Zhenhai Li

Ecology · 2013–2021

A researcher in the Science corpus: 14 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Zhenhai Li trusts overall

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

  1. Anatoly A. Gitelson
  2. Frédéric Baret
  3. Clement Atzberger
  4. Michele Meroni
  5. Giorgos Mountrakis
  6. Andrés Viña
  7. Kalyanmoy Deb
  8. Stephanie Delalieux
  9. Cédric Bacour
  10. Xiuliang Jin

Who trusts Zhenhai Li overall

  1. Xiuliang Jin
  2. Pablo J. Zarco-Tejada

Papers from Zhenhai Li's perspective

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

  1. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages
  2. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture
  3. Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. (Journal of Experimental Botany)
  4. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops
  5. Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem
  6. Diagnosis tool for plant and crop N status in vegetative stage Theory and practices for crop N management
  7. Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle (IEEE Transactions on Geoscience and Remote Sensing)
  8. Seasonality extraction by function fitting to time-series of satellite sensor data (IEEE Transactions on Geoscience and Remote Sensing)
  9. Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data : Principles and validation
  10. ImageNet classification with deep convolutional neural networks

Their papers