The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
■ SPEC 
OBNC 
■ CRDR 
■ NBOI 
1 angle 1 angles 3 angles 4 angles 5 angles 
Figure 4: Model coefficient of determination (R 2 ) of water 
content regressed on the data sets SPEC, BNC, CRDR and 
NBDI, respectively. R 2 represents the mean of all models with 
the same number of viewing zenith angles involved. For 
instance R 2 of one angle stands for the mean of five 
monodirectional models (± 36°, ±55° and nadir). 
3.3 Multi-temporal LAI Estimation 
The third case study aimed at exploiting synergistically the 
spectral, spatial and temporal information dimensions contained 
in multi-temporal CHRIS/PROBA observations. Such Earth 
observations represent a rich source of information for 
monitoring the dynamic vegetation status. For the assessment of* 
vegetation phenology the leaf area index (LAI) is essential as it 
is a key variable for the understanding and modeling of several 
eco-physiological processes within a vegetation canopy (eg. 
(Myneni, 1997). In this study, a radiative transfer model (RTM) 
is coupled to a canopy structure dynamics model (CSDM). The 
coupled models are used to exploit the complementary content 
of the spectral and temporal information dimensions for LAI 
estimation over a maize canopy. The resulting estimation of the 
temporal and spatial variation of LAI is improved by 
integrating multi-temporal CHRIS/PROBA data and ground 
meteorological observations. Further, the presented method 
provides the continuous LAI time course over the season. 
CHRIS/PROBA multi-angular data sets were acquired in Mode 
5 over the earlier described study site Vordemwald (VOR) on 
eight different dates between 26 May 2005 and 22 September 
2005. Out of these data sets, four dates that represent major steps 
in phenology of the selected agricultural fields were selected for 
further processing and data exploitation. The selected dates are 
26 May 2005 (day of year (DOY) 171), 20 June 2005 (DOY 
196), 17 August 2005 (DOY 229) and 22 September 2005 (DOY 
265). The data sets were geometrically and atmospherically 
corrected. 
A coupling scheme to combine two models, the joint radiative 
transfer models (RTM) PROSPECT/SAIL and the canopy 
structure dynamics model (CSDM), was implemented to 
estimate LAI based on the multi-temporal remote sensing 
observations (Koetz et al., 2005). The joint RTM provide an 
explicit connection between the canopy biophysical variables, 
the view and illumination geometry and the resulting canopy 
reflectance by exploiting our knowledge of the involved 
physical processes (Baret et al., 2000). The RTM have to be 
inverted to retrieve the biophysical variables from the measured 
canopy reflectance (Bacour et al., 2002; Kimes et al., 2000; 
Weiss et al., 2000). 
The use of a canopy structure dynamics model (CSDM) allows 
us also to derive a continuous estimation of LAI which is 
required in some applications, particularly those based on the 
forcing of agricultural growth or land surface models (Delecolle 
et al., 1992; Moulin et al., 1998). The used CSDM is a simple 
semi-meachanistic model describing the LAI dynamics (Baret, 
1986). Concerning the radiative transfer models in this study, 
the turbid medium radiative transfer model SAIL (Scattering 
from Arbitrarily Inclined Leaves (Verhoef, 1984; Verhoef, 
1985)) was used to describe the canopy structure. The 
PROSPECT model (Jacquemoud and Baret, 1990) was used to 
describe leaf optical properties. The coupling of the RTM and 
CSDM models was based on the hypothesis that the remotely 
sensed observations of LAI had to be consistent with the time 
profile of LAI generated by the CSDM. Consequently, the 
remotely sensed LAI was recalibrated, where necessary, 
relative to the phenologically sound LAI provided by the 
CSDM. The integration of the CSDM to the retrieval algorithm 
allowed a continuous description of the LAI time course over 
the growing season. For the evaluation of the LAI retrieval 
performance estimated LAI was compared to field measured 
LAI. 
This case study showed the successful coupling of a joint RTM 
to a CSDM to exploit the complementary content in the spectral 
and temporal information dimensions for the LAI estimation 
over a maize canopy. The knowledge of the canopy structure 
dynamic provided by the CSDM is used as ancillary 
information to achieve an improved robustness of the RTM 
inversion. Further, the presented coupled models integrate 
spacebome remote sensing data with ground meteorological 
observations providing a continuous LAI time course over the 
season along with start, end and length of the growing period. 
Crop growth as well as surface process models require such a 
continuous description of the vegetation evolution. The 
proposed methodology prepares for the assimilation of remote 
sensing observations into land surface process models. 
4. CONCLUSIONS AND OUTLOOK 
The above discussed case studies in Switzerland all present 
improved use of spectro-directional (and in one case multi 
temporal) measurements exceeding the classical use of the 
directional dimension for canopy structure retrieval and the 
spectral dimension for biochemistry. In particular improved 
measures for canopy heterogeneity (Minnaert’s k), 
unprecedented estimates of C w and C N , and temporal evolution 
of vegetation structure for integration in dynamic vegetation 
models have been demonstrated. The six years of 
CHRIS/PROBA operation in space have fostered closer 
collaboration of various Earth System sciences and allowed 
working at various scales that could be validated in the field. 
However, the realization of further spectro-directional imagers 
in space remains a challenge. Proposed missions had generally 
not found a majority for support (e.g. SPECTRA (Rast, 2004)). 
Currently, combined instrumented approaches (e.g. FLORA, 
FLEX) or approaches with more flexible acquisition pointing 
have been suggested (e.g. EnMAP), but true spectro-directional 
concepts to widen the fields of ecological monitoring and 
modeling remain sparse also in the future. 
ACKNOWLEDEGMENTS 
The work presented has been supported by the Swiss National 
Science Foundation (SNF, project 200020-101517), the 
Netherlands Organisation for Scientific Research (NWO SRON 
GO, grant EO-080) and the European Commission (contracts 
EVK2-CT-2002-00136 and GOCE-CT-2003-505376). 
CHRIS/PROBA data were acquired in the frame of the ESA 
AO proposal No. 2819. The continuing effort and support of 
SSTL (formerly SIRA) for CHRIS/PROBA is gratefully 
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