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

doi:10.1016/j.rse.2018.05.026

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:

^[1];

^[2]; Camacho, Fernando ^[3];

^[2]; Jensen, Jennifer L. R. ^[4];

^[5]; Liu, Zhili ^[6]; Piayda, Arndt ^[7];

^[8]; Roupsard, Olivier ^[9]; Serbin, Shawn P. ^[10]; Solberg, Svein ^[5]; Sonnentag, Oliver ^[11]; Thimonier, Anne ^[12];

^[13]

Univ. of Tartu (Estonia)
Trier Univ. (Germany)
Parc Científic Universitat de València (Spain)
Texas State Univ., San Marcos, TX (United States)
Norwegian Inst. of Bioeconomy Research, Akershus (Norway)
Northeast Forestry Univ., Harbin (China)
Thunen Inst. of Climate-Smart Agriculture, Braunschweig (Germany)
Beijing Normal Univ. (China)
SupAgro-CIRAD-INRA-IRD, Montpellier (France)
Brookhaven National Lab. (BNL), Upton, NY (United States)
Univ. of Montreal, Quebec (Canada)
Swiss Federal Inst. for Forest, Snow and Landscape Research, Birmensdorf (Switzerland)
Inst. of Surveying, Remote Sensing and Land Information, Vienna (Austria)

Publication Date:: 2018-05-30

Research Org.:: Brookhaven National Lab. (BNL), Upton, NY (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

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.

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                    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:https://doi.org/10.1016/j.rse.2018.05.026

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                    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:https://doi.org/10.1016/j.rse.2018.05.026.  https://www.osti.gov/servlets/purl/1466616.

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@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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DOI: https://doi.org/10.1016/j.rse.2018.05.026

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Cited by: 1 work

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Figures / Tables:

Table 1: Characteristics and results for 75 sites with raw PCA measurements. PFT is plant functional type. Lat is latitude (in degrees). Lon is longitude (in degrees). PCA is Plant 9 Canopy Analyzer. LAI_PCA is LAI estimate from PCA data. $p$ is the photon recollison probability. $γ$_E is the needle-to-shootmore »

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Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs
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Title: Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory

Abstract

Citation Formats

Figures / Tables:

Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs journal, October 2019

Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs
journal, October 2019