**7. References**


change, especially increased temperature. *L. decidua* has been identified as a species capable of strong establishment across Siberia, and with the capacity to prevent the positive feedback associated with vegetation shift, from larch to evergreen conifer, in the region. These results deal with the response of the current system to controlled climate change. Future studies need to address the ability of the system to restart following disturbance, under altered climate conditions. Early successional species, such as larch may not be able to establish dominance in conditions, which have already warmed. In this case, the

These results highlight potential for the use of remote sensing data in areas identified as vulnerable to vegetation change. Modeling studies offer the opportunity to identify a signature of climate change in vegetation dynamics in advance of those changes occurring on the ground. Remote sensing technology can be used to track land cover changes in areas identified by model results as vulnerable to vegetation shift. Furthermore, the results of this study indentify a positive feedback cycle where warming creates vegetation shift, which then creates further warming. The detailed vegetation maps derived from remote sensing data offer a capability to evaluate locations where vegetation shift has occurred in an effort to track the progress of this positive feedback cycle and assess the direction and magnitude of any albedo shift associated with such a change. Vegetation monitoring informed by modeling efforts provide a robust tool in responding to and identifying vegetation changes

This work was supported by the following NASA grants to H.H. Shugart: NNG-05-GN69G, NNX-07-A063G, NNX-07-AF10G, NAG-11084. We greatly appreciate the support and encouragement of Dr. Pavel Groisman and the Northern Eurasian Earth Science Partnership Initiative (NEESPI) in regard to this ongoing research. We extend our thanks to Dr. Paolo D'Odorico and Dr. Virginia Seamster for suggestions and feedback on earlier versions of this

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**10** 

*1,2China 3,4France* 

**Reconstructing LAI Series by Filtering Technique and a Dynamic Plant Model** 

*1State Key Laboratory of CompSys, Institute of Automation, CAS,* 

*2LIAMA, Institute of Automation, CAS,* 

*4Cirad-Amis, Montpellier Cedex 5* 

*3CNRS, National Center for Scientific Research,* 

Meng Zhen Kang1,2, Thomas Corpetti2,3, Jing Hua1,2 and Philippe de Reffye4

Remote sensing images provide very rich and useful information linked to relevant biophysical parameters such as the LAI (Leaf Area Index), fCOVER (fraction of vegetation cover) or fAPAR (fraction of Absorbed Photosynthetically Active Radiation). At the moment, several techniques for estimating such variables are available and widely used in many applications, such as estimation of the total biomass and monitoring the dynamics in canopy vegetation (Baret et al., 2007; Lecerf et al., 2008). For several years, a large number of Very High Spatial Resolution (VHSR) satellites, such as Quickbird, Geoeye and Ikonos, have been launched, and very important missions such as the Venus and the Sentinel-2 are expected in 2012 and 2013. This provides possibility of having more or less temporal consistency in VHSR observations of the land use on relevant agricultural sites. However, because of the heterogeneity of the available VHSR data, in particular due to their different wavelengths sensibility and of the intrinsic errors induced by the estimation processes, the resulting time series of biophysical parameters are more or less noisy. As a matter of fact, the estimated variables may only poorly fit their actual dynamics. The estimation of the complete sequence of such parameters is then of prime importance, in particular if one

In this chapter, we propose to explore the possibilities of using tools issued from tracking techniques, in particular particle smoothing, to recover time-consistent series of LAI (Doucet et al., 2001; Kitagawa, 1996) from noisy and incomplete observations. Such techniques, based on Monte-Carlo strategies, allow performing the estimation of an unknown state function, LAI in current case, according to a given dynamical model and to possibly corrupted measurements. The dynamical model on which we rely on is GreenLab model, a functional-structural plant model simulating plant development and growth (Yan et al., 2004). Given model parameters, GreenLab can compute the evolution of LAI, the biomass production and partitioning, the organ size and biomass. Inverse method can be applied to estimate hidden model parameters by fitting model output with measured data (Kang et al., 2008). We suppose that from remote sensing data observing agricultural parcels, the type of

**1. Introduction** 

wants to analyze the evolution of the biomass.

