Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression

By H. T. Nguyen, Byun-Woo Lee. 2006

232 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/a593f2ccd71b27681936e874e87f563bdfc32589

How researchers see this paper

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

In Analytical Chemistry

1. Lin-zhang Yang
2. Chan Seok Ryu
3. Lei Li
4. Denis Ribeiro do Valle
5. Xin-ping Chen
6. Yuncai Hu
7. Pol R. Coppin
8. Bani K. Mallick

In Ecology

1. Arnon Karnieli
2. Bastian Siegmann
3. Thomas Jarmer
4. Ittai Herrmann
5. David J. Bonfil
6. Frédéric Baret
7. Lin-zhang Yang
8. Chan Seok Ryu

In Plant Science

1. Bastian Siegmann
2. Thomas Jarmer
3. Lin-zhang Yang
4. Yuncai Hu
5. L. Xue
6. Urs Schmidhalter
7. Hailin Zhang
8. Ganghua Li

Citations in the corpus

• A comparison of three methods for estimating leaf area index of paddy rice from optimal hyperspectral bands (2011, unknown): “Partial least squares has been regarded as an effective technique to retrieve vegetation properties from reflectance data (Hansena and Schjoerring 2003; Nguyen and Lee 2006; Cho et al. 2007).”
• Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data (2015, methodology): “PLSR was originally developed for chemical applications, but it has also been used in remote sensing for many years to predict biophysical and biochemical parameters (e.g. Darvishzadeh et al. 2008; Hansen and Schjoerring 2003; Herrmann et al. 2011; Jarmer 2013; Nguyen and Lee 2006; Pu 2012).”
• Topdressing nitrogen recommendation for early rice with an active sensor in south China (2014, methodology): “…have been employed successfully to assess crop N status in terms of plant/leaf N concentration, estimating plant N uptake (Hansen and Schjoerring 2003; Xue et al. 2004; Nguyen and Lee 2006) and guiding N fertilization (Raun et al. 2002; Wood et al. 2003; Welsh et al. 2003; Raun et al. 2005).”
• Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: Non-parametric statistical approaches and physiological implications (2007, methodology): “This problem was solved by using the PLS-LDA technique, which is suited to deal with a larger number of predictors than observations and with multicollinearity (Nguyen and Byun-Woo, 2006).”
• Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection (2013, methodology): “As an alternative to SMR, PLS becomes prevalent because it alleviates the problem of high dimensionality by seeking a parsimonious number of factors through a linear projection of original bands (Nguyen & Lee, 2006).”
• LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands (2011, methodology): “Partial Least Squares (PLS) analysis is a practical predictive tool for spectral reflective data (Hansen & Schjoerring, 2003; Nguyen & Lee, 2006).”
• 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).”