**4. Conclusion**

In this chapter, we have presented a framework of estimating LAI series from remote sensing images using filtering techniques and GreenLab model. Computational experiment was done on synthetic data to show the feasibility of full process. The result shows that by embedding a dynamic model, the LAI series can be recovered even if the source data from remote sensing images are very noisy and sparse. And in doing so, the GreenLab model can be calibrated partially to simulate biomass production and allocation. The advantage of embedding a crop model is that the knowledge on crop development and growth can be used in recovering LAI series, and the link between LAI and biomass production make it possible to estimate biomass production from remote sensing data, which is the ultimate aim of estimating LAI.

Yet this theoretical work needs to be further tested by real remote sensing sources. Challenges include the initialization of model parameters, such as the setting on topological parameters and initial source and sink parameter. Detection of crop type can help to solve this issue by providing empirical parameters. Yet their values are not necessary to be accurate, and other information from remote sensing, such as leaf chlorophyll content and leaf water content, may compensate. On the other hand, the combination of a functionalstructural plant model as GreenLab brings many possibilities. For example, as the threedimensional structures of crop are built, it is possible to run radiative transfer model in virtual canopy. Although the result will be dependent on the definition of geometrical structure and optical properties of individual organs, it provides a possibility of validating the reconstructed canopy dynamics by comparing the virtual canopy with the obtained high resolution source images. The development of remote sensing technique and advance in plant modelling are increasing the interdisciplinary research of these two areas.
