Kaiguang Zhao

Ecology · 2008–2019

A researcher in the Science corpus: 9 papers; trusts 8 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

Whom Kaiguang Zhao trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Anatoly A. Gitelson
  2. Jean-Baptiste F'eret
  3. H. T. Nguyen
  4. Gregory P. Asner
  5. Christophe François
  6. J. M. L. Sloughter
  7. Clement Atzberger
  8. Sorin C. Popescu
  9. M. Merzlyak
  10. Adrian E. Raftery

Who trusts Kaiguang Zhao overall

  1. Jari Vauhkonen
  2. Lei Li
  3. David Deery
  4. Zhen Zhen
  5. M. Sumnall
  6. Lauri Korhonen
  7. Matti Maltamo
  8. Akira Kato
  9. Qin Zhang

Papers from Kaiguang Zhao's perspective

Ordered by how strongly the researchers Kaiguang Zhao trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements
  2. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages
  3. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. (Journal of plant physiology)
  4. Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves
  5. Photosynthesis and nitrogen relationships in leaves of C3 plants (Oecologia)
  6. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies
  7. Using Imaging Spectroscopy to Study Ecosystem Processes and Properties
  8. Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors
  9. Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests
  10. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals

Their papers