F. J. García-Haro

Ecology · 2002–2021

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

Subfields

Whom F. J. García-Haro trusts overall

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

  1. Allan Aasbjerg Nielsen
  2. Lucien Wald
  3. Kevin P. Murphy
  4. Feng Gao
  5. José M. Bioucas-Dias
  6. David P. Roy
  7. Thomas Hilker

Who trusts F. J. García-Haro overall

  1. Xiaolin Zhu
  2. Luis Gómez-Chova
  3. Caroline M. Gevaert
  4. Mariana Belgiu

Papers from F. J. García-Haro's perspective

Ordered by how strongly the researchers F. J. García-Haro trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data
  2. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance (IEEE Transactions on Geoscience and Remote Sensing)
  3. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS
  4. Data Fusion. Definitions and Architectures - Fusion of Images of Different Spatial Resolutions
  5. The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data (IEEE Transactions on Image Processing)
  6. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
  7. Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection
  8. Machine learning - a probabilistic perspective
  9. The impact of gridding artifacts on the local spatial properties of MODIS data : Implications for validation, compositing, and band-to-band registration across resolutions
  10. Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data (IEEE Transactions on Geoscience and Remote Sensing)

Their papers