The assessment of canopy biochemical diversity is critical for monitoring ecological and physiological functioning and for mapping vegetation change dynamics in relation to environmental resources. For example in oak woodland savannas, these dynamics are mainly driven by water constraints. Inversion using radiative transfer theory is one method for estimating canopy biochemistry. However, this approach generally only considers relatively simple scenarios to model the canopy due to the difficulty in encompassing stand heterogeneity with spatial and temporal consistency. In this research, we compared 3 modeling strategies for estimating canopy biochemistry variables (i.e. chlorophyll, carotenoids, water, dry matter) by coupling of the PROSPECT (leaf level) and DART (canopy level) models : i) a simple forest representation made of ellipsoid trees, and two representations taking into account the tree species and structural composition, and the landscape spatial pattern, using (ii) geometric tree crown shapes and iii) detailed tree crown and wood structure retrieved from terrestrial lidar acquisitions. AVIRIS 18m remote sensing data are up-scaled to simulate HyspIRI 30m images. Both spatial resolutions are validated by measurements acquired during 2013-2014 field campaigns (cover/tree inventory, LAI, leaf sampling, optical measures). The results outline the trade-off between accurate and abstract canopy modeling for inversion purposes and may provide perspectives to assess the impact of the California drought with multi-temporal monitoring of canopy biochemistry traits.
|Number of pages||1|
|Publication status||Published - 18 Dec 2015|
|Event||AGU fall meeting 2015 - San Francisco, United States|
Duration: 14 Dec 2015 → 18 Dec 2015
|Conference||AGU fall meeting 2015|
|Period||14/12/15 → 18/12/15|