Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory

Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sen...

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Bibliographic Details
Published in:Remote sensing of environment 2018-09, Vol.215 (C), p.1-6
Main Authors: Pisek, Jan, Buddenbaum, Henning, Camacho, Fernando, Hill, Joachim, Jensen, Jennifer L.R., Lange, Holger, Liu, Zhili, Piayda, Arndt, Qu, Yonghua, Roupsard, Olivier, Serbin, Shawn P., Solberg, Svein, Sonnentag, Oliver, Thimonier, Anne, Vuolo, Francesco
Format: Article
Language:English
Subjects:
Canopies
Data processing
Earth
Environmental protection
ENVIRONMENTAL SCIENCES
Foliage
Foliage clumping index
Leaf area
Leaf area index
Leaves
Life Sciences
MODIS
Multi-angle remote sensing
Photon recollision probability
Photosynthesis
Probability theory
Remote sensing
Vegetal Biology
Vegetation
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Summary:Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability (‘p-theory’), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types. The p-theory describes the probability (p-value) that a photon, having intercepted an element in the canopy, will recollide with another canopy element rather than escape the canopy. We show that empirically-based CI maps can be integrated with the MODIS LAI product. Our results indicate that it is feasible to derive approximate p-values for any location solely from Earth Observation data. This approximation is relevant for future applications of the photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data. •Synergy between a MODIS-based CI and a MODIS LAI product is examined.•Synergy assessed with spectral invariants theory, raw LAI-2000/2200 data.•It might be possible to obtain p-values for any location solely from EO data.
ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2018.05.026