Sylvain Jay

Ecology · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Sylvain Jay trusts overall

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

  1. Anatoly A. Gitelson
  2. Cédric Bacour
  3. Jan G. P. W. Clevers
  4. Jean-Baptiste F'eret
  5. W. Verhoef
  6. Wouter A. Dorigo
  7. M. Schlemmer
  8. Aleixandre Verger
  9. Jochem Verrelst
  10. M. Merzlyak

Papers from Sylvain Jay's perspective

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

  1. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture
  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. A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method
  5. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 (International Journal of Applied Earth Observation and Geoinformation)
  6. Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves
  7. Unified Optical-Thermal Four-Stream Radiative Transfer Theory for Homogeneous Vegetation Canopies (IEEE Transactions on Geoscience and Remote Sensing)
  8. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements
  9. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review
  10. Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)

Their papers