The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
summer maize were extracted from the Spectral Database 
System of Typical Objects in China, while grass and forest 
datasets were obtained using Computer Simulation Model. 
Since the ground based minimum NDVI value is higher the 
minimum NDVI value in remote sensing scale, we advanced a 
simpler semi-empirical relationship between NDVI and LAI as 
shown in Table 3. The advanced arithmetic for transformation is 
seen in Equation (3) 
VI = VI œ -(F/qo-0)exp(rKyjLAI) (3) 
where VI^ and K V i are consistent with Equation (2). 
The original and advanced semi-empirical regression functions 
are shown in Table 3. 
After all these work, with the foregoing classification map (Fig. 
3), we applied the relationship between NDVI and LAI for 
estimating the LAI of different species, and to see how these 
LAI change in different crop growth stages. As shown in Figure 
4, we could see that the LAI value of crop is the highest in May 
14, since the winter wheat grows best in May 14 and are 
harvested in June 17. And LAIs value of grass and forest are 
the highest in June 17. 
The LAI temporal distribution characteristic is shown in Fig.5; 
there are several high LAI values in March and June, which 
locate in the forest area. In 16-March-06 LAI image, 99% of 
LAI values are below 1.6, in 14-May-06 LAI image, 99% of 
LAI values are below 1.8, and in 17-June-06 LAI image, 99% 
of LAI values are below 4.2. After checking the whole data, we 
found that there were a few high NDVI value points in March 
and June, that’s why the LAI got high value in those areas. 
Linear Regression Function 
R 2 
F 
Sig. 
Non-Linear Regression Function 
R 2 
F 
Sig. 
LA1=0.397*RVI 
0.939** 
3077.24 
0.000 
LAI=0.487*exp(0.236 *RV1) 
0.655** 
376.101 
0.000 
LAI=3.599*NDVI 
0.902** 
1833.453 
0.000 
LAI =0.218 *exp(3.345 ♦NDVI) 
0.661** 
385.627 
0.000 
LAI=T7.201*PVI 
0.934** 
2816.534 
0.000 
LAI =0.629*exp(8.554 *PVI) 
0.641** 
353.635 
0.000 
LAI=5.439*SAVI 
0.927** 
2525.258 
0.000 
LAI =0.290*exp(4.369*SAVI) 
0.678** 
416.311 
0.000 
LA1=5.493 *MS AVI 
0.938** 
3005.292 
0.000 
LAI =0.361 *exp(3.904*MSA VI) 
0.676** 
414.030 
0.000 
Table 1: Regression fimctions between VI (x) and LAI (y). 
RVI is Ratio Vegetation Index, NDVI is Normalized Differenced Vegetation Index, 
PVI is Perpendicular Vegetation Index, SAVI is Soil Adjusted Vegetation Index, MSAVI is Modified SAVI. 
RVI- M . NDVI-SAVI- *«*-* Q + + + 
R NIR + R NIR + R + L 2 
Regression Function 
R 2 
SSE 
SSY 
F 
10.67-1.45 
- 
336.559 
280.210 
- 
LAI =-1.221 *Ln(°™- NDV j 
0.83-0.18 
0.622 
105.983 
280.210 
325.495 
0 29 - PVI 
LAI- 2.706 *Ln{ ) 
0.29 + 0.01 
0.402 
167.588 
280.210 
133.059 
LA,=-2.m-L„(° M - SAr j 
0.66-0.12 
0.629 
103.991 
280.210 
335.523 
LAI = -2.010 */./?( °'^9 - MSAVI 
0.69-0.10 
0.496 
141.299 
280.210 
194.654 
Table 2: Semi-empirical regression functions between VI (x) and LAI (y)