The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
where t is the time variable representing the day of the year, and 
January 1 is set zero, av is related to the asymptotic value of 
LAI, c and d denote the slopes at the first and second inflection 
points, p and q are the date of these two points, and vb and ve 
are the LAI values at the beginning and the end of the growing 
season 
The averages of MODIS LAI within the site area are fit to 
determine the parameters of double logistic function which is 
used as the process model in our method to retrieve LAI using 
multi-temporal remote sensing data. Figure 2 shows the 
averages of MODIS LAI at the Bondville site within a 49km 2 
region around the tower or field site. On days 169 and 177, 
there are no LAI values over the region due to instrument 
problems. And the fitted double logistic model is also shown in 
Figure 1. Obviously, the double logistic function can effectively 
describe the LAI profiles for these vegetation types. 
(c) 
(d) 
Figure 1. The averages of MODIS LAI at the Bondville site 
together with the fitted double logistic model 
4. EXPERIMENTAL RESULTS 
In order to test the above algorithm, the MODIS surface 
reflectance data (MOD09) at the Bondville site are used to 
retrieve LAI. The results are also compared with LAI retrieved 
the basic method which just uses the individual pixel 
measurement. Figure 2 demonstrates the retrieved LAI time 
series for crops. The LAI time series retrieved by the basic 
method are shown in Figure 2(a). And Figure 2(b), 2(c) and 2(d) 
demonstrate the LAI time series retrieved by the new method 
by integrating three, five and seven continuous MODIS surface 
reflectance data respectively. It is clear that the LAI values at 
this flux site have markedly underestimated the field 
measurements in the crop growing season. And there are 
fluctuations, especially in the crop growing season, because it is 
difficult to acquire cloud-free image due to the high amount of 
moisture content in the atmosphere during the growing season. 
By comparison, the temporally integrated inversion method can 
remove noise shown as abrupt rises or drops, especially when 
more MODIS surface reflectance data are integrated. Moreover, 
the accuracy of the LAI by the new method has been 
significantly improved over the LAI retrieved by the basic 
method compared to the field measured LAI data. 
Figure 2. Retrieved LAI time series using multi-temporal 
remote sensing data 
5. CONCLUSION 
A method to retrieve LAI using multi-temporal remote sensing 
data was designed to produce spatially and temporally 
continuous LAI products with relatively higher quality. The 
algorithm integrates the inherent change rules of biophysical 
variables into the retrieval methods to improve the temporal 
consistency of the retrieved LAI by coupling the radiative 
transfer model with the empirical statistical model. Results as 
described in this paper have shown that the new algorithm is 
able to produce more continuous LAI product, and the 
validation of the retrieved LAI against the field measurements 
shows that the use of multi-temporal remote sensing data can 
significantly improve the accuracy of the parameter retrieval. 
REFERENCES 
Combal, B., Baret, F., Weiss, M. et al, 2003. Retrieval of 
canopy biophysical variables from bidirectional reflectance: 
Using prior information to solve the ill-posed inverse problem. 
Remote Sensing of Environment, 84(1), pp. 1-15. 
Fisher A., 1994(a). A model for the seasonal variations of 
vegetation indices in coarse resolution data and its inversion to 
extract crop parameters. Remote sensing of Environment, 48, pp. 
220-230. 
Fisher A., 1994(b). A simple model for the temporal variations 
of NDVI at regional scale over agricultural countries: validation 
with ground radiometric measurements. International Journal 
of Remote sensing, 15, pp. 1421-1446. 
Jacquemoud S. and Baret F., 1990. PROSPECT: a model of 
leaf optical properties spectra. Remote sensing of Environment, 
34, pp. 75-91. 
Jacquemoud S. and Baret F., 1993. Estimating vegetation 
biophysical parameters by inversion of a reflectance model on 
high spectral resolution data. In: Crop structure and light 
microclimate: Characterization and Applications, Paris, France: 
INRA, pp. 339-350. 
1002