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.
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