LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands

By Ittai Herrmann, A. Pimstein, Arnon Karnieli, Yafit Cohen, Victor Alchanatis, David J. Bonfil. 2011

269 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/839f55d53c29ab3919b872b59b869f76ea242883

How researchers see this paper

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

In Atmospheric Science

In Ecology

In Plant Science

Citations in the corpus

Ground-level hyperspectral imagery for detecting weeds in wheat fields (2013, background): “The red-edge region is the slope connecting the low red and high NIR reflectance values in the spectrum of vegetation and is an important indicator for spectral separation of different plant species (Herrmann et al. 2011; Shapira et al. 2013).”
Field spectroscopy for weed detection in wheat and chickpea fields (2013, methodology): “…the slope connecting the red (R) and near infrared (NIR) regions in the reflectance spectra of plants, and is an important element in spectral separation of different plant species, including weeds and crops (Vrindts, De Baerdemaeker, and Ramon 2002; Smith and Blackshaw 2003; Herrmann et al. 2011).”
Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment (2015, background): “To date, despite successful implementations in studies of various species and stress factors (e.g., Feret et al., 2011; Hansen and Schjoerring, 2003; Herrmann et al., 2011; Li et al., 2014; Nguyen and Lee, 2006; Ramoelo et al., 2013; Yi et al., 2014), only a couple of water deficit-related grapevine”
Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data (2015, methodology): “It is defined as the total one-sided area of leaf tissue per unit ground area and provides information about the photosynthetic performance of plants (Chen and Black 1992; Duchemin et al. 2006; Herrmann et al. 2011; Watson 1947).”
A visible band index for remote sensing leaf chlorophyll content at the canopy scale (2013, background): “Red-edge bands are deployed on many satellite sensors (Eitel et al., 2007; Herrmann et al., 2011; Ramoelo et al., 2012) and increase sensitivity to chlorophyll content (Gitelson et al.”
Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression (2014, methodology): “…plant biochemical concentrations, partial least square regression (PLSR) overcomes the problems of collinearity and “over-fitting” compared to step linear regression analysis if optimally choosing a suitable number of principal components and deleting the noise bands (Herrmann et al., 2011).”
Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image (2014, background): “…Hank, Vuolo, Mauser, & D'Urso, 2012), or convolving the bands of hyperspectral sensors such as CASI (Richter, Atzberger, Vuolo, & D'Urso, 2011a; Richter, Atzberger, Vuolo, Weihs, & D'Urso, 2009) or field spectrometers (Clevers & Gitelson, 2013; Herrmann et al., 2011) to the Sentinel-2 bands.”