Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements

By Yuanyuan Fu, Guijun Yang, Jihua Wang, Xiao-yu Song, Haikuan Feng. 2014

182 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/cffef57761766a51fcd6a4139c3d1c3926a77565

How researchers see this paper

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

In Ecology

1. Bodo Mistele
2. Urs Schmidhalter
3. Arnon Karnieli
4. Maxim Shoshany
5. Shimon Rachmilevitch
6. Tal Rapaport
7. Uri Hochberg

In Environmental Engineering

1. Bodo Mistele
2. Urs Schmidhalter
3. Arnon Karnieli

Citations in the corpus

• Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment (2015, background): “…studies, which demonstrate the superiority of indices that are targeted for specific variables or species over non-specific formulations (e.g., Cho et al., 2007; Fu et al., 2014; Hansen and Schjoerring, 2003; Huang et al., 2004; Li et al., 2014., 2008; Ramoelo et al., 2013; Yi et al., 2014).”
• Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley (2016, methodology): “Fu et al. (2013) combined optimal binary, normalized indices retrieved in a spectral range of 350 nm to 2500 nm with band depth ratios in partial least square regression models for assessing winter wheat biomass.”