Comparison of different vegetation indices for the remote assessment of green leaf area index of crops

By Andrés Viña, Anatoly A. Gitelson, Anthony L. Nguy-Robertson, Yi Peng. 2011

730 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Plant Science

View Paper: https://www.semanticscholar.org/paper/078404c4b0428399f041a0581cf1018c3a2cacc6

How researchers see this paper

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

In Ecology

In Global and Planetary Change

In Plant Science

Citations in the corpus

Green area index from an unmanned aerial system over wheat and rapeseed crops (2014, methodology): “They can use vegetation indices that are transformed intoGAI using a statistical relationship calibrated over experimental measurements (Liu, Pattey, & Jégo, 2012; Verrelst et al., 2012; Viña et al., 2011) or over radiative transfer model simulations (Baret & Guyot, 1991; Haboudane et al.”
Prediction of rice crop yield using MODIS EVI−LAI data in the Mekong Delta, Vietnam (2013, background): “A poor EVI−LAI relationship during the leaf constant period was also observed due to loss of EVI sensitivity at full canopy cover (Stroppiana et al. 2012; Wang et al. 2005; Potithepa et al. 2010; Viña et al. 2011).”
Photographic measurement of leaf angles in field crops (2014, methodology): “content, leaf area index) using common remote sensing algorithms (Houborg et al., 2007; Viña et al., 2011).”
Estimating winter wheat (Triticum aestivum) LAI and leaf chlorophyll content from canopy reflectance data by integrating agronomic prior knowledge with the PROSAIL model (2015, methodology): “…http://dx.doi.org/10.1080/01431161.2015.1041176 © 2015 Taylor & Francis The empirical regression approach is based on statistical regression between target variables and spectral reflectance or vegetation indices (VIs) (Darvishzadeh et al. 2008; Gitelson 2004; Ustin et al. 2009; Viña et al. 2011).”
Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data (2015, background): “partial leastsquares regression (PLSR), support vector regression (SVR), random forest regression (RF), neural net regression (NNR), or multivariate adaptive regression splines (MARS)) can be calibrated with in situ LAI measurements to establish a relation to reflectance spectra or spectral indices ”
Green leaf area index estimation in maize and soybean: Combining vegetation indices to achieve maximal sensitivity (2012, methodology): “Viña et al. (2011) evaluated the potential effects of soil background on the remote estimation of gLAI.”
Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance (2014, background): “…showing the pattern like Figure 1(a), this line can be found by locating two or more patches of bare soil having different values of reflectance and finding the best fit line in spectral space (Ray 1994; Viña et al. 2011; Payero, Neale, and Wright 2004; Bannari et al. 1995; Gitelson et al. 2002).”
Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production (2014, neutral): “, subjective) attribute of leaf “greenness” (Boegh et al., 2002; Ciganda, Gitelson, & Schepers, 2008; Curran & Milton, 1983; Viña et al., 2011).”
Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm (2014, neutral): “…CIred edge and MTCI ere shown to be capable of estimating crop total chlorophyll conent, green LAI, gross primary production and fraction of absorbed hotosynthetically active radiation in maize and soybean (Gitelon, 2003b; Viña et al., 2011; Nguy-Robertson et al., 2012; Peng nd Gitelson, 2012).”
Estimating smallholder crops production at village level from Sentinel-2 time series in Mali's cotton belt (2018, background): “These included 2014 and 2015 seasonal time series of VHR DigitalGlobe imagery, used in STARS to, inter alia, (i) exhaustively delineate 31,248 agricultural parcels and generate annual cropland masks; (ii) derive a landscape-level tree mask from soil-specific NDVI thresholding of a 2014 early-season ”