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RETRIEVALS OF CANOPY BIOPHYSICAL 
VARIABLES USING MULTI-TEMPORAL REMOTE SENSING DATA 
Zhiqiang Xiao a ' b *, Jindi Wang a,b , Tiegang Tong c 
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of 
Remote Sensing Applications, 100875, China 
Research Center for Remote Sensing and GIS, School of Geography, Beijing Key Laboratory for Remote Sensing of 
Environment and Digital Cities, Beijing Normal University, 100875, China - zhqxiao@bnu.edu.cn 
c School of Info-Physics and Geomatics Engineering, Central South University, Changsha, 410083, China 
KEY WORDS: Remote Sensing, Retrieval, Leaf area index, Multi-temporal 
ABSTRACT: 
The integrated application of multi-source and multi-temporal remote sensing data is the trend of remote sensing application 
research, and it is also the practical need to solve the inversion problem of remote sensing. In this paper, a method is developed to 
retrieve canopy biophysical variables using multi-temporal remote sensing data. The inherent change rules of biophysical variables 
are introduced into the retrieval methods by coupling the radiative transfer model with land process model to simulate time series 
surface reflectances. A cost function is constructed to compare the reflectances simulated by the coupled model with time series 
reflectances measured by sensors and the canopy biophysical variables with the available prior information. And an optimization 
method is used to minimize the cost function by adjusting the values of input canopy biophysical variables such as the temporal 
behaviour of the reflectances simulated reaches the best agreement with the multi-temporal reflectances measured. Retrieval of leaf 
area index from MODIS surface reflectance data (MOD09) at the Bondville site was performed to validate this method. The 
experimental results shows that the use of multi-temporal remote sensing data can significantly improve estimation of canopy 
biophysical variables. 
1. INTRODUCTION 
Remote sensing data have been widely used to estimate the 
canopy biophysical variables which are applied to large area 
water and carbon cycle simulation, climatic modelling and 
global change research. Therefore, it is very important to 
precisely estimate these variables from remote sensing data at 
the regional or global scale. Currently, there are many methods 
to estimate biophysical variables from remote sensing data 
(Liang, 2004; Weiss, 1999). And they can be roughly divided 
into following classes: by statistical relationship between LAI 
and spectral vegetation indices, by physical model inversion 
and by other nonparametric methods. These methods have their 
own advantage and disadvantage. Since the model inversion 
methods is physically based and can adjust to a wide range of 
situation, radiative transfer models are more and more used in 
the inverse mode to estimate the canopy biophysical variables 
(Kuusk, 1991; Jacquemoud, 1993). 
It is well known that the inverse problem is by nature an ill- 
posed problem mainly because of the not unique solution and 
the measurement and model uncertainties (Combal, 2003). 
Currently, there are two sorts of ways to solve the ill-posed 
problem. One is dependent on some kinds of hypothetical 
condition, which may reduce the accuracy of remote sensing 
data products retrieved by radiative transfer model, and the 
other is to use the prior information of biophysical variables in 
model inversion. 
With the development of remote sensing technique, many new 
types of sensors have been developed, and there are large 
numbers of remote sensing data with different temporal, spatial 
and spectral resolution obtained from space-borne or air-borne 
sensors. The integrated application of multi-source, multi 
temporal remote sensing data is the trend of remote sensing 
application research, and it is also the practical need to solve the 
inversion problem of remote sensing. Noted that most of the 
biophysical variables, such as LAI, are time-dependent and 
possess inherent change rules along with time which are often 
represented by process models such as crop growth models, it is 
an important way for us to introduce the inherent change rules 
of biophysical variables into the retrieval methods to add the 
amount of information needed to retrieve biophysical variables 
and improve the accuracy of remote sensing products. In this 
paper, we will present our research on the issue of integrating 
multi-temporal remote sensing data to retrieve biophysical 
variables. 
Radiative transfer model are coupled with land process model 
to simulate time series surface reflectances. And a cost function 
is constructed, according to the posterior probability formula 
defined by Tarantola, to compare the reflectances simulated by 
the coupled model with time series reflectances measured by 
sensors. And an optimization method is used to minimize the 
cost function by adjusting the values of input canopy 
biophysical variables such as the temporal behaviour of the 
reflectances simulated reaches the best agreement with the 
multi-temporal reflectances measured. By introducing the 
physical constraints from the land surface model, the method 
can integrate multi-temporal remote sensing data to retrieve 
biophysical variables. 
* Corresponding author.