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

Abstract

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.

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3]; ORCiD logo [2];  [4]; ORCiD logo [5];  [6];  [7]; ORCiD logo [8];  [9];  [10];  [5];  [11];  [12]; ORCiD logo [13]
  1. Univ. of Tartu (Estonia)
  2. Trier Univ. (Germany)
  3. Parc Científic Universitat de València (Spain)
  4. Texas State Univ., San Marcos, TX (United States)
  5. Norwegian Inst. of Bioeconomy Research, Akershus (Norway)
  6. Northeast Forestry Univ., Harbin (China)
  7. Thunen Inst. of Climate-Smart Agriculture, Braunschweig (Germany)
  8. Beijing Normal Univ. (China)
  9. SupAgro-CIRAD-INRA-IRD, Montpellier (France)
  10. Brookhaven National Lab. (BNL), Upton, NY (United States)
  11. Univ. of Montreal, Quebec (Canada)
  12. Swiss Federal Inst. for Forest, Snow and Landscape Research, Birmensdorf (Switzerland)
  13. Inst. of Surveying, Remote Sensing and Land Information, Vienna (Austria)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1466616
Alternate Identifier(s):
OSTI ID: 1582917
Report Number(s):
BNL-207966-2018-JAAM
Journal ID: ISSN 0034-4257
Grant/Contract Number:  
SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
Remote Sensing of Environment
Additional Journal Information:
Journal Volume: 215; Journal Issue: C; Journal ID: ISSN 0034-4257
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

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, and Vuolo, Francesco. Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory. United States: N. p., 2018. Web. doi:10.1016/j.rse.2018.05.026.
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. Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory. United States. doi:10.1016/j.rse.2018.05.026.
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, and Vuolo, Francesco. Wed . "Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory". United States. doi:10.1016/j.rse.2018.05.026. https://www.osti.gov/servlets/purl/1466616.
@article{osti_1466616,
title = {Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory},
author = {Pisek, Jan and Buddenbaum, Henning and Camacho, Fernando and Hill, Joachim and Jensen, Jennifer L. R. and Lange, Holger and Liu, Zhili and Piayda, Arndt and Qu, Yonghua and Roupsard, Olivier and Serbin, Shawn P. and Solberg, Svein and Sonnentag, Oliver and Thimonier, Anne and Vuolo, Francesco},
abstractNote = {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.},
doi = {10.1016/j.rse.2018.05.026},
journal = {Remote Sensing of Environment},
number = C,
volume = 215,
place = {United States},
year = {2018},
month = {5}
}

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