Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture

By Driss Haboudanea, John R. Millera, Elizabeth Patteyc, P. J. Zarco-Tejadad, Ian B. Strachane. 2004

2,556 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/1d7a23cc71e60872d308cac5276f0b139deebffe

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Citations in the corpus

Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging (2010, background): “More detailed information on many of these vegetative indices (VI), and comparisons under different circumstances can be found elsewhere (Elvidge and Chen 1995; Haboudane et al., 2004; Payero et al., 2004; Tilling et al., 2006).”
LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands (2011, background): “Nevertheless, since LAI is nonlinearly related to NDVI (Clevers, 1989; Gamon et al., 1995; Haboudane et al., 2004; Myneni et al., 1997) the logarithmic relations are also presented in Fig.”
Using in‐situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status (2007, background): “Haboudane et al. (2004) found the second Modified Triangular Vegetation Index (MTVI2) to be a better predictor of leaf area index (LAI) than OSAVI, due to lower sensitivity to variation in plant chlorophyll and resistance to the saturation phenomena at higher LAI values.”
Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review (2015, methodology): “…capacity has been partly remediated by optimizing VIs and their associated regression models through the use of large datasets generated by leaf and canopy radiative transfer models (RTMs) (Ceccato et al., 2002; Zarco-Tejada et al., 2001, 2005; Haboudane et al., 2004; Le Maire et al., 2004, 2008).”
HSCNN+: Advanced CNN-Based Hyperspectral Recovery from RGB Images (2018, background): “The spectral characteristic has been proven useful in many fields, ranging from remote sensing to medical diagnosis and agriculture [16, 17, 18].”
Remote Estimation of Crop Chlorophyll Content Using Spectral Indices Derived From Hyperspectral Data (2008, methodology): “For this purpose, two main approaches have been presented: scaling-up strategies based on model inversion techniques [32], and the study of prediction power and stability of spectral indices on the basis of forward model simulations with radiative transfer models [9], [27], [31].”
LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements (2008, neutral): “Many of the previous studies, however, are either based on simulated data (Atzberger, 2004; Broge and Leblanc, 2001; Chaurasia and Dadhwal, 2004; Haboudane et al., 2004; Udelhoven et al., 2000), non-grassed plants such as agricultural crops (Atzberger, 1995, 1997; Baret et al.”
Estimation of biomass in wheat using random forest regression algorithm and remote sensing data (2016, background): “5 × (RR – RG)] / h ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ðRNIR þ 1Þ(2)−6 RNIR þ 5 ffiffiffiffiffiffi RR p q”