Spectral vegetation indices as nondestructive tools for determining durum wheat yield.

By Nieves Aparicio, Dolors Villegas, J. Casadesús, Jose Luis Araus, Conxita Royo. 2000

445 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5d7a6aef965e7f8ea6bd04536e370a2486d782fa

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Citations in the corpus

Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley (2016, background): “Aparicio et al. (2000) reported increasing coefficients of determination during the vegetation period of a normalised difference vegetation index (R900 − R680)/(R900 + R680) and a simple ratio index (R900/R680) with durum wheat yield in an irrigated field trial.”
Adjusting Midseason Nitrogen Rate Using a Sensor-Based Optimization Algorithm to Increase Use Efficiency in Corn (2008, background): “…crop spectral properties have been used to assess multiple crop parameters such as photosynthetic capacity, productivity and potential yield (Penuelas et al., 1994; Aparicio et al., 2000; Thenkabail et al., 2000; Ma et al., 2001; Raun et al., 2001; Baez-Gonzales et al., 2002; Teal et al., 2006a).”
Breeding for Yield Potential and Stress Adaptation in Cereals (2008, methodology): “In addition, other physiological characteristics of plants, such as canopy architecture, plant water status, nitrogen concentration and even photosynthetic efficiency are captured in the spectra (Araus et al., 2001; Aparicio et al., 2000; Tambussi et al., 2002; Babar et al., 2006).”
Potential Use of Spectral Reflectance Indices as a Selection Tool for Grain Yield in Winter Wheat under Great Plains Conditions (2007, background): “Experiments in multiple locations for predicting grain yield of wheat using SRI have shown that approximately 50 to 65% of yield variability can be explained by NDVI and SR (Tucker et al., 1980; Aparicio et al., 2000; Serrano et al., 2000; Raun et al., 2001).”
The Potential of Using Spectral Reflectance Indices to Estimate Yield in Wheat Grown Under Reduced Irrigation (2006, methodology): “In general, the NIR-based indices gave higher correlations than either NDVI or SR.”
Predicting Grain Yield and Protein Content in Winter Wheat at Different N Supply Levels Using Canopy Reflectance Spectra (2007, methodology): “Also, Aparicio et al. (2000) reported that RVI was more strongly correlated with yield compared with NDVI; however, this study showed the contrary results.”
Genetic analysis of indirect selection for winter wheat grain yield using spectral reflectance indices (2007, background): “Canopy spectral refl ectance has also recently been reported to be useful for predicting grain yield in wheat ( Aparicio et al., 2000; Araus et al., 2001; Royo et al., 2003; Babar et al., 2006a).”
View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data (2009, background): “Successive measurements of vegetation indices during the growing season or between multiple dates have also been proposed as an alternative for yield prediction (Aparicio et al., 2000; Chang et al., 2003; Li et al., 2007; Liu & Kogan, 2002; Rudorff & Batista, 1990; Wiegand et al., 1991).”
Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions (2003, methodology): “The most widely used is the normalized difference vegetation index (NDVI), originally proposed as a means of estimating green biomass (Tucker 1979) and ultimately grain yield (Peñuelas et al. 1997, Aparicio et al. 2000, Ma et al. 2001, Shanahan et al. 2001).”
Plant breeding and drought in C3 cereals: what should we breed for? (2002, methodology): “This makes SRI a useful tool in breeding programmes when screening either for potential yield or for resistance to different stresses (Elliott and Regan, 1993; Bellairs et al., 1996: PenÄuelas et al., 1997; PenÄuelas and Filella, 1998; Aparicio et al., 2000; Araus et al., 2001) (Fig.”