were acquired between October 14th and 16th using the 
FieldSpec Pro and FieldSpec Pro FR (ASD Inc., the U.S.), 
portable hyperspectral sensors, while airborne hyperspectral 
data were acquired in October 29th using the HyMap (HyVista 
Corporation, Austraria), an airborne hyperspectral sensor. 
The second field survey was conducted between the late August 
and early September, which is around the same time as the 
heading stage of wheat, of the year 2010 (Table 1). Ground- 
based hyperspectral data were acquired between August 25th 
and September 2nd using the FieldSpec 3 FR and FieldSpec Pro 
FR, while airborne hyperspectral data were acquired in 
September 6th using the HyMap. 
Table 1: Data Used for this Study and Observation Date 
  
  
  
  
  
  
  
  
Observation Date 
Data Type - 
First Survey Second Survey 
FieldSpec 2009/10/14-2009/10/16 2010/08/25-2010/09/02 
HyMap 2009/10/29 2010/9/6 
Growth Conditions 2010/08/24-2010/09/06, 
Data 2009/10/13:2000/10/15 2010/10/14-201010/19 
Sample Analysis 2010/08/24-2010/09/06, 
Data 2009/10/16:2009/10/20 2010/10/14-2010/10/19 
  
3.1 Specifications of Ground-based Hyperspectral Data 
To acquire ground-based hyperspectral data and conduct plant 
sampling, the total 30 and 33 quadrats (10m by 10m) were 
installed for the later grain filling and heading stage 
observations respectively. In the field surveys, the reflectance 
spectra were measured at northwest, southwest, and southeast 
corners of each sample quadrat. To acquire typical spectral data, 
two sets of ridge, furrow, ridge and furrow were repeatedly 
measured from approximately 1m above a head of wheat 
(Figure 2). The measurement wavelength ranged from 350nm to 
2,500nm, and the wavelength resolution was 1nm. 
  
N 
2Rows 1 
10m / 
OO 
2Rows 2RoWs Wheat heads 
| pod About 4 
A { YASDFOV 1m 
400 
OOo o 
i 4 4 
  
  
  
  
  
  
Figure 2: Overview of Quadrat (Left) 
and Acquisition Method (Right) 
3.2 Specifications of Airborne Hyperspectral Data 
For the airborne surveys using the HyMap, the flight height was 
2,250m, and the number of flight lines was nine. The 
observation wavelengths ranged from 440 to 2,480nm. The 
number of bands was 126, and the wavelength resolution was 
approximately 20nm. The view angle was 60 degrees, and the 
spatial resolution was about 4.2m. 
3.3 Acquisition of Wheat Growth Conditions Data 
For the later grain filling stage observation, the Leaf Area Index 
(LAI) and wheat height were measured at each sample point of 
the 30 quadrats around the same time as the ground-based 
reflectance spectra measurement. Wheat samples were also 
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 
  
collected in the field. The number of wheat heads were counted, 
and the wet and dry wheat weights were also measured. After 
threshing, the wheat grain weight was measured, and 15 
components, including the grain nitrogen content rate, were 
analyzed. 
For the heading stage observation, the LAI, SPAD values, and 
wheat height were measured in the 33 quadrats around the same 
time as the ground-based reflectance spectra measurement. 
Wheat samples were also collected in the field. The number of 
wheat heads was counted, and wet and dry wheat weights were 
measured. After threshing, the wheat grain weight was 
measured, and 17 components, including the leaf nitrogen 
content rate, were analyzed. For the year 2010, wheat samples 
were collected again from October 14th to 19th, which is during 
the harvesting stage. The number of wheat heads, grain weight, 
and biomass were measured, and 17 components, including the 
grain nitrogen content rate, were analyzed. 
4. METHODS 
This study involved (1) estimation items selection, (2) 
estimation equation derivation, (3) estimation accuracy 
verification, and (4) estimation map development. Regarding 
the estimation equation derivation, the PLS regression was used 
for the heading stage observation only (Figure 3). 
Growth 
: Conditions Data 
Field Spec (Heading & HyMap 
Harvesting 
Stages) 
Correlation 
Analysis 
Preprocessing 
Preprocessing Estimation Item (Geometric and 
Nemo Selection atmospheric 
pling corrections) 
line Seas 
Estimation Equation 
Derivation (Known 
Vegetation Index, NDSI, 
Multi Regression, and 
PLS Regression) 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Estimation Equation 
Derivation (Known 
Vegetation Index, NDSI, 
Multi Regression, and 
PLS Regression) 
gis Bee 
  
  
  
  
  
  
  
  
  
  
  
Estimation Y 
Accuracy pat 
Verification Estimation Map 
Development 
  
  
  
  
Figure 3: Workflow of this Study 
41 Estimation Items Selection 
The sample wheat data were analyzed to examine correlations 
between wheat features, and estimation items that are related to 
the wheat yield, quality, and growth conditions were 
determined. The sample wheat data of the heading stage were 
collected around the same time as ground-based and airborne 
hyperspectral data acquisition. The sample wheat data of the 
harvesting stage were also analyzed to examine correlations, 
and estimation items that are related to the wheat yield and 
quality were estimated. 
4.2 Estimation Methods 
For the later grain filling stage observation, (1) known 
vegetation index, (2) normalized differential spectral index 
(NDSI) (Inoue et al., 2008), and (3) multi regression analysis 
were examined. For the heading stage observation, (1) known 
vegetation index, (2) NDSI, (3) multi regression analysis, and 
(4) PLS regression analysis were examined. Using these 
estimation methods, correlations between the reflectance 
  
  
    
  
  
     
    
   
   
  
  
  
  
    
   
    
  
    
   
     
        
   
    
    
     
     
      
    
   
    
    
   
   
     
    
    
     
     
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