decreases (Shibayama and Akiyama, 1989). Therefore, it is 
possible that a similar situation will occur to the case of wheat. 
For the known vegetation index for the LAI, the cellulose 
absorption index (CAI) indicated a relatively high 
determination coefficient, and the wavelength band around 
2,200nm was selected for the multi regression equation. 
For the grain weight, nitrogen content rate, and biomass, all 
estimation methods showed a low determination coefficient. 
This situation implied that it is difficult to develop an 
estimation equation. 
Table 4: Determination Coefficient 
for Later Grain Filling Stage 
Item Methodology 
Grain Known Index 
Weight 
(g 
Multi Regression 0.380 |1220, 1730 1200, 1430, 1720 
Grain 
Nitrogen 
Content 
Rate (94) Multi Regression 0.501 (1330, 1970 680, 1980 
Known Index 
Known Index 
Head 
Moisture 
(%) 
Depth of water 970 
Multi Regression 750, 1720, 2090 1720, 2090, 2270 
Known Index 
Biomass 
(g) 
Multi Regression 0.545 |620, 1770 1320, 1950 
Known Index 
RGR 
Multi Regression 1480, 2080, 2270 680, 1470, 2280 
  
8: Determination coefficient of 0.6 or higher 
5.2.2 Heading Stage 
Table 5 shows estimation results of the selected estimation 
items using the four estimation methods. The grain nitrogen 
content rate, SPAD values, and leaf nitrogen content rate 
indicated a high determination coefficient (R?: 0.8 or over). The 
multi regression analysis was the most appropriate method for 
the grain nitrogen content rate, while the PLS regression was 
the most appropriate method for the SPAD values and leaf 
nitrogen content rate. The grain weight, ash content, biomass, 
and LAI also indicated a relatively high determination 
coefficient (R?: 0.6 or over). The multi regression analysis was 
the most appropriate method for the ash content and LAI, while 
the multi regression analysis using the FieldSpec was the most 
appropriate method for the grain weight and biomass. For the 
HyMap, the PLS regression using the HyMap was the most 
appropriate method for the same estimation items. 
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 
Table 5: Determination Coefficient for H 
Item Methodology 
Known 
: Index 
Grain 
Weight 
(G) | Multi Regression | 0.489 |530, 1372, 2098, 2421 | 0.506 [3% 721: 1206, 1573, 
1 2100, 2277 
Grain 
Nitrogen 
Content Multi Resression 363, 613, 769, 1460, , 764, 1047, 1220, 
Rate (%) 1 2028 2242 
Ash 
Content | Multi Regression 
(%) 
721, 762, 1000, 764, 1047, 1330, 
1209, 2207 1676, 2360 
Biomass 
(g) 
712, 1055, 1209, 1550, 1, 911, 1206, 1586, 
Multi Regression 
1728, 2102, 2273 2099, 2277 
Multi Regression 478, 688, 2013, 2421 764, 911, 2045, 2409 
364, 699, 875, 1047, 
1762, 2275 1477, 2277 
Multi Regression 707, 911, 1047, 1206, 
Leaf 
Nitrogen 
Content | Multi Regression. 356, 635, 763, 1460, , 764, 1047, 1220, 
Rate (%) 1 2227, 2398 1 2360 
  
38: Determination coefficient of 0.6 or higher 
The accuracies of grain weight, grain nitrogen content rate, and 
ash content of the heading stage, and head moisture of the later 
grain filling stage showed a high degree of conformance, and 
their estimation accuracies were verified (Table 6). 
Table 6: Determination Coefficient is Results 
Estimation Items Heading Stage Later Grain Filling Stage 
Grain Weight x(R^-0.31) 
Yield 
A(R’=0.59) 
Nitrogen Content Rate A(R’=0.54) 
Content = 
Quality |Leaf Nitrogen Content Rate 
SPAD Value 
Growth 
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