A visible band index for remote sensing leaf chlorophyll content at the canopy scale
By E. Hunt, P. Doraiswamy, J. McMurtrey, C. Daughtry, E. Perry, B. Akhmedov. International Journal of Applied Earth Observation and Geoinformation, 2013
600 citations recorded by the source corpus.
Subfields: Ecology, Global and Planetary Change, Plant Science
View Paper: https://www.semanticscholar.org/paper/15967cbb91aebe58488cb1c922f1e79895a8000e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
- Maria Luisa Buchaillot
- Achim Walter
- Jose Luis Araus
- Andreas Hund
- Frank Liebisch
- Georg Bareth
- Joel Segarra
- Andreas Bolten
In Global and Planetary Change
- Maria Luisa Buchaillot
- Achim Walter
- Jose Luis Araus
- Andreas Hund
- Frank Liebisch
- Joel Segarra
- Shawn C. Kefauver
In Plant Science
- Achim Walter
- Frank Liebisch
- Jose Luis Araus
- Andreas Hund
- Abdul Mounem Mouazen
- Georg Bareth
- G. Slafer
- Matthew P Reynolds
Citations in the corpus
- Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging (2014, background): “Furthermore, vegetation indices in the visible domain have potential in crop monitoring [59,60].”
- Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 2: On-line field measurement (2018, background): “This large absorption (small reflectance) masks the 245 significant band at 670 nm, associated with red colour band at 680 nm and linked to 246chlorophyll a (Hunt et al., 2013).”
- Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications (2020, background): “Another index that uses the visible bands of the spectrum is the Triangular Greenness Index (TGI), which has been shown to estimate correctly chlorophyll concentration at leaf and canopy level [34].”
- Plant phenotyping: from bean weighing to image analysis (2015, background): “Recent studies in wheat and maize correlate NDVI with important crop properties such as biomass [39,40], chlorophyll content [41,42] and nitrogen status [34,43].”
- Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction (2019, neutral): “Further, the Visible Vegetation Index (VVI, Planetary Habitability Laboratory, 2015), Triangular Greenness Index (TGI, Hunt et al., 2013), Normalized Green Red Difference Index (NGRDI), and Green Leaf Index (GLI, Hunt et al., 2013)) were derived from the channels.”
- Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method (2016, background): “Some of these VIs are suitable for both LCC and CCC inversion, whereas others are only suitable for either LCC or CCC (Main et al. 2011; Hunt et al. 2013; Liang et al. 2012).”