Estimating leaf area index by coupling radiative transfer model and a dynamic model from multi-source remote sensing data

Satellite remote sensing enables derivation of LAI globally at available spatial resolution and temporal frequency, and several LAI products have been produced. However, there are problems for the current global or regional LAI products, which restrict the application of these products. On the one hand, there are gaps between the large number of parameters of physical models and the small amount of data obtained by single sensor, which may cause the decrease in accuracy that LAI products should have. On the other hand, there are gaps between instantaneous observation of remote sensing and parameters which have change rules. Custom methods to retrieve LAI from remote sensing data are just involving the transient observations, discarding the information about process. To resolve these problems, we develop a methodology to retrieve LAI by involving diverse data from multiple sensor and LAI change rule. The methodology can take full advantage of the different band and angle information of time series MODIS and MISR data to improve the accuracy of the retrieved LAI over the MODIS LAI product compared to the field measured LAI data. And the retrieved LAI is also temporally continuous.