Retrievals of Canopy Biophysical Variables Using Multi-temporal Remote Sensing Data

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. * Corresponding author.