Selection of Optimum Reflectance Ratios for Estimating Leaf Nitrogen and Chlorophyll Concentrations of Field-Grown Cotton

By Duli Zhao, K. Raja Reddy, Vijaya Gopal Kakani, John J. Read, S. Koti. 2005

132 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f8e53fdb3effd4134090a1f3851ec7076bede199

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Citations in the corpus

Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop (2007, background): “One of the goals of farm managers is to detect plant N status accurately and provide N fertilizer in a timely manner to improve yield, increase N use efficiency and minimize N losses to the environment (Zhao et al. 2005).”
Canopy reflectance in cotton for growth assessment and lint yield prediction (2007, background): “Several studies have suggested that leaf reflectance of corn (Blackmer et al., 1994; Zhao et al., 2003), cotton ( Zhao et al., 2005a ), and sorghum (Zhao et al., 2005b) at wavelengths of 550 and 710 nm are most sensitive to N supply.”
Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae) (2011, background): “Concentration of leaf chlorophyll was expressed as lg/sq cm of leaf in order to determine relationship between leafhopper damage severity and chlorophyll concentration (Daughtry et al., 2000; Zhao et al., 2005).”
Hyperspectral remote sensing of plant pigments. (2006, background): “Reflectance spectrum of a typical green leaf over the visible (400–700 nm) and near infrared (here 700–1000 nm) wavebands. from the near-infrared, red edge, and visible regions for Chl estimation for leaves (Zhao et al., 2005) and even at the canopy scale (Coops et al., 2003; Ferri et al., 2004).”
Estimating forage biomass and quality in a mixed sown pasture based on partial least squares regression with waveband selection (2008, background): “2000; Peñuelas & Inoue 2000; Inoue & Peñuelas 2001), and the nutrient status of field crops (Shibayama & Akiyama 1986; Takahashi et al. 2000; Read et al. 2002; Zhao et al. 2005).”
Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass (2008, background): “Many studies have shown that hyperspectral measurements can be used to quantify biophysical characteristics of the vegetation at leaf scale (Gitelson et al., 2003; le Maire et al., 2004; Zhao et al., 2005) or at canopy scale using in situ data, airborne sensors like AVIRIS, CASI and HyMap, or spaceb”