Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features

By D. Sims, John A. Gamon. 2003

578 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Plant Science

View Paper: https://www.semanticscholar.org/paper/e062cf5e0c177fd7106c1588b64cfba1bc51d833

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

In Global and Planetary Change

In Plant Science

Citations in the corpus

Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes (2012, background): “Moreover, a low canopy reflectance between 1150 and 1260 nm has been reported to be associated with reduced stomatal conductance and high relative water content in a wide range of species (Sims and Gamon, 2003).”
Object-based crop identification using multiple vegetation indices, textural features and crop phenology (2011, background): “This efficiency can be attributed to the fact that this crop growth in flooded fields and its spectral properties are very distinguishable due to effect of water in the NIR and SWIR spectral regions (Sims & Gamon, 2003).”
Foliar spectral properties following leaf clipping and implications for handling techniques (2006, unknown): “Sims and Gamon (2003) conducted a thorough investigation of water content for thick and thin tissues encompassing a variety of plant species.”
Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection (2013, unknown): “The discrepancies for LMA and EWT were attributed possibly to our consideration of the whole spectrum whereas previous studies focused on some selected ranges of spectra (Sims & Gamon, 2003).”
Identification of grapevine varieties using leaf spectroscopy and partial least squares (2013, background): “7%, respectively, which were substantially higher than the 81% reported by Soldavini et al. (2009) who developed software for ampelographic measurements.”
Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing (2019, background): “The plant water status spectral indices utilize simple ratios between the reflectance of a wavelength located within an range of the electromagnetic spectrum strongly absorption by water, described as water absorption bands, and another wavelength located outside the water absorption band typically ”
Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies (2009, background): “Many empirical studies have evaluated water content algorithms based on analyses of leaf spectra, where leaves were measured in an integrating sphere or as a stack of foliage (Kokaly & Clark,1999; Sims & Gamon, 2003; Stimson et al., 2005; Yu et al., 2000).”
Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment (2015, methodology): “…Bei et al., 2011; Rodriguez-Perez et al., 2007), this band was not incorporated in the WABI models due to its susceptibility to water vapor absorption (Eitel et al., 2006; Sims and Gamon, 2003) and since the 1500 nm region was generally better correlated to changes in all physiological variables.”
Fusion of Feature Selection Methods and Regression Algorithms for Predicting the Canopy Water Content of Rice Based on Hyperspectral Data (2021, background): “Reflectance spectra present many possible water indices because there are several water absorption features in the near- and far-infrared region [25].”