Estimating foliage nitrogen concentration from HYMAP data using continuum, removal analysis

By Hu Zhi, Brian J. Turnera, Stephen J. Durya, Ian R. Wallisb, William J. Foleyb. 2004

370 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/63a344f99f22b2b74a87ca01ecbfff6e146e009d

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In Ecological Modeling

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In Environmental Engineering

Citations in the corpus

Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment (2015, background): “…studies, which demonstrate the superiority of indices that are targeted for specific variables or species over non-specific formulations (e.g., Cho et al., 2007; Fu et al., 2014; Hansen and Schjoerring, 2003; Huang et al., 2004; Li et al., 2014., 2008; Ramoelo et al., 2013; Yi et al., 2014).”
Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression (2013, methodology): “Usually, spectra are subdivided before applying CR on specific absorption features, but the method has also shown great efficiency when applied on the full spectrum (Huang et al. 2004).”
Vegetation classification method with biochemical composition estimated from remote sensing data (2011, methodology): “At present, biochemical composition estimation from remote-sensing data usually includes three main methods: sensitive spectral bands (Huang et al. 2004), vegetation index (Wu et al. 2008) and model inversion (Kempeneers et al. 2008).”
Mapping two Eucalyptus subgenera using multiple endmember spectral mixture analysis and continuum-removed imaging spectrometry data (2011, methodology): “CR is most often used in spectral data analysis to identify the geological composition of materials and to quantify vegetation biochemistry (Huang et al., 2004; Kruse & Lefkoff, 1993; Mutanga & Skidmore, 2004; van der Meer, 2004).”
Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation (2007, methodology): “Stepwise regression has been widely used in constructing accurate predictive relationships for leaf biochemistry based on reflectance spectra (e.g. Curran et al. 2001), but several limitations to the technique have been recognized (Huang et al. 2004).”
A comparison of three methods for estimating leaf area index of paddy rice from optimal hyperspectral bands (2011, neutral): “Multiple linear regression (MLR), especially stepwise multiple linear regression (MSLR), is the most widely used approach for estimating plant biophysical and biochemical parameters with reflectance measurements (Grossman et al. 1996; Curran et al. 2001; Huang et al. 2004).”
Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations (2011, methodology): “…et al. 2005; Mutanga and Kumar 2007; Numata et al. 2009; Skidmore et al. 2010), forests (Martin and Aber 1997; Schlerf et al. 2010) and agricultural areas (LaCapra et al. 1996; Thenkabail et al. 2000; Hansen and Schjoerring 2003; Huang et al. 2004; Zarco-Tejada et al. 2004; Wang et al. 2009).”
Spectral difference analysis and airborne imaging classification for citrus greening infected trees (2012, neutral): “(Huang et al., 2004; Wen et al., 2008).”
Retrieval of leaf water content spanning the visible to thermal infrared spectra (2014, methodology): “This technique has been successfully used to 164 estimate several leaf bio-chemicals (Huang et al. 2004; Asner and Martin 2008; Ramoelo et al. 165 2011).”
A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling (2007, methodology): “Several studies report that PLS performs better in comparison to traditional regression techniques (Hansen and Schjoerring, 2003; Huang et al., 2004).”
Hyperspectral remote sensing of plant pigments. (2006, methodology): “Furthermore, continuum removal analysis was found to be superior to the use of standard derivative analysis for quantifying tree canopy biochemical properties from airborne imaging spectrometry data, though total N was examined rather than pigment concentrations specifically (Huang et al., 2004).”