Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics

By Prasad S. Thenkabail, Ronald B. Smith, E. Pauw. 2000

1,325 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Plant Science

View Paper: https://www.semanticscholar.org/paper/a73e1909e58568a2974415571fdd9ab27c54a11c

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Citations in the corpus

Earth system science related imaging spectroscopy — an assessment (2009, background): “Significant progress was then achieved when, in particular, airborne imaging spectrometers became available on a wider basis (e.g. Gower et al., 1987; Kruse et al.,1990; Rowlands et al.,1994; Green et al.,1998), some of them helping to prepare for spaceborne imaging spectrometer activities (Goetz & ”
Improved forest biomass estimates using ALOS AVNIR-2 texture indices (2011, background): “The reasons for this have beenwidely discussed (Foody et al., 2001; Le Toan et al., 1992; Moran et al., 1994; Sader et al., 1989; Thenkabail et al., 2000), and we have summarized the main points in Section 4.5.”
Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture (2020, background): “With the sensitivity analysis, previous studies have identified spectral bands that are sensitive to different crop properties, for instance, ~515, ~550, ~570, ~670, 700–740, ~800, and ~855 nm for investigating chlorophyll content; ~405, ~515, ~570, ~705, and ~720 nm for evaluating nitrogen status; ”
Principal component analysis applied to feature-oriented band ratios of hyperspectral data: A tool for vegetation studies (2004, methodology): “Although hyperspectral data processing techniques are becoming increasingly popular, traditional methods, such as band ratios and principal component analysis (PCA), are still the focus of recent successful studies of vegetation (Thenkabail et al. 2000).”
Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat (2010, methodology): “In the second approach, n situ reference measurements of the target variable(s) are colected to calibrate statistical models (Darvishzadeh et al., 2008c; urran, 1994; Thenkabail et al., 2002; Broge and Mortensen, 2002; chlerf et al., 2010; Yang et al., 2010).”
Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression (2014, methodology): “However, this method suffers from “over fitting”, because the number of spectral bands exceeds the number of experimental samples (Thenkabail et al., 2000; Nguyen and Lee, 2006).”
Assessing Nitrogen and Potassium Deficiencies in Olive Orchards through Discriminant Analysis of Hyperspectral Data (2007, background): “The normalized difference vegetation index has been widely cited to distinguish crop growth, and N and water stresses (Scharf et al., 2002; Thenkabail et al., 2000).”
Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing (2011, background): “…sensing indicates that the selection of new wavebands has been carried out in a number of cases focusing on how to find out the most sensitive wavebands related to biophysical, physiological or biochemical characteristics (Zarco-Tehada et al., 2001; Thenkabail et al., 2000; Evri et al., 2008).”
Sources of variability in canopy reflectance and the convergent properties of plants. (2011, background): “Among these articles, variation in NDVI has been related to properties such as LAI (e.g. Thenkabail et al., 2000; Houborg & Boegh, 2008), chlorophyll (e.g. Reddy et al., 2001), N stress (e.g. Peñuelas et al., 1994), foliar biomass (e.g. Freitas et al., 2005; Mirik et al., 2005; González-Alonso et…”
Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance (2011, methodology): “…2001); past research has dictated the use of various ratio indices (Aoki et al., 1981; Carter, 1994; Lyon et al., 1998), derivatives of reflectance spectra (Elvidge and Chen, 1995; Thenkabail et al., 2000, 2002), and a linear mixture modelling approach (Maas, 2000; Huang and Zhang, 2008).”
Vegetation classification method with biochemical composition estimated from remote sensing data (2011, methodology): “In this study, the stepwise multivariable regression (SMR) method, which was most commonly used to predict crop variables in plants (Thenkabail et al. 2000, Curran et al. 2001, Haboudane et al. 2002), was applied to develop the biochemical contents estimation model using sample biochemical data and…”