        
   
       
    
    
    
    
  
   
   
   
    
   
      
Grain 
Potassiu 
m (96) 
Grain 
Nitrogen 
(%) 
Head Grain 
    
   
    
Stem Dry| Total Grain 
Weight | Biomass | Weight 
® (g) (g) 
  
Height 
(cm) 
      
     
    
  
  
     
  
  
    
  
     
    
     
(9) us (%) 
  
LAI 
The Number 
Heads 
Height (cm) 
    
-0.01 | -0.01 
   
-0.19] -0.31 
   
-0.29| -0.11 
  
Spectral Reflectance 
Data (Later Grain 
Filling Stage) | 
| 
| 
  
  
  
  
    
  
  
Biomass | 
| 
n % Y ux - Pis 
f 1 
tose Moisture Grain Weight Nitrogen | 
i> | Content Rate | 
{ 
puru pu TH 
“Growth D : | i 
| Conditions - x b Quality - | 
   
   
  
  
  
  
  
     
Figure 4: Estimation Items for Later Grain Filling Stage 
5.1.2 Heading Stage 
Table 3 shows correlation coefficients between the sample 
wheat data. For the grain weight, relatively high correlations 
with biomass of the heading stage and biomass of harvesting 
stage were determined (R: about 0.7). For the grain nitrogen 
content rate, a very high correlation with leaf nitrogen content 
rate was determined (R: 0.83), and correlations with LAI and 
SPAD values were also determined (R: about 0.7). For the ash 
content, which is related to the wheat quality, correlations with 
other estimation items were not determined. Biomass, LAI, 
SPAD values, and leaf nitrogen content rate were also selected 
as estimation items, and the estimation methods using 
hyperspectral data were examined for these selected items 
(Figure 5). 
Table 3: Correlations between Estimation Items 
for Heading S 
Moisture | Nitrogen The Grain Nitrogen 
Content | ends | Weight (@) Content 
SPAD 
Culm 
Length 
Biomass 
[3] 
Content 
Nitrogen 
Content 
The 
Heads 
Grain 
Weight 
Biomass 
(g 
WE nue <n BD 
Nitrogen 
Content 
Ash 
Content 
mB on 
  
  
   
  
  
  
Leaf Nitrogen 
Content Rate 
      
       
   
  
  
Biomass 
Grain Nitrogen 
Content Rate 
Figure 5: Estimation Items for Heading Stage 
5.2 Examinations of the Estimation Methods 
5.2.1 Later Grain Filling Stage 
Table 4 shows estimation results of the selected estimation 
items using the three estimation methods. For the head moisture 
and LAI, the multi regression analysis using the FieldSpec and 
HyMap showed a relatively high determination coefficient (R*: 
0.6 or over). In the multi regression equation for the head 
moisture, the short wavelength infrared region (SWIR) was 
selected. However, the previous case study of rice indicated that 
the reflectance ratio of SWIR increases as the head moisture