Sources of variability in canopy reflectance and the convergent properties of plants.

By Scott V. Ollinger. New Phytologist, 2011

746 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/732ef5eca875df5476f20d185cdf432630da6ef8

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Citations in the corpus

Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review (2015, background): “Interestingly, also bands in the NIR (e.g., 950 nm) and SWIR (e.g., 1430, 1730 nm) which are located outside the chlorophyll spectral absorption region were identified to play an important role.”
Differentiating plant functional types using reflectance: which traits make the difference? (2018, background): “growth forms such as shrubs and trees the crown shape and foliage clumping, which describes the aggregation of foliage within a canopy, might be important additional structural canopy properties relating to plant functioning (Niinemets 2010; Ollinger 2011).”
Seasonal patterns of foliar reflectance in relation to photosynthetic capacity and color index in two co-occurring tree species, Quercus rubra and Betula papyrifera (2012, neutral): “Although the basic features of foliar reflectance are widely understood, non-linear and species-specific relations between pigment content and leaf spectral properties hinder upscaling exercises required for large-scale remote sensing applications (Ollinger, 2010).”
Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods (2018, background): “The advantage of these statistical models is that not only variablespecific absorption features can be used for information extraction, but also secondary relationships with variables related to other absorption features that co-vary with the variable of interest can be exploited (Ollinger 2011; Ver”
New estimates of leaf angle distribution from terrestrial LiDAR: Comparison with measured and modelled estimates from nine broadleaf tree species (2019, background): “Yet, despite the strong sensitivity of many of these models to variability in LAD, the difficulty in measuring LAD often causes it to be one of the most poorly constrained parameters in structural models of canopy radiative transfer (see e.g. Ollinger, 2011).”
Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests. (2016, neutral): “, Ollinger, S.V., Martin, M.E., Hollinger, D.Y., Lepine, L.C., Day, M.C., Bartlett, 708 M.K., Richardson, A.D., & Norby, R.J. (2012). Variation in foliar nitrogen and albedo in 709 response to nitrogen fertilization and elevated CO2.”
Marker-Assisted Plant Breeding: Principles and Practices (2015, methodology): “tosynthetic activity, and water content can be obtained from multispectral imaging (Ollinger 2011; Chaerle and Van Der Straeten 2001).”
Review of optical-based remote sensing for plant trait mapping (2013, background): “leaf aggregation, leaf angle distribution) present a substantial challenge for RTMs parameterization and interpretation from RS data (Ollinger, 2011).”
A Review of Imaging Techniques for Plant Phenotyping (2014, background): “These vegetation indices are usually related to different plant characteristics such as the photosynthetic active biomass, pigment content and water status [92].”
Mapping functional diversity using individual tree-based morphological and physiological traits in a subtropical forest (2021, background): “…signals of nonleaf material in gaps, the atmosphere and changing illumination and viewing geometries (Asner and Martin, 2008; Cavender-Bares et al., Z. Zheng et al. Remote Sensing of Environment 252 (2021) 112170 Z. Zheng et al. Remote Sensing of Environment 252 (2021) 112170 2017; Ollinger, 2011).”
Species distribution modelling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions (2013, background): “…detailed quantification of vegetation structure and composition available from ground-sampled plots or classified imagery products, raw reflectances are not easily interpretable by ecologists which may limit their ecological relevance in uncovering species–vegetation associations (Ollinger, 2011).”
Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression (2014, methodology): “However, many bands that are sensitive to canopy structure rather than plant photosynthetic pigment have been positively related to the canopy N content (Ollinger, 2011).”